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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2c8d4d6e",
+   "metadata": {},
+   "source": [
+    "# Charting the Path to Sustainability: Integrating Land Sector Data with SDGs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2707ecb5",
+   "metadata": {},
+   "source": [
+    "### TASK 2- Attribute SDG Mapping Prototype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f02f4ded",
+   "metadata": {},
+   "source": [
+    "#### INTRODUCTION: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "692a10a9",
+   "metadata": {},
+   "source": [
+    "Sustainable Development Goals (SDG) are a bold,universal agreement to end poverty and all its dimensions and craft an equal, just and secure world – for people, planet and prosperity.The SDGs have been developed through an unprecedented consultative process that brought national governments and millions of citizens from the globe together to negotiate and adopt this ambitious agenda. There are 17 Sustainable Development Goals, which are listed below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "665f7b4f",
+   "metadata": {},
+   "source": [
+    "No 1. End poverty in all it's forms everywhere.\n",
+    "\n",
+    "No 2. End hunger, achieve food security and improved nutrition and promote sustainable agriculture.\n",
+    "\n",
+    "No 3. Ensure healthy lives and promote well-being for us all at all age.\n",
+    "\n",
+    "No 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.\n",
+    "\n",
+    "No 5. Acheive gender equality and empower all women and girls.\n",
+    "\n",
+    "No 6. Ensure availability and sustainable management of water and sanitation for all.\n",
+    "\n",
+    "No 7. Ensure access to affordable, reliable, sustainable and mordern energy for all.\n",
+    "\n",
+    "No 8. Promote sustained inclusive and sustainable economic growth, full and productive employment and decent work for all.\n",
+    "\n",
+    "No 9. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.\n",
+    "\n",
+    "No 10. Reduce inequality within and among countries.\n",
+    "\n",
+    "No 11. Make cities and human settle inclusive,safe resilient and sustainable.\n",
+    "\n",
+    "No 12. Ensure sustainable consumption and production patterns.\n",
+    "\n",
+    "No 13. Take urgent action to combat climate and its impacts.\n",
+    "\n",
+    "No 14. Conserve and sustainably use the oceans, seas and marine resources for sustainable development.\n",
+    "\n",
+    "No 15. Protect, restore and promote sustainable use of terrestial ecosystems, sustainably manage forests, combat dessertification, halt and reverse land degradationa and halt biodiversity loss.\n",
+    "\n",
+    "No 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels.\n",
+    "\n",
+    "No 17. Strengthen the means of implementationand recitalize the global partnership for sustainable development."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03f92db9",
+   "metadata": {},
+   "source": [
+    "#### SDGs for this project: \n",
+    "\n",
+    "No 11. Make cities and human settle inclusive,safe, resilient and sustainable (Sustainable Cities and Communities).\n",
+    "\n",
+    "No 13: Take urgent action to combat climate and its impacts (Climate Action)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f683249",
+   "metadata": {},
+   "source": [
+    "### ADMINISTRATIVE DATASET"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8fd9b16",
+   "metadata": {},
+   "source": [
+    "#### ROAD DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "26bef72d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing libraries \n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "%matplotlib inline\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0f141ece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load the json file\n",
+    "with open (\"C:/Users/adere/Downloads/Global Roads Open Access Data Set.json.geojson\") as file:\n",
+    "    road = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e67eb6de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create GeoDataFrame from the road data\n",
+    "Global_road = gpd.GeoDataFrame.from_features(road['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fcfb9b3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ONME</th>\n",
+       "      <th>FCLASS</th>\n",
+       "      <th>SRFTPE</th>\n",
+       "      <th>ISSEASONAL</th>\n",
+       "      <th>CURNTPRAC</th>\n",
+       "      <th>GDWTHRPRAC</th>\n",
+       "      <th>SUM_LENGTH_KM</th>\n",
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+       "    <tr>\n",
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+       "  </tbody>\n",
+       "</table>\n",
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+      "text/plain": [
+       "                                            geometry  OBJECTID  ONME  FCLASS  \\\n",
+       "0  LINESTRING (-66.82451 17.98029, -66.82455 17.9...         1  None       0   \n",
+       "1  LINESTRING (-66.62012 17.98131, -66.62126 17.9...         2  None       0   \n",
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+       "4  LINESTRING (-66.63308 18.01087, -66.63332 18.0...         5  None       0   \n",
+       "\n",
+       "   SRFTPE  ISSEASONAL  CURNTPRAC  GDWTHRPRAC  SUM_LENGTH_KM  \n",
+       "0     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "1     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "2     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "3     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "4     0.0         0.0        NaN         NaN    16686.65607  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "478b64b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Define a mapping function for road data\n",
+    "def map_road_to_sdg(x):\n",
+    "    if x['FCLASS'] != '':\n",
+    "        return('SDG 11 - Sustainable Cities and Communities')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b981b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply the mapping function to the dataset\n",
+    "Global_road['Sustainable Cities and Communities'] = Global_road.apply(map_road_to_sdg, axis=1)\n",
+    "\n",
+    "# Create a bar chart to visualize the mapped data\n",
+    "sdg_counts = Global_road['Sustainable Cities and Communities'].value_counts()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sdg_counts.plot(kind='bar', color=['blue', 'green'])\n",
+    "plt.title('GLOBAL ROAD MAPPING TO SDG')\n",
+    "plt.xlabel('SDG Categories')\n",
+    "plt.ylabel('Number of Features')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a4fb6037",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ONME</th>\n",
+       "      <th>FCLASS</th>\n",
+       "      <th>SRFTPE</th>\n",
+       "      <th>ISSEASONAL</th>\n",
+       "      <th>CURNTPRAC</th>\n",
+       "      <th>GDWTHRPRAC</th>\n",
+       "      <th>SUM_LENGTH_KM</th>\n",
+       "      <th>Sustainable Cities and Communities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LINESTRING (-66.82451 17.98029, -66.82455 17.9...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "      <td>SDG 11 - Sustainable Cities and Communities</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LINESTRING (-66.62012 17.98131, -66.62126 17.9...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "      <td>SDG 11 - Sustainable Cities and Communities</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  OBJECTID  ONME  FCLASS  \\\n",
+       "0  LINESTRING (-66.82451 17.98029, -66.82455 17.9...         1  None       0   \n",
+       "1  LINESTRING (-66.62012 17.98131, -66.62126 17.9...         2  None       0   \n",
+       "\n",
+       "   SRFTPE  ISSEASONAL  CURNTPRAC  GDWTHRPRAC  SUM_LENGTH_KM  \\\n",
+       "0     0.0         0.0        NaN         NaN    16686.65607   \n",
+       "1     0.0         0.0        NaN         NaN    16686.65607   \n",
+       "\n",
+       "            Sustainable Cities and Communities  \n",
+       "0  SDG 11 - Sustainable Cities and Communities  \n",
+       "1  SDG 11 - Sustainable Cities and Communities  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test the prototype\n",
+    "Global_road.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "95bf2796",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the file\n",
+    "file_path = r\"C:/Users/adere/Downloads/WDPA_WDOECM_wdpa_gdb_polygons/WDPA_WDOECM_wdpa_gdb_polygons.shp\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "32bde39a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Read geospatial data from the specified file path using GeoPandas\n",
+    "protected_area = gpd.read_file(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "09c22095",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WDPAID</th>\n",
+       "      <th>WDPA_PID</th>\n",
+       "      <th>PA_DEF</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>ORIG_NAME</th>\n",
+       "      <th>DESIG</th>\n",
+       "      <th>DESIG_ENG</th>\n",
+       "      <th>DESIG_TYPE</th>\n",
+       "      <th>IUCN_CAT</th>\n",
+       "      <th>INT_CRIT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MANG_AUTH</th>\n",
+       "      <th>MANG_PLAN</th>\n",
+       "      <th>VERIF</th>\n",
+       "      <th>METADATAID</th>\n",
+       "      <th>SUB_LOC</th>\n",
+       "      <th>PARENT_ISO</th>\n",
+       "      <th>ISO3</th>\n",
+       "      <th>SUPP_INFO</th>\n",
+       "      <th>CONS_OBJ</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10715.0</td>\n",
+       "      <td>10715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-KAM</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>209777.0</td>\n",
+       "      <td>209777_E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sarali Land between Rivers / Great Volzhsko-Ka...</td>\n",
+       "      <td>Great Volzhsko-Kamsky</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-TA</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>POLYGON ((49.30487 55.36806, 49.30433 55.37184...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     WDPAID  WDPA_PID PA_DEF  \\\n",
+       "0   10715.0     10715      1   \n",
+       "1  209777.0  209777_E      1   \n",
+       "\n",
+       "                                                NAME              ORIG_NAME  \\\n",
+       "0                                         Kronotskiy             Kronotskiy   \n",
+       "1  Sarali Land between Rivers / Great Volzhsko-Ka...  Great Volzhsko-Kamsky   \n",
+       "\n",
+       "                          DESIG                     DESIG_ENG     DESIG_TYPE  \\\n",
+       "0  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "1  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "\n",
+       "         IUCN_CAT        INT_CRIT  ...     MANG_AUTH     MANG_PLAN  \\\n",
+       "0  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "1  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "\n",
+       "            VERIF  METADATAID  SUB_LOC PARENT_ISO  ISO3       SUPP_INFO  \\\n",
+       "0  State Verified         840   RU-KAM        RUS   RUS  Not Applicable   \n",
+       "1  State Verified         840    RU-TA        RUS   RUS  Not Applicable   \n",
+       "\n",
+       "         CONS_OBJ                                           geometry  \n",
+       "0  Not Applicable  MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...  \n",
+       "1  Not Applicable  POLYGON ((49.30487 55.36806, 49.30433 55.37184...  \n",
+       "\n",
+       "[2 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "36dd7d78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['WDPAID', 'WDPA_PID', 'PA_DEF', 'NAME', 'ORIG_NAME', 'DESIG',\n",
+       "       'DESIG_ENG', 'DESIG_TYPE', 'IUCN_CAT', 'INT_CRIT', 'MARINE',\n",
+       "       'REP_M_AREA', 'GIS_M_AREA', 'REP_AREA', 'GIS_AREA', 'NO_TAKE',\n",
+       "       'NO_TK_AREA', 'STATUS', 'STATUS_YR', 'GOV_TYPE', 'OWN_TYPE',\n",
+       "       'MANG_AUTH', 'MANG_PLAN', 'VERIF', 'METADATAID', 'SUB_LOC',\n",
+       "       'PARENT_ISO', 'ISO3', 'SUPP_INFO', 'CONS_OBJ', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for the column names\n",
+    "protected_area.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "3c8b2aa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "STATUS\n",
+       "Designated      237644\n",
+       "Proposed          1152\n",
+       "Established       1098\n",
+       "Inscribed          224\n",
+       "Not Reported        35\n",
+       "Adopted             12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area['STATUS'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "6b5a6034",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Areas = ['Designated', 'Established', 'Inscribed']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "90b7d330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a mapping function for protected areas\n",
+    "def map_protected_area_to_sdg(x):\n",
+    "    if x['STATUS'] in Areas:\n",
+    "        return \"SDG 11 - Sustainable Cities and Communities\"\n",
+    "    else:\n",
+    "        return \"not_mapped\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d23c0b68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply the mapping function to the 'protected_area' dataset\n",
+    "results = [map_protected_area_to_sdg(row) for index, row in protected_area.iterrows()]\n",
+    "\n",
+    "# Count the number of protected areas mapped  and not_mapped\n",
+    "mapped_count = results.count(\"SDG 11 - Sustainable Cities and Communities\")\n",
+    "not_mapped_count = results.count(\"not_mapped\")\n",
+    "\n",
+    "# Create pie chart to visualize the prototype\n",
+    "labels = [\"Mapped to SDG 11\", \"Not Mapped\"]\n",
+    "sizes = [mapped_count, not_mapped_count]\n",
+    "colors = [\"lightcoral\", \"lightblue\"]\n",
+    "explode = (0.1, 0)  # To explode the \"Mapped to SDG 11\" slice\n",
+    "\n",
+    "# Create the pie chart\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Set the title\n",
+    "plt.title('MAPPING OF PROTECTED AREAS to SDG 11')\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e08328d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WDPAID</th>\n",
+       "      <th>WDPA_PID</th>\n",
+       "      <th>PA_DEF</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>ORIG_NAME</th>\n",
+       "      <th>DESIG</th>\n",
+       "      <th>DESIG_ENG</th>\n",
+       "      <th>DESIG_TYPE</th>\n",
+       "      <th>IUCN_CAT</th>\n",
+       "      <th>INT_CRIT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MANG_AUTH</th>\n",
+       "      <th>MANG_PLAN</th>\n",
+       "      <th>VERIF</th>\n",
+       "      <th>METADATAID</th>\n",
+       "      <th>SUB_LOC</th>\n",
+       "      <th>PARENT_ISO</th>\n",
+       "      <th>ISO3</th>\n",
+       "      <th>SUPP_INFO</th>\n",
+       "      <th>CONS_OBJ</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10715.0</td>\n",
+       "      <td>10715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-KAM</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>209777.0</td>\n",
+       "      <td>209777_E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sarali Land between Rivers / Great Volzhsko-Ka...</td>\n",
+       "      <td>Great Volzhsko-Kamsky</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-TA</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>POLYGON ((49.30487 55.36806, 49.30433 55.37184...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     WDPAID  WDPA_PID PA_DEF  \\\n",
+       "0   10715.0     10715      1   \n",
+       "1  209777.0  209777_E      1   \n",
+       "\n",
+       "                                                NAME              ORIG_NAME  \\\n",
+       "0                                         Kronotskiy             Kronotskiy   \n",
+       "1  Sarali Land between Rivers / Great Volzhsko-Ka...  Great Volzhsko-Kamsky   \n",
+       "\n",
+       "                          DESIG                     DESIG_ENG     DESIG_TYPE  \\\n",
+       "0  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "1  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "\n",
+       "         IUCN_CAT        INT_CRIT  ...     MANG_AUTH     MANG_PLAN  \\\n",
+       "0  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "1  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "\n",
+       "            VERIF  METADATAID  SUB_LOC PARENT_ISO  ISO3       SUPP_INFO  \\\n",
+       "0  State Verified         840   RU-KAM        RUS   RUS  Not Applicable   \n",
+       "1  State Verified         840    RU-TA        RUS   RUS  Not Applicable   \n",
+       "\n",
+       "         CONS_OBJ                                           geometry  \n",
+       "0  Not Applicable  MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...  \n",
+       "1  Not Applicable  POLYGON ((49.30487 55.36806, 49.30433 55.37184...  \n",
+       "\n",
+       "[2 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "cd883b35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load json file\n",
+    "with open(\"C:/Users/adere/Downloads/IPCC_ClimateZoneMap_Vector.geojson\") as file:\n",
+    "    climate = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3d7ba44d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create GeoDataFrame from the climate data\n",
+    "Climate_data= gpd.GeoDataFrame.from_features(climate['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "77800e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>CLASS_NAME</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MULTIPOLYGON (((-37.65000 83.50000, -37.65000 ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MULTIPOLYGON (((-38.75000 83.40000, -38.75000 ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  CLASS_NAME\n",
+       "0  MULTIPOLYGON (((-37.65000 83.50000, -37.65000 ...           0\n",
+       "1  MULTIPOLYGON (((-38.75000 83.40000, -38.75000 ...           0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Climate_data.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "4fffe1d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define the mapping function for climate\n",
+    "def map_climate_to_sdg(x):\n",
+    "    if x['CLASS_NAME'] == '':\n",
+    "        return 'Climate Action'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "f9f0c4d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sKHRf7du3L/Fn9sQTT+iJJ54otmb+/PmqUqWKXnzxxULD5dNPP63hw4dr3bp1evjhh0t03Hz79u0rNKw3aNCgTH7ubdu21cyZMzVy5Ei1a9dOzz33nOrWraukpCS999572rlzp2bOnKk2bdoUu5833nhD3377rfr06aPmzZvL1dVVR48e1ezZs3XmzBm9/fbbBbaJj4+X1WpVTk6OTpw4oa+++koff/yxfH19tXbtWjk7O9tqn3/+eR04cEAvvPCCvvnmG/Xp00f16tVTVlaWjhw5og8//FCOjo6qVq3aDc/5P//5jyTpyJEjkq5+YZP/YLTHHntMkvT999/rb3/7mx577DHVr19fDg4O+uGHHzRjxgzVqFFDY8aMKdkHDACwIXQDAP401q1bp9TU1CLnJJakp556SitXrtTHH3+sUaNG/YHdFdSjRw+1adNG27ZtK7CuUaNGSkhI0HvvvadVq1bpww8/1MWLF+Xt7a077rhDb7zxRrH3Sufz8fGxPcxq2bJlWrp0qS5evChfX1+1bt1aa9asKfBAtmnTphW6ry1bthT7FPLSOH36tNauXauoqKgiR3Ojo6P1/PPPa/78+aUO3UXdgjBv3rwiv5AprWHDhumuu+7StGnTNHr0aJ05c0be3t5q166d4uLiFBERccN9REdHS5KWL1+ut99+W+np6fL29lZ4eLjWr1+vrl27FtimS5cukq4+f8Db21tNmzbVW2+9pQEDBhS47cDBwUEfffSRHnvsMc2bN0/jxo3TmTNn5OrqqgYNGqhjx47617/+VaJ7/nv16mX3/r333tN7770n6f8e4ubn56eAgABNmzZNKSkpunLliurUqaOoqCi9+OKLRd5qAAAomsUo6aMyAQAAAABAqXBPNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBLm6S5DeXl5OnHihDw8PGSxWMq7HQAAAACASQzD0Pnz5xUQECAHh6LHswndZejEiRMKDAws7zYAAAAAAH+Q5ORk1alTp8j1hO4y5OHhIenqh+7p6VnO3QAAAAAAzJKRkaHAwEBbDiwKobsM5V9S7unpSegGAAAAgFvAjW4t5kFqAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJikSnk3AABlxmIp7w4AAPjjGUZ5dwCgGIx0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYp19A9ZcoU3XXXXfLw8JCvr6969OihgwcP2tXExMTIYrHYvVq3bm1Xk5WVpWHDhsnHx0dubm7q3r27jh8/bleTlpam6OhoWa1WWa1WRUdH69y5c3Y1SUlJ6tatm9zc3OTj46Phw4crOzvblHMHAAAAAFR+5Rq6t27dqqFDh2rHjh3atGmTrly5os6dOyszM9OurkuXLkpJSbG91q9fb7d+5MiRWrVqlZYvX664uDhduHBBUVFRys3NtdX069dPCQkJio2NVWxsrBISEhQdHW1bn5ubq4ceekiZmZmKi4vT8uXLtWLFCo0ePdrcDwEAAAAAUGlZDMMwyruJfKdOnZKvr6+2bt2qe++9V9LVke5z585p9erVhW6Tnp6umjVr6uOPP1afPn0kSSdOnFBgYKDWr1+vyMhIJSYmKjQ0VDt27FCrVq0kSTt27FBERIQOHDigkJAQbdiwQVFRUUpOTlZAQIAkafny5YqJidHJkyfl6el5w/4zMjJktVqVnp5eonoAZcxiKe8OAAD44/15/pwHbiklzX9/qnu609PTJUne3t52y7/++mv5+vqqcePGGjx4sE6ePGlbFx8fr5ycHHXu3Nm2LCAgQGFhYdq2bZskafv27bJarbbALUmtW7eW1Wq1qwkLC7MFbkmKjIxUVlaW4uPjy/5kAQAAAACVXpXybiCfYRgaNWqU2rVrp7CwMNvyrl27qlevXgoKCtLRo0f18ssv6/7771d8fLxcXFyUmpoqZ2dneXl52e3Pz89PqampkqTU1FT5+voWOKavr69djZ+fn916Ly8vOTs722qul5WVpaysLNv7jIyMmzt5AAAAAECl9KcJ3c8995y+//57xcXF2S3Pv2RcksLCwtSyZUsFBQVp3bp16tmzZ5H7MwxDlmsuNbUUctnpzdRca8qUKZo0aVLRJwUAAAAAuKX9KS4vHzZsmNasWaMtW7aoTp06xdbWqlVLQUFB+umnnyRJ/v7+ys7OVlpaml3dyZMnbSPX/v7++u233wrs69SpU3Y1149op6WlKScnp8AIeL7x48crPT3d9kpOTi7ZCQMAAAAAbgnlGroNw9Bzzz2nlStXavPmzQoODr7hNmfOnFFycrJq1aolSQoPD5eTk5M2bdpkq0lJSdG+ffvUpk0bSVJERITS09O1a9cuW83OnTuVnp5uV7Nv3z6lpKTYajZu3CgXFxeFh4cX2ouLi4s8PT3tXgAAAAAA5CvXp5cPGTJES5cu1WeffaaQkBDbcqvVKldXV124cEETJ07Uo48+qlq1aunYsWN68cUXlZSUpMTERHl4eEiSnn32WX3++edatGiRvL29NWbMGJ05c0bx8fFydHSUdPXe8BMnTmju3LmSpKeeekpBQUFau3atpKtThjVv3lx+fn56++23dfbsWcXExKhHjx569913S3Q+PL0cKGc8vRwAcCvi6eVAuShp/ivX0F3UvdILFy5UTEyMLl26pB49emjv3r06d+6catWqpfvuu0+vvfaaAgMDbfWXL1/W2LFjtXTpUl26dEkdO3bUnDlz7GrOnj2r4cOHa82aNZKk7t27a/bs2apevbqtJikpSUOGDNHmzZvl6uqqfv366Z133pGLi0uJzofQDZQzQjcA4FZE6AbKRYUI3ZUNoRsoZ4RuAMCtiD/ngXJRIefpBgAAAACgMiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGCScg3dU6ZM0V133SUPDw/5+vqqR48eOnjwoF2NYRiaOHGiAgIC5Orqqg4dOmj//v12NVlZWRo2bJh8fHzk5uam7t276/jx43Y1aWlpio6OltVqldVqVXR0tM6dO2dXk5SUpG7dusnNzU0+Pj4aPny4srOzTTl3AAAAAEDlV66he+vWrRo6dKh27NihTZs26cqVK+rcubMyMzNtNVOnTtX06dM1e/Zs7d69W/7+/urUqZPOnz9vqxk5cqRWrVql5cuXKy4uThcuXFBUVJRyc3NtNf369VNCQoJiY2MVGxurhIQERUdH29bn5ubqoYceUmZmpuLi4rR8+XKtWLFCo0eP/mM+DAAAAABApWMxDMMo7ybynTp1Sr6+vtq6davuvfdeGYahgIAAjRw5Us8//7ykq6Pafn5+euutt/T0008rPT1dNWvW1Mcff6w+ffpIkk6cOKHAwECtX79ekZGRSkxMVGhoqHbs2KFWrVpJknbs2KGIiAgdOHBAISEh2rBhg6KiopScnKyAgABJ0vLlyxUTE6OTJ0/K09Pzhv1nZGTIarUqPT29RPUAypjFUt4dAADwx/vz/DkP3FJKmv/+VPd0p6enS5K8vb0lSUePHlVqaqo6d+5sq3FxcVH79u21bds2SVJ8fLxycnLsagICAhQWFmar2b59u6xWqy1wS1Lr1q1ltVrtasLCwmyBW5IiIyOVlZWl+Pj4QvvNyspSRkaG3QsAAAAAgHx/mtBtGIZGjRqldu3aKSwsTJKUmpoqSfLz87Or9fPzs61LTU2Vs7OzvLy8iq3x9fUtcExfX1+7muuP4+XlJWdnZ1vN9aZMmWK7R9xqtSowMLC0pw0AAAAAqMT+NKH7ueee0/fff69ly5YVWGe57pJRwzAKLLve9TWF1d9MzbXGjx+v9PR02ys5ObnYngAAAAAAt5Y/RegeNmyY1qxZoy1btqhOnTq25f7+/pJUYKT55MmTtlFpf39/ZWdnKy0trdia3377rcBxT506ZVdz/XHS0tKUk5NTYAQ8n4uLizw9Pe1eAAAAAADkK9fQbRiGnnvuOa1cuVKbN29WcHCw3frg4GD5+/tr06ZNtmXZ2dnaunWr2rRpI0kKDw+Xk5OTXU1KSor27dtnq4mIiFB6erp27dplq9m5c6fS09Ptavbt26eUlBRbzcaNG+Xi4qLw8PCyP3kAAAAAQKVXrk8vHzJkiJYuXarPPvtMISEhtuVWq1Wurq6SpLfeektTpkzRwoUL1ahRI02ePFlff/21Dh48KA8PD0nSs88+q88//1yLFi2St7e3xowZozNnzig+Pl6Ojo6SpK5du+rEiROaO3euJOmpp55SUFCQ1q5dK+nqlGHNmzeXn5+f3n77bZ09e1YxMTHq0aOH3n333RKdD08vB8oZTy8HANyKeHo5UC5Kmv/KNXQXda/0woULFRMTI+nqaPikSZM0d+5cpaWlqVWrVnrvvfdsD1uTpMuXL2vs2LFaunSpLl26pI4dO2rOnDl2DzY7e/ashg8frjVr1kiSunfvrtmzZ6t69eq2mqSkJA0ZMkSbN2+Wq6ur+vXrp3feeUcuLi4lOh9CN1DOCN0AgFsRoRsoFxUidFc2hG6gnBG6AQC3Iv6cB8pFhZynGwAAAACAyoTQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJyiR0nzt3rix2AwAAAABApVLq0P3WW2/pk08+sb3v3bu3atSoodq1a+u7774r0+YAAAAAAKjISh26586dq8DAQEnSpk2btGnTJm3YsEFdu3bV2LFjy7xBAAAAAAAqqiql3SAlJcUWuj///HP17t1bnTt3Vr169dSqVasybxAAAAAAgIqq1CPdXl5eSk5OliTFxsbqgQcekCQZhqHc3Nyy7Q4AAAAAgAqs1CPdPXv2VL9+/dSoUSOdOXNGXbt2lSQlJCSoYcOGZd4gAAAAAAAVValD94wZM1SvXj0lJydr6tSpcnd3l3T1svMhQ4aUeYMAAAAAAFRUFsMwjPJuorLIyMiQ1WpVenq6PD09y7sd4NZjsZR3BwAA/PH4cx4oFyXNfzc1T/fHH3+sdu3aKSAgQL/88oskaebMmfrss89urlsAAAAAACqhUofu999/X6NGjVLXrl117tw528PTqlevrpkzZ5Z1fwAAAAAAVFilDt3vvvuu5s2bp5deekmOjo625S1bttQPP/xQps0BAAAAAFCRlTp0Hz16VHfeeWeB5S4uLsrMzCyTpgAAAAAAqAxKHbqDg4OVkJBQYPmGDRsUGhpaFj0BAAAAAFAplHrKsLFjx2ro0KG6fPmyDMPQrl27tGzZMk2ZMkUffvihGT0CAAAAAFAhlTp0DxgwQFeuXNG4ceN08eJF9evXT7Vr19asWbPUt29fM3oEAAAAAKBCKlXovnLlipYsWaJu3bpp8ODBOn36tPLy8uTr62tWfwAAAAAAVFiluqe7SpUqevbZZ5WVlSVJ8vHxIXADAAAAAFCEUj9IrVWrVtq7d68ZvQAAAAAAUKmU+p7uIUOGaPTo0Tp+/LjCw8Pl5uZmt75Zs2Zl1hwAAAAAABWZxTAMozQbODgUHBy3WCwyDEMWi0W5ubll1lxFk5GRIavVqvT0dHl6epZ3O8Ctx2Ip7w4AAPjjle7PeQBlpKT5r9Qj3UePHv1djQEAAAAAcKsodegOCgoyow8AAAAAACqdUofuxYsXF7v+r3/96003AwAAAABAZVLqe7q9vLzs3ufk5OjixYtydnZWtWrVdPbs2TJtsCLhnm6gnHFPNwDgVsQ93UC5KGn+K/WUYWlpaXavCxcu6ODBg2rXrp2WLVv2u5oGAAAAAKAyKXXoLkyjRo305ptvasSIEWWxOwAAAAAAKoUyCd2S5OjoqBMnTpTV7gAAAAAAqPBK/SC1NWvW2L03DEMpKSmaPXu22rZtW2aNAQAAAABQ0ZU6dPfo0cPuvcViUc2aNXX//fdr2rRpZdUXAAAAAAAVXqlDd15enhl9AAAAAABQ6ZT6nu5XX31VFy9eLLD80qVLevXVV8ukKQAAAAAAKoNSz9Pt6OiolJQU+fr62i0/c+aMfH19lZubW6YNViTM0w2UM+bpBgDcipinGygXps3TbRiGLIX8Yfvdd9/J29u7tLsDAAAAAKDSKvE93V5eXrJYLLJYLGrcuLFd8M7NzdWFCxf0zDPPmNIkAAAAAAAVUYlD98yZM2UYhgYOHKhJkybJarXa1jk7O6tevXqKiIgwpUkAAAAAACqiEofu/v37S5KCg4PVpk0bOTk5mdYUAAAAAACVQamnDGvfvr3t35cuXVJOTo7deh4gBgAAAADAVaV+kNrFixf13HPPydfXV+7u7vLy8rJ7AQAAAACAq0oduseOHavNmzdrzpw5cnFx0YcffqhJkyYpICBAixcvNqNHAAAAAAAqpFJfXr527VotXrxYHTp00MCBA3XPPfeoYcOGCgoK0pIlS/SXv/zFjD4BAAAAAKhwSj3SffbsWQUHB0u6ev/22bNnJUnt2rXTN998U7bdAQAAAABQgZU6dNevX1/Hjh2TJIWGhurf//63pKsj4NWrVy/Vvr755ht169ZNAQEBslgsWr16td36mJgY29zg+a/WrVvb1WRlZWnYsGHy8fGRm5ubunfvruPHj9vVpKWlKTo6WlarVVarVdHR0Tp37pxdTVJSkrp16yY3Nzf5+Pho+PDhys7OLtX5AAAAAABwrVKH7gEDBui7776TJI0fP952b/ff/vY3jR07tlT7yszM1B133KHZs2cXWdOlSxelpKTYXuvXr7dbP3LkSK1atUrLly9XXFycLly4oKioKOXm5tpq+vXrp4SEBMXGxio2NlYJCQmKjo62rc/NzdVDDz2kzMxMxcXFafny5VqxYoVGjx5dqvMBAAAAAOBaFsMwjN+zg6SkJO3Zs0cNGjTQHXfccfONWCxatWqVevToYVsWExOjc+fOFRgBz5eenq6aNWvq448/Vp8+fSRJJ06cUGBgoNavX6/IyEglJiYqNDRUO3bsUKtWrSRJO3bsUEREhA4cOKCQkBBt2LBBUVFRSk5OVkBAgCRp+fLliomJ0cmTJ0s8DVpGRoasVqvS09OZOg0oDxZLeXcAAMAf7/f9OQ/gJpU0/5V6pPtaly9fVt26ddWzZ8/fFbiL8/XXX8vX11eNGzfW4MGDdfLkSdu6+Ph45eTkqHPnzrZlAQEBCgsL07Zt2yRJ27dvl9VqtQVuSWrdurWsVqtdTVhYmC1wS1JkZKSysrIUHx9fZG9ZWVnKyMiwewEAAAAAkK/UoTs3N1evvfaaateuLXd3dx05ckSS9PLLL2v+/Pll2lzXrl21ZMkSbd68WdOmTdPu3bt1//33KysrS5KUmpoqZ2fnAvOD+/n5KTU11Vbj6+tbYN++vr52NX5+fnbrvby85OzsbKspzJQpU2z3iVutVgUGBv6u8wUAAAAAVC6lDt1vvPGGFi1apKlTp8rZ2dm2vGnTpvrwww/LtLk+ffrooYceUlhYmLp166YNGzbo0KFDWrduXbHbGYYhyzWXmVoKueT0ZmquN378eKWnp9teycnJJTktAAAAAMAtotShe/Hixfrggw/0l7/8RY6OjrblzZo104EDB8q0uevVqlVLQUFB+umnnyRJ/v7+ys7OVlpaml3dyZMnbSPX/v7++u233wrs69SpU3Y1149op6WlKScnp8AI+LVcXFzk6elp9wIAAAAAIF+pQ/evv/6qhg0bFliel5ennJycMmmqKGfOnFFycrJq1aolSQoPD5eTk5M2bdpkq0lJSdG+ffvUpk0bSVJERITS09O1a9cuW83OnTuVnp5uV7Nv3z6lpKTYajZu3CgXFxeFh4ebek4AAAAAgMqrSmk3uP322/Xf//5XQUFBdss//fRT3XnnnaXa14ULF/Tzzz/b3h89elQJCQny9vaWt7e3Jk6cqEcffVS1atXSsWPH9OKLL8rHx0ePPPKIJMlqtWrQoEEaPXq0atSoIW9vb40ZM0ZNmzbVAw88IElq0qSJunTposGDB2vu3LmSpKeeekpRUVEKCQmRJHXu3FmhoaGKjo7W22+/rbNnz2rMmDEaPHgwo9cAAAAAgJtW6tD9yiuvKDo6Wr/++qvy8vK0cuVKHTx4UIsXL9bnn39eqn3t2bNH9913n+39qFGjJEn9+/fX+++/rx9++EGLFy/WuXPnVKtWLd1333365JNP5OHhYdtmxowZqlKlinr37q1Lly6pY8eOWrRokd2l70uWLNHw4cNtTznv3r273dzgjo6OWrdunYYMGaK2bdvK1dVV/fr10zvvvFPajwcAAAAAAJubmqf7iy++0OTJkxUfH6+8vDy1aNFCEyZMsJu661bEPN1AOWOebgDArYh5uoFyUdL8V+LQfeTIEQUHBxf7NO9bHaEbKGf89wkAcCsidAPloqT5r8QPUmvUqJFOnTple9+nT59CnwoOAAAAAACuKnHovn5AfP369crMzCzzhgAAAAAAqCxKPWUYAAAAAAAomRKHbovFUuB+bu7vBgAAAACgaCWeMswwDMXExMjFxUWSdPnyZT3zzDNyc3Ozq1u5cmXZdggAAAAAQAVV4tDdv39/u/dPPPFEmTcDAAAAAEBlUuLQvXDhQjP7AAAAAACg0uFBagAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJShS6W7RoobS0NEnSq6++qosXL5raFAAAAAAAlUGJQndiYqIyMzMlSZMmTdKFCxdMbQoAAAAAgMqgRFOGNW/eXAMGDFC7du1kGIbeeecdubu7F1o7YcKEMm0QAAAAAICKymIYhnGjooMHD+qVV17R4cOH9b///U+hoaGqUqVgXrdYLPrf//5nSqMVQUZGhqxWq9LT0+Xp6Vne7QC3HoulvDsAAOCPd+M/5wGYoKT5r0Sh+1oODg5KTU2Vr6/v726ysiF0A+WM0A0AuBURuoFyUdL8V6LLy6+Vl5f3uxoDAAAAAOBWUerQLUmHDx/WzJkzlZiYKIvFoiZNmmjEiBFq0KBBWfcHAAAAAECFVep5ur/44guFhoZq165datasmcLCwrRz507dfvvt2rRpkxk9AgAAAABQIZX6nu4777xTkZGRevPNN+2Wv/DCC9q4cSMPUuOebqD8cE83AOBWxD3dQLkoaf4r9Uh3YmKiBg0aVGD5wIED9eOPP5Z2dwAAAAAAVFqlDt01a9ZUQkJCgeUJCQk80RwAAAAAgGuU+kFqgwcP1lNPPaUjR46oTZs2slgsiouL01tvvaXRo0eb0SMAAAAAABVSqe/pNgxDM2fO1LRp03TixAlJUkBAgMaOHavhw4fLcgvfU8k93UA5u4X/+wMAuIVxTzdQLkqa/0oduq91/vx5SZKHh8fN7qJSIXQD5YzQDQC4FRG6gXJR0vx3U/N05yNsAwAAAABQtFI/SA0AAAAAAJQMoRsAAAAAAJMQugEAAAAAMEmpQndOTo7uu+8+HTp0yKx+AAAAAACoNEoVup2cnLRv375belowAAAAAABKqtSXl//1r3/V/PnzzegFAAAAAIBKpdRThmVnZ+vDDz/Upk2b1LJlS7m5udmtnz59epk1BwAAAABARVbq0L1v3z61aNFCkgrc281l5wAAAAAA/J9Sh+4tW7aY0QcAAAAAAJXOTU8Z9vPPP+uLL77QpUuXJEmGYZRZUwAAAAAAVAalDt1nzpxRx44d1bhxYz344INKSUmRJD355JMaPXp0mTcIAAAAAEBFVerQ/be//U1OTk5KSkpStWrVbMv79Omj2NjYMm0OAAAAAICKrNT3dG/cuFFffPGF6tSpY7e8UaNG+uWXX8qsMQAAAAAAKrpSj3RnZmbajXDnO336tFxcXMqkKQAAAAAAKoNSh+57771Xixcvtr23WCzKy8vT22+/rfvuu69MmwMAAAAAoCIr9eXlb7/9tjp06KA9e/YoOztb48aN0/79+3X27Fl9++23ZvQIAAAAAECFVOqR7tDQUH3//fe6++671alTJ2VmZqpnz57au3evGjRoYEaPAAAAAABUSBaDCbbLTEZGhqxWq9LT0+Xp6Vne7QC3HoulvDsAAOCPx5/zQLkoaf4r9eXlkpSWlqb58+crMTFRFotFTZo00YABA+Tt7X3TDQMAAAAAUNmU+vLyrVu3Kjg4WP/4xz+Ulpams2fP6h//+IeCg4O1detWM3oEAAAAAKBCKvXl5WFhYWrTpo3ef/99OTo6SpJyc3M1ZMgQffvtt9q3b58pjVYEXF4OlDMuLwcA3Iq4vBwoFyXNf6Ue6T58+LBGjx5tC9yS5OjoqFGjRunw4cM31y0AAAAAAJVQqUN3ixYtlJiYWGB5YmKimjdvXhY9AQAAAABQKZToQWrff/+97d/Dhw/XiBEj9PPPP6t169aSpB07dui9997Tm2++aU6XAAAAAABUQCW6p9vBwUEWi0U3KrVYLMrNzS2z5ioa7ukGyhn3dAMAbkXc0w2UizKdMuzo0aNl1hgAAAAAALeKEoXuoKAgs/sAAAAAAKDSKVHovt6vv/6qb7/9VidPnlReXp7duuHDh5dJYwAAAAAAVHSlDt0LFy7UM888I2dnZ9WoUUOWa+6htFgshG4AAAAAAP6/Ej1I7VqBgYF65plnNH78eDk4lHrGsUqNB6kB5YwHqQEAbkU8SA0oFyXNf6VOzRcvXlTfvn0J3AAAAAAA3ECpk/OgQYP06aefmtELAAAAAACVSqkvL8/NzVVUVJQuXbqkpk2bysnJyW799OnTy7TBioTLy4FyxuXlAIBbEZeXA+WiTOfpvtbkyZP1xRdfKCQkRJIKPEgNAAAAAABcVerQPX36dC1YsEAxMTEmtAMAAAAAQOVR6nu6XVxc1LZtWzN6AQAAAACgUil16B4xYoTeffddM3oBAAAAAKBSKfXl5bt27dLmzZv1+eef6/bbby/wILWVK1eWWXMAAAAAAFRkpQ7d1atXV8+ePc3oBQAAAACASqXUoXvhwoVm9AEAAAAAQKVT6nu6AQAAAABAyZR6pDs4OLjY+biPHDnyuxoCAAAAAKCyKHXoHjlypN37nJwc7d27V7GxsRo7dmxZ9QUAAAAAQIVX6tA9YsSIQpe/99572rNnz+9uCAAAAACAyqLM7unu2rWrVqxYUVa7AwAAAACgwiuz0P2f//xH3t7eZbU7AAAAAAAqvFJfXn7nnXfaPUjNMAylpqbq1KlTmjNnTpk2BwAAAABARVbq0N2jRw+79w4ODqpZs6Y6dOig2267raz6AgAAAACgwrMYhmGUdxOVRUZGhqxWq9LT0+Xp6Vne7QC3nmKmMwQAoNLiz3mgXJQ0/5XZPd0AAAAAAMBeiS8vd3BwsLuXuzAWi0VXrlz53U0BAAAAAFAZlDh0r1q1qsh127Zt07vvviuuVAcAAAAA4P+UOHQ//PDDBZYdOHBA48eP19q1a/WXv/xFr732Wpk2BwAAAABARXZT93SfOHFCgwcPVrNmzXTlyhUlJCToo48+Ut26dcu6PwAAAAAAKqxShe709HQ9//zzatiwofbv36+vvvpKa9euVVhY2E0d/JtvvlG3bt0UEBAgi8Wi1atX2603DEMTJ05UQECAXF1d1aFDB+3fv9+uJisrS8OGDZOPj4/c3NzUvXt3HT9+3K4mLS1N0dHRslqtslqtio6O1rlz5+xqkpKS1K1bN7m5ucnHx0fDhw9Xdnb2TZ0XAAAAAABSKUL31KlTVb9+fX3++edatmyZtm3bpnvuued3HTwzM1N33HGHZs+eXeQxp0+frtmzZ2v37t3y9/dXp06ddP78eVvNyJEjtWrVKi1fvlxxcXG6cOGCoqKilJuba6vp16+fEhISFBsbq9jYWCUkJCg6Otq2Pjc3Vw899JAyMzMVFxen5cuXa8WKFRo9evTvOj8AAAAAwK2txPN0Ozg4yNXVVQ888IAcHR2LrFu5cuXNNWKxaNWqVerRo4ekq6PcAQEBGjlypJ5//nlJV0e1/fz89NZbb+npp59Wenq6atasqY8//lh9+vSRdPXS98DAQK1fv16RkZFKTExUaGioduzYoVatWkmSduzYoYiICB04cEAhISHasGGDoqKilJycrICAAEnS8uXLFRMTo5MnT5Z4zm3m6QbKGfN0AwBuRTzMGCgXZT5P91//+lf17t1b3t7etsu0C3uVlaNHjyo1NVWdO3e2LXNxcVH79u21bds2SVJ8fLxycnLsagICAhQWFmar2b59u6xWqy1wS1Lr1q1ltVrtasLCwmyBW5IiIyOVlZWl+Pj4InvMyspSRkaG3QsAAAAAgHwlfnr5okWLTGyjoNTUVEmSn5+f3XI/Pz/98ssvthpnZ2d5eXkVqMnfPjU1Vb6+vgX27+vra1dz/XG8vLzk7OxsqynMlClTNGnSpFKeGQAAAADgVnFTTy//I1muu1zUMIwCy653fU1h9TdTc73x48crPT3d9kpOTi62LwAAAADAreVPG7r9/f0lqcBI88mTJ22j0v7+/srOzlZaWlqxNb/99luB/Z86dcqu5vrjpKWlKScnp8AI+LVcXFzk6elp9wIAAAAAIN+fNnQHBwfL399fmzZtsi3Lzs7W1q1b1aZNG0lSeHi4nJyc7GpSUlK0b98+W01ERITS09O1a9cuW83OnTuVnp5uV7Nv3z6lpKTYajZu3CgXFxeFh4ebep4AAAAAgMqrxPd0m+HChQv6+eefbe+PHj2qhIQEeXt7q27duho5cqQmT56sRo0aqVGjRpo8ebKqVaumfv36SZKsVqsGDRqk0aNHq0aNGvL29taYMWPUtGlTPfDAA5KkJk2aqEuXLho8eLDmzp0rSXrqqacUFRWlkJAQSVLnzp0VGhqq6Ohovf322zp79qzGjBmjwYMHM3oNAAAAALhp5Rq69+zZo/vuu8/2ftSoUZKk/v37a9GiRRo3bpwuXbqkIUOGKC0tTa1atdLGjRvl4eFh22bGjBmqUqWKevfurUuXLqljx45atGiR3bRmS5Ys0fDhw21POe/evbvd3OCOjo5at26dhgwZorZt28rV1VX9+vXTO++8Y/ZHAAAAAACoxEo8TzdujHm6gXLGPN0AgFsRf84D5aLM5+kGAAAAAAClQ+gGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAEzypw7dEydOlMVisXv5+/vb1huGoYkTJyogIECurq7q0KGD9u/fb7ePrKwsDRs2TD4+PnJzc1P37t11/Phxu5q0tDRFR0fLarXKarUqOjpa586d+yNOEQAAAABQif2pQ7ck3X777UpJSbG9fvjhB9u6qVOnavr06Zo9e7Z2794tf39/derUSefPn7fVjBw5UqtWrdLy5csVFxenCxcuKCoqSrm5ubaafv36KSEhQbGxsYqNjVVCQoKio6P/0PMEAAAAAFQ+Vcq7gRupUqWK3eh2PsMwNHPmTL300kvq2bOnJOmjjz6Sn5+fli5dqqefflrp6emaP3++Pv74Yz3wwAOSpH/9618KDAzUl19+qcjISCUmJio2NlY7duxQq1atJEnz5s1TRESEDh48qJCQkD/uZAEAAAAAlcqffqT7p59+UkBAgIKDg9W3b18dOXJEknT06FGlpqaqc+fOtloXFxe1b99e27ZtkyTFx8crJyfHriYgIEBhYWG2mu3bt8tqtdoCtyS1bt1aVqvVVgMAAAAAwM34U490t2rVSosXL1bjxo3122+/6fXXX1ebNm20f/9+paamSpL8/PzstvHz89Mvv/wiSUpNTZWzs7O8vLwK1ORvn5qaKl9f3wLH9vX1tdUUJSsrS1lZWbb3GRkZpT9JAAAAAECl9acO3V27drX9u2nTpoqIiFCDBg300UcfqXXr1pIki8Vit41hGAWWXe/6msLqS7KfKVOmaNKkSTc8DwAAAADArelPf3n5tdzc3NS0aVP99NNPtvu8rx+NPnnypG3029/fX9nZ2UpLSyu25rfffitwrFOnThUYRb/e+PHjlZ6ebnslJyff9LkBAAAAACqfChW6s7KylJiYqFq1aik4OFj+/v7atGmTbX12dra2bt2qNm3aSJLCw8Pl5ORkV5OSkqJ9+/bZaiIiIpSenq5du3bZanbu3Kn09HRbTVFcXFzk6elp9wIAAAAAIN+f+vLyMWPGqFu3bqpbt65Onjyp119/XRkZGerfv78sFotGjhypyZMnq1GjRmrUqJEmT56satWqqV+/fpIkq9WqQYMGafTo0apRo4a8vb01ZswYNW3a1PY08yZNmqhLly4aPHiw5s6dK0l66qmnFBUVxZPLAQAAAAC/y586dB8/flyPP/64Tp8+rZo1a6p169basWOHgoKCJEnjxo3TpUuXNGTIEKWlpalVq1bauHGjPDw8bPuYMWOGqlSpot69e+vSpUvq2LGjFi1aJEdHR1vNkiVLNHz4cNtTzrt3767Zs2f/sScLAAAAAKh0LIZhGOXdRGWRkZEhq9Wq9PR0LjUHysMNHn4IAEClxJ/zQLkoaf6rUPd0AwAAAABQkRC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELqvM2fOHAUHB6tq1aoKDw/Xf//73/JuCQAAAABQQRG6r/HJJ59o5MiReumll7R3717dc8896tq1q5KSksq7NQAAAABABWQxDMMo7yb+LFq1aqUWLVro/fffty1r0qSJevTooSlTptxw+4yMDFmtVqWnp8vT09PMVgEUxmIp7w4AAPjj8ec8UC5Kmv8Y6f7/srOzFR8fr86dO9st79y5s7Zt21ZOXQEAAAAAKrIq5d3An8Xp06eVm5srPz8/u+V+fn5KTU0tdJusrCxlZWXZ3qenp0u6+o0HAAAA8Ifgb0+gXOTnvhtdPE7ovo7lustTDcMosCzflClTNGnSpALLAwMDTekNAAAAKMBqLe8OgFva+fPnZS3mf4eE7v/Px8dHjo6OBUa1T548WWD0O9/48eM1atQo2/u8vDydPXtWNWrUKDKoAwBQ2WRkZCgwMFDJyck80wQAcMswDEPnz59XQEBAsXWE7v/P2dlZ4eHh2rRpkx555BHb8k2bNunhhx8udBsXFxe5uLjYLatevbqZbQIA8Kfl6elJ6AYA3FKKG+HOR+i+xqhRoxQdHa2WLVsqIiJCH3zwgZKSkvTMM8+Ud2sAAAAAgAqI0H2NPn366MyZM3r11VeVkpKisLAwrV+/XkFBQeXdGgAAAACgAmKebgAA8LtkZWVpypQpGj9+fIHbrgAAuNURugEAAAAAMIlDeTcAAAAAAEBlRegGAAAAAMAkhG4AAFAiFotFq1evliQdO3ZMFotFCQkJ5drTn8GiRYuYMhQAUCRCNwDglnXy5Ek9/fTTqlu3rlxcXOTv76/IyEht377dVlOvXj1ZLBZZLBa5urqqXr166t27tzZv3lzoPlesWKH7779fXl5eqlatmkJCQjRw4EDt3bu32F7eeOMNtWnTRtWqVSs0wJ05c0ZdunRRQECAXFxcFBgYqOeee04ZGRm/6zPIl5qaqmHDhql+/fq2/Xfr1k1fffVVofWBgYG2mT7MZEa437ZtmxwdHdWlS5dSb1uvXj3NnDnTblmfPn106NChMuoOAFDZELoBALesRx99VN99950++ugjHTp0SGvWrFGHDh109uxZu7r8qSQPHjyoxYsXq3r16nrggQf0xhtv2NU9//zz6tOnj5o3b641a9Zo//79+uCDD9SgQQO9+OKLxfaSnZ2tXr166dlnny10vYODgx5++GGtWbNGhw4d0qJFi/Tll1/qmWee+X0fgq4G2/DwcG3evFlTp07VDz/8oNjYWN13330aOnRoods4OjrK399fVapUvNlHFyxYoGHDhikuLk5JSUm/e3+urq7y9fUtg84AAJWSAQDALSgtLc2QZHz99dfF1gUFBRkzZswosHzChAmGg4ODceDAAcMwDGP79u2GJGPWrFmF7icvL69EfS1cuNCwWq0lqp01a5ZRp06dEtUWp2vXrkbt2rWNCxcuFFiXlpZm+7ckY9WqVYZhGMbRo0cNScbevXsNwzCMLVu2GJKM2NhYo3nz5kbVqlWN++67z/jtt9+M9evXG7fddpvh4eFh9O3b18jMzLTtc8OGDUbbtm0Nq9VqeHt7Gw899JDx888/2x3z2lf79u1t6xYsWGDcdttthouLixESEmK89957NzzXCxcuGB4eHsaBAweMPn36GJMmTSpQ89lnnxnh4eGGi4uLUaNGDeORRx4xDMMw2rdvX6Afwyj8ZzZnzhyjfv36hpOTk9G4cWNj8eLFduslGfPmzTN69OhhuLq6Gg0bNjQ+++yzG/YPAKh4GOkGANyS3N3d5e7urtWrVysrK6vU248YMUKGYeizzz6TJC1btkzu7u4aMmRIofUWi+V39Xu9EydOaOXKlWrfvv3v2s/Zs2cVGxuroUOHys3NrcD60t6rPHHiRM2ePVvbtm1TcnKyevfurZkzZ2rp0qVat26dNm3apHfffddWn5mZqVGjRmn37t366quv5ODgoEceeUR5eXmSpF27dkmSvvzyS6WkpGjlypWSpHnz5umll17SG2+8ocTERE2ePFkvv/yyPvroo2L7++STTxQSEqKQkBA98cQTWrhwoYxrZk9dt26devbsqYceekh79+7VV199pZYtW0qSVq5cqTp16tiufEhJSSn0GKtWrdKIESM0evRo7du3T08//bQGDBigLVu22NVNmjRJvXv31vfff68HH3xQf/nLXwpcZQEAqATKO/UDAFBe/vOf/xheXl5G1apVjTZt2hjjx483vvvuO7uaoka6DcMw/Pz8jGeffdYwDMPo0qWL0axZM7v106ZNM9zc3Gyvc+fO3bCnG4109+3b13B1dTUkGd26dTMuXbp0w30WZ+fOnYYkY+XKlTesVQlGur/88ktb/ZQpUwxJxuHDh23Lnn76aSMyMrLIY5w8edKQZPzwww+FHidfYGCgsXTpUrtlr732mhEREVHsObRp08aYOXOmYRiGkZOTY/j4+BibNm2yrY+IiDD+8pe/FLl9Yb8P1//M2rRpYwwePNiuplevXsaDDz5oey/J+Pvf/257f+HCBcNisRgbNmwotn8AQMXDSDcA4Jb16KOP6sSJE1qzZo0iIyP19ddfq0WLFlq0aFGJtjcMw24E+/rR7IEDByohIUFz585VZmam3YjqzZoxY4b+97//afXq1Tp8+LBGjRpVZO0zzzxjG9F3d3cv8hwK6/1mNWvWzPZvPz8/VatWTfXr17dbdvLkSdv7w4cPq1+/fqpfv748PT0VHBwsScXea33q1CklJydr0KBBduf3+uuv6/Dhw0Vud/DgQe3atUt9+/aVJFWpUkV9+vTRggULbDUJCQnq2LFj6U/8GomJiWrbtq3dsrZt2yoxMdFu2bWflZubmzw8POw+GwBA5VDxnn4CAEAZqlq1qjp16qROnTppwoQJevLJJ/XKK68oJiam2O3OnDmjU6dO2UJio0aNFBcXp5ycHDk5OUm6eml29erVdfz48TLr19/fX/7+/rrttttUo0YN3XPPPXr55ZdVq1atArWvvvqqxowZU+z+GjVqJIvFosTERPXo0eN395d/7tLVIH/t+/xl+ZeOS1K3bt0UGBioefPmKSAgQHl5eQoLC1N2dnaRx8jfft68eWrVqpXdOkdHxyK3mz9/vq5cuaLatWvblhmGIScnJ6WlpcnLy0uurq4lO9EbuP5LjOu/oJF0w88GAFA5MNINAMA1QkNDlZmZecO6WbNmycHBwRZUH3/8cV24cEFz5swxucP/kz9KXdQ96b6+vmrYsKHtVRhvb29FRkbqvffeK/S8z507V2b9Xu/MmTNKTEzU3//+d3Xs2FFNmjRRWlqaXY2zs7MkKTc317bMz89PtWvX1pEjR+zOr2HDhrYvQa535coVLV68WNOmTVNCQoLt9d133ykoKEhLliyRdHX0uahp0vL7ubaXwjRp0kRxcXF2y7Zt26YmTZoUux0AoHJipBsAcEs6c+aMevXqpYEDB6pZs2by8PDQnj17NHXqVD388MN2tefPn1dqaqpycnJ09OhR/etf/9KHH36oKVOm2MJsRESERo8erdGjR+uXX35Rz549bXNZz58/XxaLRQ4ORX/XnZSUpLNnzyopKUm5ubm2eakbNmwod3d3rV+/Xr/99pvuuusuubu768cff9S4cePUtm1b1atX73d9FnPmzFGbNm10991369VXX1WzZs105coVbdq0Se+//36By6LLipeXl2rUqKEPPvhAtWrVUlJSkl544QW7Gl9fX7m6uio2NlZ16tRR1apVZbVaNXHiRA0fPlyenp7q2rWrsrKytGfPHqWlpRV6yf3nn3+utLQ0DRo0SFar1W7dY489pvnz5+u5557TK6+8oo4dO6pBgwbq27evrly5og0bNmjcuHGSrs7T/c0336hv375ycXGRj49PgWONHTtWvXv3VosWLdSxY0etXbtWK1eu1JdfflmGnx4AoMIozxvKAQAoL5cvXzZeeOEFo0WLFobVajWqVatmhISEGH//+9+Nixcv2uqCgoJs00M5OzsbdevWNXr37m1s3ry50P1+8sknRocOHQyr1Wo4OTkZderUMfr162fs2LGj2H769+9fYDoqScaWLVsMwzCMzZs3GxEREYbVajWqVq1qNGrUyHj++eftpvT6PU6cOGEMHTrUCAoKMpydnY3atWsb3bt3tx3fMEr2ILVr+ynsoXCvvPKKcccdd9jeb9q0yWjSpInh4uJiNGvWzPj666/tjmMYhjFv3jwjMDDQcHBwsJsybMmSJUbz5s0NZ2dnw8vLy7j33nuLfCBcVFSU3YPMrhUfH29IMuLj4w3DMIwVK1bY9uvj42P07NnTVrt9+3ajWbNmhouLy++eMuzaczQMw7BarcbChQsL7REAUHFZDKMMnuoCAAAAAAAK4J5uAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAFDpxMTEqEePHuXdBgAAhG4AAP5IJ0+e1NNPP626devKxcVF/v7+ioyM1Pbt22019erVk8VikcVikaurq+rVq6fevXtr8+bNhe5zxYoVuv/+++Xl5aVq1aopJCREAwcO1N69e2/Yz5YtW/Tggw+qRo0aqlatmkJDQzV69Gj9+uuvJT6nDh06aOTIkSWu/yPMmjVLixYtKu82AAAgdAMA8Ed69NFH9d133+mjjz7SoUOHtGbNGnXo0EFnz561q3v11VeVkpKigwcPavHixapevboeeOABvfHGG3Z1zz//vPr06aPmzZtrzZo12r9/vz744AM1aNBAL774YrG9zJ07Vw888ID8/f21YsUK/fjjj/rnP/+p9PR0TZs2rczP/Y+Qm5urvLw8Wa1WVa9evbzbAQBAMgAAwB8iLS3NkGR8/fXXxdYFBQUZM2bMKLB8woQJhoODg3HgwAHDMAxj+/bthiRj1qxZhe4nLy+vyGMkJycbzs7OxsiRI4vs1TAM4/Tp00bfvn2N2rVrG66urkZYWJixdOlSW13//v0NSXavo0ePGoZhGPv37ze6du1quLm5Gb6+vsYTTzxhnDp1yrZtRkaG0a9fP6NatWqGv7+/MX36dKN9+/bGiBEjbDVnz541oqOjjerVqxuurq5Gly5djEOHDtnWL1y40LBarcbatWuNJk2aGI6OjsaRI0eM/v37Gw8//LDdZ/HWW28ZwcHBRtWqVY1mzZoZn376qd1x+vXrZ/j4+BhVq1Y1GjZsaCxYsKDIzw8AgJJipBsAgD+Iu7u73N3dtXr1amVlZZV6+xEjRsgwDH322WeSpGXLlsnd3V1DhgwptN5isRS5r08//VTZ2dkaN25coevzR4kvX76s8PBwff7559q3b5+eeuopRUdHa+fOnZKuXsYdERGhwYMHKyUlRSkpKQoMDFRKSorat2+v5s2ba8+ePYqNjdVvv/2m3r17244xatQoffvtt1qzZo02bdqk//73v/rf//5n10dMTIz27NmjNWvWaPv27TIMQw8++KBycnJsNRcvXtSUKVP04Ycfav/+/fL19S1wPn//+9+1cOFCvf/++9q/f7/+9re/6YknntDWrVslSS+//LJ+/PFHbdiwQYmJiXr//ffl4+NT5OcHAEBJVSnvBgAAuFVUqVJFixYt0uDBg/XPf/5TLVq0UPv27dW3b181a9bshtt7e3vL19dXx44dkyQdOnRI9evXV5Uq//d/59OnT9eECRNs73/99VdZrdYC+/rpp5/k6empWrVqFXvM2rVra8yYMbb3w4YNU2xsrD799FO1atVKVqtVzs7Oqlatmvz9/W1177//vlq0aKHJkyfbli1YsECBgYE6dOiQatWqpY8++khLly5Vx44dJUkLFy5UQECAXY9r1qzRt99+qzZt2kiSlixZosDAQK1evVq9evWSJOXk5GjOnDm64447Cj2HzMxMTZ8+XZs3b1ZERIQkqX79+oqLi9PcuXPVvn17JSUl6c4771TLli0lXb2vHgCAssBINwAAf6BHH31UJ06c0Jo1axQZGamvv/5aLVq0KPFDvwzDsBvBvn40e+DAgUpISNDcuXOVmZkpwzBKtJ+i5Obm6o033lCzZs1Uo0YNubu7a+PGjUpKSip2u/j4eG3ZssU2uu/u7q7bbrtNknT48GEdOXJEOTk5uvvuu23bWK1WhYSE2N4nJiaqSpUqatWqlW1ZjRo1FBISosTERNsyZ2fnYr+0+PHHH3X58mV16tTJrp/Fixfr8OHDkqRnn31Wy5cvV/PmzTVu3Dht27bthp8NAAAlwUg3AAB/sKpVq6pTp07q1KmTJkyYoCeffFKvvPKKYmJiit3uzJkzOnXqlIKDgyVJjRo1UlxcnHJycuTk5CTp6mXh1atX1/Hjx4vdV+PGjZWenq6UlJRiR7unTZumGTNmaObMmWratKnc3Nw0cuRIZWdnF7v/vLw8devWTW+99VaBdbVq1dJPP/0kqeCXBtd+SVDSLwxcXV2L/QIhLy9PkrRu3TrVrl3bbp2Li4skqWvXrvrll1+0bt06ffnll+rYsaOGDh2qd955p7jTBADghhjpBgCgnIWGhiozM/OGdbNmzZKDg4Nt/unHH39cFy5c0Jw5c0p9zMcee0zOzs6aOnVqoevPnTsnSfrvf/+rhx9+WE888YTuuOMO1a9f3xaY8zk7Oys3N9duWYsWLbR//37Vq1dPDRs2tHu5ubmpQYMGcnJy0q5du2zbZGRk2O07NDRUV65csd0/Ll394uHQoUNq0qRJic81NDRULi4uSkpKKtBLYGCgra5mzZqKiYnRv/71L82cOVMffPBBiY8BAEBRGOkGAOAPcubMGfXq1UsDBw5Us2bN5OHhoT179mjq1Kl6+OGH7WrPnz+v1NRU5eTk6OjRo/rXv/6lDz/8UFOmTFHDhg0lSRERERo9erRGjx6tX375RT179rQ9xGz+/PmyWCxycCj8+/XAwEDNmDFDzz33nDIyMvTXv/5V9erV0/Hjx7V48WK5u7tr2rRpatiwoVasWKFt27bJy8tL06dPV2pqql3orVevnnbu3Kljx47J3d1d3t7eGjp0qObNm6fHH39cY8eOlY+Pj37++WctX75c8+bNk4eHh/r376+xY8fa7lV/5ZVX5ODgYBu1btSokR5++GENHjxYc+fOlYeHh1544QXVrl27wOdVHA8PD40ZM0Z/+9vflJeXp3bt2ikjI0Pbtm2Tu7u7+vfvrwkTJig8PFy33367srKy9Pnnn5cq2AMAUBRGugEA+IO4u7urVatWmjFjhu69916FhYXp5Zdf1uDBgzV79my72gkTJqhWrVpq2LChoqOjlZ6erq+++krPP/+8Xd0777yjpUuXau/evYqKilKjRo3Uq1cv5eXlafv27fL09CyynyFDhmjjxo369ddf9cgjj+i2227Tk08+KU9PT9vD015++WW1aNFCkZGR6tChg/z9/W0j7fnGjBkjR0dHhYaGqmbNmkpKSlJAQIC+/fZb5ebmKjIyUmFhYRoxYoSsVqvti4Dp06crIiJCUVFReuCBB9S2bVs1adJEVatWte174cKFCg8PV1RUlCIiImQYhtavX2+7nL6kXnvtNU2YMEFTpkxRkyZNFBkZqbVr19ou1Xd2dtb48ePVrFkz3XvvvXJ0dNTy5ctLdQwAAApjMYq6YQoAAOAPlJmZqdq1a2vatGkaNGhQebcDAECZ4PJyAABQLvbu3asDBw7o7rvvVnp6ul599VVJKtWl4wAA/NkRugEAQLl55513dPDgQTk7Oys8PFz//e9/5ePjU95tAQBQZri8HAAAAAAAk/AgNQAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABM8v8Ad+2zOKVaprAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply the mapping function to the dataset\n",
+    "Climate_data['Climate Action'] = Climate_data.apply(map_road_to_sdg, axis= 1)\n",
+    "\n",
+    "# Create a bar chart to visualize the mapped data\n",
+    "sdg_counts = Climate_data['Climate Action'].value_counts()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sdg_counts.plot(kind='bar', color=['red', 'green'])\n",
+    "plt.title('MAPPING CLIMATE TO SDG 13')\n",
+    "plt.xlabel('SDG Categories')\n",
+    "plt.ylabel('Number of Features')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdcc0daf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Task 2/SDG_Prototype_Mapping_documentation.md b/Task 2/SDG_Prototype_Mapping_documentation.md
new file mode 100644
index 000000000..ed946adc4
--- /dev/null
+++ b/Task 2/SDG_Prototype_Mapping_documentation.md	
@@ -0,0 +1,97 @@
+# MAPPING PROTOTYPE FOR SUSTAINABLE DEVELOPMENT GROWTH(SDG)
+
+
+
+This is the prototype for mapping the Sustainable Development Goals (SDG) in the administrative dataset.
+
+
+The Sustainable Development Goal in alignment with the administrative dataset category that was chosen gare No 11 and No 13
+
+
+#### SDG Goal 11:
+
+
+Sustainable Cities and Communities- the goal of this SDG is to make cities and human settlements inclusive, safe, resilient and sustainable.
+
+
+#### SDG Goal 13:
+Climate Action - the goal of this SDG is to take urgent action to combat climate and its impacts.
+
+
+
+
+## ADMINISTRATIVE DATASET
+
+
+The administrative dataset is a geospatial data which contains the global road data, protected area data, and boundary datasets.
+
+
+### GLOBAL ROAD DATA
+
+
+#### Dataset Attribute:
+The Global Road dataset has the following features: Geometry, Object Id, Onme, Fclass, Srftpe, Isseasonal, Cuntprac, Gdwthrprac, Sum_Length_Km. The 'FClass' feature ranges from 0, 1, 2,3 ,4,5 and 6 and it contains types of the road such as unspecified-highway, Primary, Secondary, Tertiary, Local/Urban and Trail respectively.
+
+
+#### Mapping Explanation:
+The attributes within the 'FClass' column was mapped with SDG 11 based on the substantial impact of these attributes on the achievement of the Sustainable Development Goal. SDG 11, which focuses on 'Sustainable Cities and Communities,'
+
+
+SDG 11 aims to make cities and human settlements inclusive, safe, resilient, and sustainable. Transportation infrastructure is crucial for development, and it directly ties to the goal of creating sustainable and accessible cities. The different types of roads can play a significant role in achieving this goal.
+
+
+### CLIMATE DATA
+
+
+#### Dataset Attribute:
+
+
+The Climate dataset has the following columns: Geometry and class_names.
+
+
+#### Mapping Explanation:
+
+
+The values in the class_names of the climate dataset represent the different climate zones: Temperate Moist, Warm Temperate Dry, Cool Temperate Moist, Cool Temperate Dry, Polar Moist, Polar Dry, Boreal Moist, Boreal Dry and Tropical Montane and are mapped to SDG 13- Climate Action. All of the values in the class_name attributes were mapped because of their relevance to the SDG.
+
+
+The SDG No 13 aims at making efforts to reduce greenhouse gas emissions, enhance climate resilience, and promote climate education and awareness. The connection to SDG 13 signifies the relevance of climate data in the pursuit of sustainable and responsible actions to mitigate climate change and promote environmental resilience.
+
+
+### PROTECTED AREA DATASET
+
+
+#### Data Attributes:
+
+
+The protected area dataset columns includes: WDPAID, WDPA_PID, PA_DEF, NAME, ORIG_NAME, DESIG, DESIG_ENG, DESIG_TYPE, IUCN_CAT, INT_CRIT, MARINE,REP_M_AREA, GIS_M_AREA, REP_AREA, GIS_AREA, NO_TAKE,NO_TK_AREA, STATUS, STATUS_YR, GOV_TYPE, OWN_TYPE,MANG_AUTH, MANG_PLAN, VERIF, METADATAID, SUB_LOC,PARENT_ISO, ISO3, SUPP_INFO, CONS_OBJ, geometry.
+
+
+**STATUS**: This is the attribute mapped to the SDG. It describes the status of protected areas or specific sites of cultural or natural significance.
+   
+
+
+#### Mapping Explanation:
+
+
+The 'STATUS' attribute in the protected area dataset provides information about the status of various protected areas or sites of cultural or natural significance. Mapping this attribute to SDG 11, focuses on Sustainable Cities and Communities.
+
+
+The Designated, Established and the Inscribed status indicates that the area has been officially designated for protection. This SDG 11 aligns with the goal of ensuring sustainable urbanisation and safe, inclusive, resilient cities and communities. 
+
+
+#### Reference 
+
+
+[Sustainable Development Goal](https://www.fao.org/3/CA3121EN/ca3121en.pdf)
+
+
+
+
+```python
+
+
+```
+
+
+
diff --git a/Task 3/Moja_Project_Task_3.ipynb b/Task 3/Moja_Project_Task_3.ipynb
new file mode 100644
index 000000000..c9583f9cd
--- /dev/null
+++ b/Task 3/Moja_Project_Task_3.ipynb	
@@ -0,0 +1,2504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4fc0a9cc",
+   "metadata": {},
+   "source": [
+    "# CHARTING THE PATH TO SUSTAINABILITY: Integrating Land Sector Data with SDGs "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e08faf2",
+   "metadata": {},
+   "source": [
+    "### TASK 3 - GEOSPATIAL ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cd882121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing libraries \n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import geopandas as gpd\n",
+    "%matplotlib inline\n",
+    "import json\n",
+    "from esda.moran import Moran\n",
+    "import libpysal as lps\n",
+    "import splot\n",
+    "from splot.esda import moran_scatterplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b4df2354",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load data\n",
+    "with open (\"C:/Users/adere/Downloads/NGA-20231026T051526Z-001/NGA/NGA_AL2_Nigeria.json\") as file:\n",
+    "    nga = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0cd53ebe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nga_data = gpd.GeoDataFrame.from_features(nga['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe92f2af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open (\"C:/Users/adere/Downloads/KoppenGeigerClimateShifts/2001-2025-A1FI.geojson\") as file:\n",
+    "    climate = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "66e46779",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Climate = gpd.GeoDataFrame.from_features(climate['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37c0d7cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clip \"nga data\" to the extent of the climate data\n",
+    "nga_data_clipped = gpd.overlay(nga_data, Climate, how='intersection')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bc9b0a16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>ISO3166_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>enname</th>\n",
+       "      <th>locname</th>\n",
+       "      <th>offname</th>\n",
+       "      <th>boundary</th>\n",
+       "      <th>adminlevel</th>\n",
+       "      <th>wikidata</th>\n",
+       "      <th>wikimedia</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>GRIDCODE</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1548</td>\n",
+       "      <td>1548</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  country ISO3166_2     name   enname  locname offname        boundary  \\\n",
+       "0     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "1     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "\n",
+       "   adminlevel wikidata   wikimedia            timestamp  OBJECTID    ID  \\\n",
+       "0           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1548  1548   \n",
+       "1           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1565  1565   \n",
+       "\n",
+       "   GRIDCODE  Shape_Length  Shape_Area  \\\n",
+       "0        12           4.0        0.75   \n",
+       "1        11           5.0        1.00   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....  \n",
+       "1  MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nga_data_clipped.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "66d5fac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dictionary to map integers to climate codes\n",
+    "climate_mapping = {\n",
+    "    11: \"Af\",\n",
+    "    12: \"Am\",\n",
+    "    13: \"As\",\n",
+    "    14: \"Aw\",\n",
+    "    21: \"BWk\",\n",
+    "    22: \"BWh\",\n",
+    "    26: \"BSk\",\n",
+    "    27: \"BSh\",\n",
+    "    31: \"Cfa\",\n",
+    "    32: \"Cfb\",\n",
+    "    33: \"Cfc\",\n",
+    "    34: \"Csa\",\n",
+    "    35: \"Csb\",\n",
+    "    36: \"Csc\",\n",
+    "    37: \"Cwa\",\n",
+    "    38: \"Cwb\",\n",
+    "    39: \"Cwc\",\n",
+    "    41: \"Dfa\",\n",
+    "    42: \"Dfb\",\n",
+    "    43: \"Dfc\",\n",
+    "    44: \"Dfd\",\n",
+    "    45: \"Dsa\",\n",
+    "    46: \"Dsb\",\n",
+    "    47: \"Dsc\",\n",
+    "    48: \"Dsd\",\n",
+    "    49: \"Dwa\",\n",
+    "    50: \"Dwb\",\n",
+    "    51: \"Dwc\",\n",
+    "    52: \"Dwd\",\n",
+    "    61: \"EF\",\n",
+    "    62: \"ET\"\n",
+    "}\n",
+    "\n",
+    "# Use the \"GRIDCODE\" column in your DataFrame to map to climate code\n",
+    "nga_data_clipped['Climate Code'] = nga_data_clipped['GRIDCODE'].map(climate_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "71af92b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>ISO3166_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>enname</th>\n",
+       "      <th>locname</th>\n",
+       "      <th>offname</th>\n",
+       "      <th>boundary</th>\n",
+       "      <th>adminlevel</th>\n",
+       "      <th>wikidata</th>\n",
+       "      <th>wikimedia</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>GRIDCODE</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>Climate Code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1548</td>\n",
+       "      <td>1548</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....</td>\n",
+       "      <td>Am</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...</td>\n",
+       "      <td>Af</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  country ISO3166_2     name   enname  locname offname        boundary  \\\n",
+       "0     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "1     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "\n",
+       "   adminlevel wikidata   wikimedia            timestamp  OBJECTID    ID  \\\n",
+       "0           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1548  1548   \n",
+       "1           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1565  1565   \n",
+       "\n",
+       "   GRIDCODE  Shape_Length  Shape_Area  \\\n",
+       "0        12           4.0        0.75   \n",
+       "1        11           5.0        1.00   \n",
+       "\n",
+       "                                            geometry Climate Code  \n",
+       "0  POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....           Am  \n",
+       "1  MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...           Af  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nga_data_clipped.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5eb1a236",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open (\"C:/Users/adere/Downloads/KoppenGeigerClimateShifts/2001-2025-A2.geojson\") as file:\n",
+    "    climate1 = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9ad475a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "climate_1 =gpd.GeoDataFrame.from_features(climate1['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2badcab2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clip \"nga data\" to the extent of the climate data\n",
+    "nga_data1_clipped = gpd.overlay(nga_data, climate_1, how='intersection')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7457e8c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>ISO3166_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>enname</th>\n",
+       "      <th>locname</th>\n",
+       "      <th>offname</th>\n",
+       "      <th>boundary</th>\n",
+       "      <th>adminlevel</th>\n",
+       "      <th>wikidata</th>\n",
+       "      <th>wikimedia</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>GRIDCODE</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1555</td>\n",
+       "      <td>1555.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1575</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  country ISO3166_2     name   enname  locname offname        boundary  \\\n",
+       "0     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "1     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "\n",
+       "   adminlevel wikidata   wikimedia            timestamp  OBJECTID      ID  \\\n",
+       "0           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1555  1555.0   \n",
+       "1           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1575  1575.0   \n",
+       "\n",
+       "   GRIDCODE  Shape_Length  Shape_Area  \\\n",
+       "0      12.0           4.0        0.75   \n",
+       "1      11.0           5.0        1.00   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....  \n",
+       "1  MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nga_data1_clipped.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d918848d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dictionary to map integers to climate codes\n",
+    "climate_mapping = {\n",
+    "    11: \"Af\",\n",
+    "    12: \"Am\",\n",
+    "    13: \"As\",\n",
+    "    14: \"Aw\",\n",
+    "    21: \"BWk\",\n",
+    "    22: \"BWh\",\n",
+    "    26: \"BSk\",\n",
+    "    27: \"BSh\",\n",
+    "    31: \"Cfa\",\n",
+    "    32: \"Cfb\",\n",
+    "    33: \"Cfc\",\n",
+    "    34: \"Csa\",\n",
+    "    35: \"Csb\",\n",
+    "    36: \"Csc\",\n",
+    "    37: \"Cwa\",\n",
+    "    38: \"Cwb\",\n",
+    "    39: \"Cwc\",\n",
+    "    41: \"Dfa\",\n",
+    "    42: \"Dfb\",\n",
+    "    43: \"Dfc\",\n",
+    "    44: \"Dfd\",\n",
+    "    45: \"Dsa\",\n",
+    "    46: \"Dsb\",\n",
+    "    47: \"Dsc\",\n",
+    "    48: \"Dsd\",\n",
+    "    49: \"Dwa\",\n",
+    "    50: \"Dwb\",\n",
+    "    51: \"Dwc\",\n",
+    "    52: \"Dwd\",\n",
+    "    61: \"EF\",\n",
+    "    62: \"ET\"\n",
+    "}\n",
+    "\n",
+    "# Use the \"GRIDCODE\" column in your DataFrame to map to climate code\n",
+    "nga_data1_clipped['Climate Code'] = nga_data1_clipped['GRIDCODE'].map(climate_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "918f0183",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure with two subplots\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Plot the first GeoDataFrame based on the 'Climate Code'\n",
+    "nga_data_clipped.plot(column='Climate Code', cmap='coolwarm', ax=axes[0])\n",
+    "axes[0].set_title('Climate 2001-2025 A1F1')\n",
+    "\n",
+    "# Plot the second GeoDataFrame based on the 'Climate Code'\n",
+    "nga_data1_clipped.plot(column='Climate Code', cmap='coolwarm', legend=True, ax=axes[1])\n",
+    "axes[1].set_title('Climate 2001-2025 A2')\n",
+    "\n",
+    "# Create a legend for the second subplot (axes[1]) and place it in the lower right corner\n",
+    "legend = axes[1].get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 0))\n",
+    "\n",
+    "# Add a title for the entire figure\n",
+    "fig.suptitle('Climate Comparison')\n",
+    "\n",
+    "# Show the plots\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbf09586",
+   "metadata": {},
+   "source": [
+    "### Observation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0daebb8",
+   "metadata": {},
+   "source": [
+    "### Left Subplot ('Climate 2001-2025 A1F1'):\n",
+    "Observation in the climate distribution for the years 2001-2025.\n",
+    "\n",
+    "**Af** - Equatorial Fully Humid: This climate type primarily remains in the southern coastal regions of Nigeria. It is characterized by consistently high temperatures and abundant rainfall throughout the year.\n",
+    "\n",
+    "**Am** - Equatorial Monsoonal: 'Am' climate regions also persist in the southern parts of Nigeria, where they experience high temperatures with distinct wet and dry seasons.\n",
+    "\n",
+    "**Aw** - Equatorial Winter Dry: 'Aw' climate regions can be seen in the central parts of Nigeria, representing areas with a brief dry season during the winter months.\n",
+    "\n",
+    "**Bsh** - Steppe Climate - Hot Arid: The 'Bsh' climate regions appear in the northern part of Nigeria, where hot and arid conditions with limited rainfall prevail.\n",
+    "\n",
+    "**BWh** - Desert Climate - Hot Arid: The 'BWh' climate regions are visible in the far north, particularly around the Sahara Desert, where extremely hot and arid conditions with minimal rainfall dominate.\n",
+    "\n",
+    "### Right Subplot ('Climate 2001-2025 A2'):\n",
+    "Observation in the climate distribution for the years 2001-2025.\n",
+    "\n",
+    "**Af** - Equatorial Fully Humid: 'Af' climate regions have shifted slightly. These changes may indicate an influence on the Equatorial Fully Humid climate, potentially affecting areas with year-round rainfall.\n",
+    "\n",
+    "**Am** - Equatorial Monsoonal: 'Am' regions have not experienced significant shifts or variations, thereby not affecting the wet and dry seasons in the southern and central parts of Nigeria.\n",
+    "\n",
+    "**Aw** - Equatorial Winter Dry: 'Aw' regions have undergone changes, which may impact areas with a brief dry season during the winter.\n",
+    "\n",
+    "**Bsh** - Steppe Climate - Hot Arid: The 'Bsh' climate regions have experienced changes, which affect hot and arid conditions in the northern regions.\n",
+    "\n",
+    "**BW** - Desert Climate - Hot Arid: 'BW' regions show slight changes, potentially impacting the extremely hot and arid conditions in the far north.\n",
+    "\n",
+    "### Comparison and Implications:\n",
+    "\n",
+    "Changes in climate boundaries or the expansion of certain climate types can lead to alterations in local weather conditions. These shifts in climate types can have significant implications for agriculture, water resource management, and urban planning in the affected regions.\n",
+    "\n",
+    "Changes in climate patterns, as described in the observations, can have significant impacts on various economic activities in Nigeria. Here's how these changes can affect economic activities:\n",
+    "\n",
+    "**Agriculture**:\n",
+    "\n",
+    "Climate Impact: Changes in climate types, such as shifts in the distribution of 'Af,' 'Am,' and 'Aw' regions, can influence the timing and amount of rainfall. This can impact crop growth, suitability, and yields.\n",
+    "\n",
+    "Economic Impact: Agriculture is a major economic sector in Nigeria. Variations in climate can lead to altered planting seasons, reduced crop yields, and changes in crop choices. This can affect food production, income for farmers, and food security.\n",
+    "\n",
+    "**Water Resource Management**:\n",
+    "\n",
+    "Climate Impact: Climate changes can lead to shifts in water availability, with potential effects on water resources.\n",
+    "\n",
+    "Economic Impact: Water is essential for various economic activities, including agriculture, industry, and domestic use. Changes in water availability can impact water management practices, potentially leading to increased competition for limited water resources, which can increase costs for industries.\n",
+    "\n",
+    "**Urban Planning**:\n",
+    "\n",
+    "Climate Impact: Changes in climate types can influence temperature and rainfall patterns. For example, urban areas with altered climate conditions may experience more frequent extreme weather events.\n",
+    "\n",
+    "Economic Impact: Urban areas are hubs for economic activities and infrastructure development. To adapt to changing climate conditions, cities may need to invest in resilient infrastructure, cooling systems, and public services to ensure residents' well-being. These investments can place a financial burden on local governments and businesses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0ae7efaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open (\"C:/Users/adere/Downloads/KoppenGeigerClimateShifts/2001-2025-B1.geojson\") as file:\n",
+    "    climate2 = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b5936526",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "climate_2 =gpd.GeoDataFrame.from_features(climate2['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "10a01b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clip \"nga data\" to the extent of the climate data\n",
+    "Nga_clipped_data2 = gpd.overlay(nga_data, climate_2, how='intersection')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "13bb81af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dictionary to map integers to climate codes\n",
+    "climate_mapping = {\n",
+    "    11: \"Af\",\n",
+    "    12: \"Am\",\n",
+    "    13: \"As\",\n",
+    "    14: \"Aw\",\n",
+    "    21: \"BWk\",\n",
+    "    22: \"BWh\",\n",
+    "    26: \"BSk\",\n",
+    "    27: \"BSh\",\n",
+    "    31: \"Cfa\",\n",
+    "    32: \"Cfb\",\n",
+    "    33: \"Cfc\",\n",
+    "    34: \"Csa\",\n",
+    "    35: \"Csb\",\n",
+    "    36: \"Csc\",\n",
+    "    37: \"Cwa\",\n",
+    "    38: \"Cwb\",\n",
+    "    39: \"Cwc\",\n",
+    "    41: \"Dfa\",\n",
+    "    42: \"Dfb\",\n",
+    "    43: \"Dfc\",\n",
+    "    44: \"Dfd\",\n",
+    "    45: \"Dsa\",\n",
+    "    46: \"Dsb\",\n",
+    "    47: \"Dsc\",\n",
+    "    48: \"Dsd\",\n",
+    "    49: \"Dwa\",\n",
+    "    50: \"Dwb\",\n",
+    "    51: \"Dwc\",\n",
+    "    52: \"Dwd\",\n",
+    "    61: \"EF\",\n",
+    "    62: \"ET\"\n",
+    "}\n",
+    "\n",
+    "# Use the \"GRIDCODE\" column in your DataFrame to map to climate code\n",
+    "Nga_clipped_data2['Climate Code'] = Nga_clipped_data2['GRIDCODE'].map(climate_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "57998d38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>ISO3166_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>enname</th>\n",
+       "      <th>locname</th>\n",
+       "      <th>offname</th>\n",
+       "      <th>boundary</th>\n",
+       "      <th>adminlevel</th>\n",
+       "      <th>wikidata</th>\n",
+       "      <th>wikimedia</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>GRIDCODE</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>Climate Code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1555</td>\n",
+       "      <td>1555.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....</td>\n",
+       "      <td>Am</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1572</td>\n",
+       "      <td>1572.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...</td>\n",
+       "      <td>Af</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  country ISO3166_2     name   enname  locname offname        boundary  \\\n",
+       "0     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "1     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "\n",
+       "   adminlevel wikidata   wikimedia            timestamp  OBJECTID      ID  \\\n",
+       "0           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1555  1555.0   \n",
+       "1           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1572  1572.0   \n",
+       "\n",
+       "   GRIDCODE  Shape_Length  Shape_Area  \\\n",
+       "0      12.0           4.0        0.75   \n",
+       "1      11.0           5.0        1.00   \n",
+       "\n",
+       "                                            geometry Climate Code  \n",
+       "0  POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....           Am  \n",
+       "1  MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...           Af  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nga_clipped_data2.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ba8dd0fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open (\"C:/Users/adere/Downloads/KoppenGeigerClimateShifts/2001-2025-B2.geojson\") as file:\n",
+    "    climate3 =json.load(file)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "caafee0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "climate_3 = gpd.GeoDataFrame.from_features(climate3['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bf7155d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clip \"nga data\" to the extent of the climate data\n",
+    "Nga_clipped_data3 = gpd.overlay(nga_data, climate_3, how='intersection')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b70f9f0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dictionary to map integers to climate codes\n",
+    "climate_mapping = {\n",
+    "    11: \"Af\",\n",
+    "    12: \"Am\",\n",
+    "    13: \"As\",\n",
+    "    14: \"Aw\",\n",
+    "    21: \"BWk\",\n",
+    "    22: \"BWh\",\n",
+    "    26: \"BSk\",\n",
+    "    27: \"BSh\",\n",
+    "    31: \"Cfa\",\n",
+    "    32: \"Cfb\",\n",
+    "    33: \"Cfc\",\n",
+    "    34: \"Csa\",\n",
+    "    35: \"Csb\",\n",
+    "    36: \"Csc\",\n",
+    "    37: \"Cwa\",\n",
+    "    38: \"Cwb\",\n",
+    "    39: \"Cwc\",\n",
+    "    41: \"Dfa\",\n",
+    "    42: \"Dfb\",\n",
+    "    43: \"Dfc\",\n",
+    "    44: \"Dfd\",\n",
+    "    45: \"Dsa\",\n",
+    "    46: \"Dsb\",\n",
+    "    47: \"Dsc\",\n",
+    "    48: \"Dsd\",\n",
+    "    49: \"Dwa\",\n",
+    "    50: \"Dwb\",\n",
+    "    51: \"Dwc\",\n",
+    "    52: \"Dwd\",\n",
+    "    61: \"EF\",\n",
+    "    62: \"ET\"\n",
+    "}\n",
+    "\n",
+    "# Use the \"GRIDCODE\" column in your DataFrame to map to climate code\n",
+    "Nga_clipped_data3['Climate Code'] = Nga_clipped_data3['GRIDCODE'].map(climate_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b1f8b813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>ISO3166_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>enname</th>\n",
+       "      <th>locname</th>\n",
+       "      <th>offname</th>\n",
+       "      <th>boundary</th>\n",
+       "      <th>adminlevel</th>\n",
+       "      <th>wikidata</th>\n",
+       "      <th>wikimedia</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>GRIDCODE</th>\n",
+       "      <th>Shape_Length</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>Climate Code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1579</td>\n",
+       "      <td>1579</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....</td>\n",
+       "      <td>Am</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NGA</td>\n",
+       "      <td></td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td></td>\n",
+       "      <td>administrative</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Q1033</td>\n",
+       "      <td>en:Nigeria</td>\n",
+       "      <td>2019-09-18 23:02:02</td>\n",
+       "      <td>1596</td>\n",
+       "      <td>1596</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...</td>\n",
+       "      <td>Af</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  country ISO3166_2     name   enname  locname offname        boundary  \\\n",
+       "0     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "1     NGA            Nigeria  Nigeria  Nigeria          administrative   \n",
+       "\n",
+       "   adminlevel wikidata   wikimedia            timestamp  OBJECTID    ID  \\\n",
+       "0           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1579  1579   \n",
+       "1           2    Q1033  en:Nigeria  2019-09-18 23:02:02      1596  1596   \n",
+       "\n",
+       "   GRIDCODE  Shape_Length  Shape_Area  \\\n",
+       "0        12           4.0        0.75   \n",
+       "1        11           5.0        1.00   \n",
+       "\n",
+       "                                            geometry Climate Code  \n",
+       "0  POLYGON ((3.99934 6.42563, 3.99710 6.42605, 3....           Am  \n",
+       "1  MULTIPOLYGON (((7.40626 4.49956, 7.40648 4.498...           Af  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nga_clipped_data3.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a1d6e0dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure with two subplots\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Plot the first GeoDataFrame based on the 'Climate Code'\n",
+    "Nga_clipped_data2.plot(column='Climate Code', cmap='coolwarm', ax=axes[0])\n",
+    "axes[0].set_title('Climate 2001-2025 B1')\n",
+    "\n",
+    "# Plot the second GeoDataFrame based on the 'Climate Code'\n",
+    "Nga_clipped_data3.plot(column='Climate Code', cmap='coolwarm', legend=True, ax=axes[1])\n",
+    "axes[1].set_title('Climate 2001-2025 B2')\n",
+    "\n",
+    "# Create a legend for the second subplot (axes[1]) and place it in the lower right corner\n",
+    "legend = axes[1].get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 0))\n",
+    "\n",
+    "# Add a title for the entire figure\n",
+    "fig.suptitle('Climate Comparison')\n",
+    "\n",
+    "# Show the plots\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0935a945",
+   "metadata": {},
+   "source": [
+    "### Observation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b1d880",
+   "metadata": {},
+   "source": [
+    "### Left Subplot ('Climate 2001-2025 B1'):\n",
+    "\n",
+    "Observations regarding the climate distribution for the years 2001-2025 in the left subplot are as follows:\n",
+    "\n",
+    "**Bsh** - Steppe Climate - Hot Arid: In the northern part of Nigeria, it notes the prevalence of hot and arid conditions. Steppe climates are characterized by persistently high temperatures and limited rainfall.\n",
+    "\n",
+    "**BWh** - Desert Climate - Hot Arid: This climate type is present in the far northern regions, particularly around the Sahara Desert. It signifies extremely hot and arid conditions with minimal rainfall.\n",
+    "\n",
+    "### Right Subplot ('Climate 2001-2025 B2'):\n",
+    "\n",
+    "Observations in the right subplot, which also covers the climate distribution for the years 2001-2025, are as follows:\n",
+    "\n",
+    "**Bsh** - Steppe Climate - Hot Arid: These regions have experienced changes, potentially affecting the prevalence of hot and arid conditions in the northern areas.\n",
+    "\n",
+    "**BW** - Desert Climate - Hot Arid: These regions have also undergone changes, influencing the presence of extremely hot and arid conditions in the far north.\n",
+    "\n",
+    "### Comparison and Implications:\n",
+    "\n",
+    "Comparing the two subplots, there is a shifts in climate boundaries or alterations in the distribution of specific climate types. These changes may suggest variations in local weather conditions.\n",
+    "\n",
+    "The implications of such alterations in climate types are significant. They can impact various aspects which includes:\n",
+    "\n",
+    "**Agriculture**:\n",
+    "\n",
+    "Climate Impact: Changes in climate distribution, such as a shift towards hot and arid conditions (Bsh and BW climate types), can affect crop suitability and planting seasons. Increased aridity and prolonged droughts can reduce agricultural productivity.\n",
+    "\n",
+    "Economic Impact: Agriculture is a major economic activity in Nigeria. Changes in climate can lead to decreased crop yields, impacting food production and food security. Farmers may face challenges in choosing suitable crops and may need to adopt more resilient agricultural practices, which can be costly.\n",
+    "\n",
+    "**Water Resource Management**:\n",
+    "\n",
+    "Climate Impact: Altered climate patterns can impact water availability, leading to changes in water resources. Hot and arid conditions (Bsh and BW) can result in reduced water availability in rivers and aquifers.\n",
+    "\n",
+    "Economic Impact: Water is essential for various economic activities, including agriculture, industry, and domestic use. Reduced water availability can lead to increased competition for water resources and increased costs for water-intensive industries. Water scarcity can also affect hydropower generation and necessitate investments in alternative water sources and infrastructure.\n",
+    "\n",
+    "**Urban Planning**:\n",
+    "\n",
+    "Climate Impact: Understanding climate changes is essential for urban planning, particularly in areas vulnerable to extreme weather events. Climate changes can lead to increased temperatures and more frequent heatwaves.\n",
+    "\n",
+    "Economic Impact: Urban areas are hubs for economic activities and infrastructure development. To adapt to climate changes, cities may need to invest in heat-resistant infrastructure, cooling systems, and public services to ensure residents' well-being. These investments can place a financial burden on local governments and businesses.\n",
+    "\n",
+    "The presence of 'Bsh' and 'BW' climate codes in both scenarios indicates the persistence of hot and arid conditions. Changes in the distribution of these climate types may have significant implications for water resources, extreme temperature, and arid land ecosystems in Nigeria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "995cd8e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Shape_Length   Shape_Area\n",
+      "count      6.000000     6.000000\n",
+      "mean     221.333333   396.625000\n",
+      "std      213.926779   565.804753\n",
+      "min        4.000000     0.750000\n",
+      "25%       33.750000    16.250000\n",
+      "50%      191.000000   134.250000\n",
+      "75%      403.000000   565.562500\n",
+      "max      487.000000  1424.250000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate summary statistics for 'Shape_Length' and 'Shape_Area'\n",
+    "summary_stats = nga_data_clipped[['Shape_Length', 'Shape_Area']].describe()\n",
+    "print(summary_stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9b122172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "# Create box plots for 'Shape_Length' and 'Shape_Area'\n",
+    "nga_data_clipped[['Shape_Length', 'Shape_Area']].boxplot(ax=ax)\n",
+    "\n",
+    "# Set the y-axis label\n",
+    "ax.set_ylabel('Length and Area')\n",
+    "\n",
+    "# Set the title\n",
+    "ax.set_title('Summary Statistics for Shape Length and Area 2001-2025 A1FI')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70306f4c",
+   "metadata": {},
+   "source": [
+    "###  Summary of findings:\n",
+    "\n",
+    "**Shape_Length Box Plot**:\n",
+    "\n",
+    "Median: The median 'Shape_Length' is around the center of the interquartile range (IQR), indicating that roughly half of the geographic features have a 'Shape_Length' shorter than this value.\n",
+    "Spread: The IQR is relatively narrow, suggesting that the 'Shape_Length' values are concentrated within a limited range. Most of the features have similar lengths.\n",
+    "Outliers: There are a few outliers with 'Shape_Length' values significantly longer than the majority of the features. These outliers might represent exceptional or unique geographic elements.\n",
+    "\n",
+    "**Shape_Area Box Plot**:\n",
+    "\n",
+    "Median: The median 'Shape_Area' is positioned near the center of the IQR, indicating that roughly half of the features have 'Shape_Area' values smaller than this value.\n",
+    "Spread: The IQR is wider compared to 'Shape_Length,' suggesting that 'Shape_Area' values are more variable. There is a broader range of feature sizes.\n",
+    "Outliers: A few outliers are present with 'Shape_Area' values significantly larger than the majority of the features. These outliers may represent unusually large geographic areas.\n",
+    "\n",
+    "### Comparison:\n",
+    "'Shape_Length' tends to be more consistent in length, with a narrow IQR, while 'Shape_Area' varies more in size, with a wider IQR. This indicates that geographic features in the dataset differ more in terms of their areas than their lengths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "66248925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Shape_Length   Shape_Area\n",
+      "count      6.000000     6.000000\n",
+      "mean     164.284040   379.425608\n",
+      "std      157.319810   557.791131\n",
+      "min        4.000000     0.750000\n",
+      "25%       27.882580    15.991060\n",
+      "50%      155.675014   135.040188\n",
+      "75%      253.383270   484.735165\n",
+      "max      399.116421  1428.115098\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate summary statistics for 'Shape_Length' and 'Shape_Area'\n",
+    "summary_stats_1 = nga_data1_clipped[['Shape_Length', 'Shape_Area']].describe()\n",
+    "print(summary_stats_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5ed18f05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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//MNIcgk8RblQAD506JDx9/cv9Bo+efKkiYmJMf369XO2FbyG/vOf/7j0vemmm0zdunWd0wV/zHz22Wcu/YYMGeISgI258CUQ0dHRLpeppKWlmTJlyphJkyYVv9FFuNBz4c56WrVqZWJjY132/8zMTBMZGel2AB40aJCRZPbs2WOM+b/9/umnn3bpV/B6vueee1zaPXm+inoMsrKyTEhIiPnnP//pVr1/tmLFClOmTBnzyCOPuLTXqVOnyNfB4cOHnX/UGvN/Ye69994r1HfixIlGkjl8+HCR6/ZWAL7rrruMv7+/+frrr51tl1KXMcbMmTPHSCr0mH722WfmySefNB9//LFZu3atmTVrlqlataoJCQkx27dvd6ted/eXojz55JNu/Q6wAi6BsLioqCh9+eWX2rJli/7+97/r5ptv1g8//KCxY8eqcePG+u233y567B49erhM169fX5LUvXv3Qu1Fvf120003ubzddb7lJenQoUPOth9//FH9+/dXTEyM/Pz8ZLfblZCQIOmPT/mf69Zbby3U9vDDDys9PV3vv/++JCk/P1+vvPKKunfvrho1ahS90f9frVq19O2332rt2rWaMGGCOnfurC1btuihhx5S69atz3v5xJ/Nnj1bzZs3V9myZeXv7y+73a7PP/+8yG1wR+3atVW+fHmNGTNGs2fP1u7duy9qnAI//PCD9u/fr8GDB6ts2bLF9mvZsqXmzZun559/Xhs3bnS55MVTe/fu1eHDhzVgwACX/SM0NFS33nqrNm7cqOzsbJdlinp+ixIYGKglS5Zo9+7dmj59uu644w4dPXpUL7zwgurXr6+9e/e69I+JiXG+/VugSZMmhfbn999/XzfeeKNCQ0Odz+PcuXOLfB47deqk6Oho57Sfn59uv/127du3z/kW87Jly9SxY0fFxsbq7Nmzzp9u3bpJktauXevW9hZnxYoVOnv2rO655x6X8cuWLauEhIRCl8DYbDb17NnzvI/D2rVrFRYWpq5du7r0u/POOz2ur2PHjgoLC3NOR0dHq1KlSm69je/Jc3Gh9Zw6dUpbtmxRnz59XPb/sLCwQo9HcbKysvSf//xHbdq0Ub169SRJCQkJqlWrlubNm+dyOU2Bc/dnT56vrKwsjRkzRrVr15a/v7/8/f0VGhqqU6dOeXxc2bZtm/r166cbbrhBkyZNKjT/fHfBOHeeJ30vxBjj8jic77Kgp59+Wu+8846mT5+uFi1aeLTu4uZ99tlnGjZsmPr27avhw4e7zOvatauef/559ejRQ+3bt9ewYcP05Zdfymaz6Zlnnrngtl3M/lLg3//+t1544QWNGjVKN9988wXXdbUjAEOSdN1112nMmDF6//33dfjwYT3yyCM6ePCgpkyZctFjRkZGukwHBAQU215UIPRkeUnOMbKystSuXTtt2rRJzz//vNasWaMtW7Zo8eLFkqTTp0+7LB8cHOxyjVeBZs2aqV27ds7rEZctW6aDBw+6fYuiMmXKqH379nrmmWf00Ucf6fDhw7r99tu1devWIq/XOte0adP0t7/9Ta1atdKiRYu0ceNGbdmyRV27di20De6KiIjQ2rVr1bRpUz3xxBNq2LChYmNjNW7cuIsKpUePHpUkVa1a9bz9Fi5cqIEDB+rf//63WrdurcjISN1zzz0e3/ZH+uM6XUlFXmMdGxur/Px8HT9+3KW9uOuxi1O/fn0lJydr/vz5OnTokKZNm6Zjx47p6aefdukXFRVVaNnAwECX52fx4sXq16+fqlSpovnz52vDhg3asmWLBg0aVOR+HxMTU2xbwbb/+uuv+vjjj2W3211+GjZsKEmX9IdrwfiSdP311xdax8KFCwuNHxwcXOgPoMDAQJftO3bsmEuwL1BU24W487gXxdPn4kLrOX78uPLz88/7nF3IwoULlZWVpX79+unEiRM6ceKEMjIy1K9fP/38889KTU0ttMy5+7Mnz1f//v01a9Ys3XfffVqxYoU2b96sLVu2qGLFih4dV7755hslJiaqTp06+vTTTxUYGOgyPyoqyrm//lnB9bAFx/GCx7i4vjabTeXKlXO7LumPP7bOfRyKuhXkhAkT9Pzzz+uFF14odFy/2LpWrFihPn36KDExUe+8845b4b1GjRpq27atNm7ceMG+F7O/SNIbb7yhIUOG6IEHHtCLL754wfVYQeFPPsHy7Ha7xo0bp+nTp2vnzp3O9sDAwEIfapGKPkCUpFWrVunw4cNas2aN86yvJJ04caLI/uc7QI0YMUK33Xabtm3bplmzZumaa65RYmLiRdUVEhKisWPHauHChS6Pa3Hmz5+vDh066JVXXnFpP3ny5EWtv0Djxo21YMECGWP03Xffad68eXr22WcVFBSkxx9/3KOxCj6A9+cPvxSlQoUKmjFjhmbMmKFDhw7po48+0uOPP6709HQtX77co3UW/GI6cuRIoXmHDx9WmTJlVL58eZd2T88gnbvsI488omeffdat5+1c8+fPV3x8vBYuXOhSR1GvJUlF/lFQ0Faw7RUqVFCTJk30wgsvFDlGbGysx3X+WYUKFSRJH3zwgapXr35JYxWIiorS5s2bC7VfzB9BF8vT5+JCypcvX+z9W93drrlz50qSkpOTXT44+uf5Xbp0cWk7d3929/nKyMjQsmXLNG7cOJfXek5Ojkcf1Prmm2/UuXNnVa9eXSkpKYqIiCjUp3Hjxnrvvfd09uxZlw9Z79ixQ5Kc96GuVauWgoKCnO1/tmPHDtWuXfu87y4VpUWLFtqyZYtL27mviQkTJmj8+PEaP368nnjiiUJjXExdK1asUO/evZWQkKBFixY5T864wxjj1gf8LmZ/eeONN3Tfffdp4MCBmj179iUdD68mnAG2uKJChPR/lwn8+aBRo0YNfffddy79Vq1apaysLN8VeBEKXtznnpF49dVXPR6r4AtCRo0apZUrV2ro0KFuHTw8eVyLO3Nls9kKbcN3331X6E4HBX08PStss9l07bXXavr06SpXrpy2bdt2wZrOdc0116hWrVp6/fXX3Q4R1apV00MPPaTExESXdbqrbt26qlKlit59910ZY5ztp06d0qJFi5x3hrgYxT1vhw8fVmZm5kUFS5vNpoCAAJf9Ji0trcg7D0jS559/7jyjJ0l5eXlauHChatWq5TzT3qNHD+3cuVO1atXSddddV+jnUgNwly5d5O/vr/379xc5/nXXXefxmAkJCTp58qQ+++wzl/ai7nBxsfv0hXj6XFxISEiIWrZsqcWLF7ucQT558qQ+/vjjCy6/Z88ebdiwQbfeeqtWr15d6KdTp0768MMPL3iSwd3ny2azyRhT6Ljy73//u8gvsijK9u3b1blzZ1WtWlWpqamF/tgscMsttygrK0uLFi1yaX/zzTcVGxurVq1aSZL8/f3Vs2dPLV682OWP+0OHDmn16tXq06ePW3X9WVhYWKHt/3MYfe655zR+/Hg99dRTGjduXJFjeFpXSkqKevfurbZt22rp0qWFHuPzOXDggL766ivdcMMN5+13MfvLvHnzdN999+nuu+/Wv//9b8Lvn3AG2OK6dOmiqlWrqmfPnqpXr57y8/O1fft2TZ06VaGhoXr44YedfQcMGKCnn35azzzzjBISErR7927NmjWryL/+S1KbNm1Uvnx5Pfjggxo3bpzsdrveeecdffvttx6P5efnp2HDhmnMmDEKCQlxud3b+TRs2FCdOnVSt27dVKtWLZ05c0abNm3S1KlTFR0drcGDBzv7Nm7cWGvWrNHHH3+sypUrKywsTHXr1lWPHj303HPPady4cUpISNDevXv17LPPKj4+3uWatrCwMFWvXl0ffvihOnXqpMjISFWoUKHI65SXLVuml19+Wb1791bNmjVljNHixYt14sQJlzPbxdVUlJdeekk9e/bUDTfcoEceeUTVqlXToUOHtGLFCr3zzjvKyMhQx44d1b9/f9WrV09hYWHasmWLli9fflG/3MqUKaMpU6borrvuUo8ePTRkyBDl5OToxRdf1IkTJwp9054nHnjgAZ04cUK33nqrGjVqJD8/P33//feaPn26ypQpozFjxng8Zo8ePbR48WINHTpUffv21c8//6znnntOlStX1n//+99C/StUqKC//OUvevrppxUSEqKXX35Z33//vUtQfPbZZ5Wamqo2bdpoxIgRqlu3rs6cOaODBw/q008/1ezZsy94WUpmZmaR31RVsWJFJSQk6Nlnn9WTTz6pH3/8UV27dlX58uX166+/avPmzQoJCdGECRM8ehwGDhyo6dOn6+6779bzzz+v2rVr67PPPtOKFSskud7eqnHjxpKkyZMnq1u3bvLz81OTJk08OqNWFE+fC3c899xz6tq1qxITEzVq1Cjl5eVp8uTJCgkJueBZ1YKzeY899liha8mlP4L0559/rvnz57sci89Vo0YNt56v8PBwtW/fXi+++KLzGLF27VrNnTvXrcsM9u7dq86dO0uSXnjhBf33v/91edxq1arlfFeoW7duSkxM1N/+9jdlZmaqdu3aeu+997R8+XLNnz9ffn5+zuUmTJig66+/Xj169NDjjz+uM2fO6JlnnlGFChUKfSPp119/7bycITMzU8YY5358/fXXX/Adi6lTp+qZZ55R165d1b1790KXHfw5hLpb17p169S7d2/FxMToiSeeKPRtlQ0aNHBeZte5c2e1b99eTZo0UXh4uHbs2KEpU6bIZrPpueeeO2/tnu4v77//vgYPHqymTZtqyJAhhd6BadasmUdB/apTgh/AQymwcOFC079/f1OnTh0TGhpq7Ha7qVatmhkwYIDztlgFcnJyzGOPPWbi4uJMUFCQSUhIMNu3by/2LhDn3ui74BP5595qZ+DAgSYkJMQ5XXAXiBdffNGlX1F3BChufevXrzetW7c2wcHBpmLFiua+++4z27ZtK/Rp83PXXZSDBw8aSebBBx88b78/e/XVV02fPn1MzZo1TXBwsAkICDC1atUyDz74oPn5559d+m7fvt3ceOONJjg42Ehyfqo5JyfHjB492lSpUsWULVvWNG/e3CxdutQMHDjQVK9e3WWMlStXmmbNmpnAwEAjyfl8nHsXiO+//97ceeedplatWiYoKMhERESYli1bmnnz5rlVU1F3gTDmj1undevWzURERJjAwEBTq1Yt56fCz5w5Yx588EHTpEkTEx4eboKCgkzdunXNuHHjLvilI8U958YYs3TpUtOqVStTtmxZExISYjp16mS++uorlz7F7XPFWbFihRk0aJBp0KCBiYiIMP7+/qZy5cqmT58+ZsOGDS59i7uTQlHPz9///ndTo0YNExgYaOrXr2/mzJlT5Bcl6P9/ycPLL79satWqZex2u6lXr16RN7k/evSoGTFihImPjzd2u91ERkaaFi1amCeffNJkZWWddzsL7mBR1M+fP1W/dOlS07FjRxMeHm4CAwNN9erVTd++fc3KlStdtreo11BR23fo0CHTp08fExoaasLCwsytt95qPv30UyPJfPjhh85+OTk55r777jMVK1Y0NpvNZR8ueIzOde5xqDiePhfurOejjz4yTZo0MQEBAaZatWrm73//+wW/CCM3N9dUqlTJNG3atNg+Z8+eNVWrVjWNGzc2xlz4SxTceb5++eUXc+utt5ry5cubsLAw07VrV7Nz5063Hr+C9Rf38+djqzF/3IVixIgRJiYmxgQEBJgmTZoUeVcFY/64Q02nTp1McHCwCQ8PN71793bezvHPCu464s76i3K+fb+o58udugqe6+J+/ny8TE5ONg0aNDBhYWHG39/fxMbGmrvvvtvs3bv3vHVfzP5yvsfqz68pq7IZ86f3EAEUMnPmTI0YMUI7d+50fsgI8AWbzaZhw4Zp1qxZJV3KZTNx4kQ99dRTOnTo0AXPWgOAt3AJBFCMb775RgcOHNCzzz6rm2++mfALXKKCYF+vXj05HA6tWrVK//rXv3T33XcTfgFcVgRgoBi33HKL0tLS1K5dO82ePbukywGueMHBwZo+fboOHjyonJwcVatWTWPGjNFTTz1V0qUBsBgugQAAAIClcBs0AAAAWAoBGAAAAJZCAAYAAICl8CE4N+Xn5+vw4cMKCwvjm1QAAABKIWOMTp48qdjY2PN+vTQB2E2HDx9WXFxcSZcBAACAC/j555/Pe3tFArCbwsLCJP3xgBZ8pSHgCw6HQykpKUpKSpLdbi/pcgDgknFcw+WSmZmpuLg4Z24rDgHYTQWXPYSHhxOA4VMOh0PBwcEKDw/nFwWAqwLHNVxuF7pclQ/BAQAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAwGfy8vK0du1affHFF1q7dq3y8vJKuiSAAAwAAHxj8eLFql27thITEzVt2jQlJiaqdu3aWrx4cUmXBosjAAMAAK9bvHix+vbtq8aNG+vLL7/Ue++9py+//FKNGzdW3759CcEoUQRgAADgVXl5eRo1apR69OihpUuXqlWrVgoKClKrVq20dOlS9ejRQ6NHj+ZyCJQYAjAAAPCqL7/8UgcPHtQTTzyhMmVco0aZMmU0duxYHThwQF9++WUJVQirIwADAACvOnLkiCSpUaNGRc4vaC/oB1xuBGAAAOBVlStXliTt3LmzyPkF7QX9gMuNAAwAALyqXbt2qlGjhiZOnKj8/HyXefn5+Zo0aZLi4+PVrl27EqoQVkcABgAAXuXn56epU6dq2bJl6t27tzZu3KjTp09r48aN6t27t5YtW6Z//OMf8vPzK+lSYVElGoC/+OIL9ezZU7GxsbLZbFq6dGmxfYcMGSKbzaYZM2a4tOfk5Gj48OGqUKGCQkJC1KtXL/3yyy8ufY4fP64BAwYoIiJCERERGjBggE6cOOH9DQIAAJKkPn366IMPPtCOHTvUvn173XnnnWrfvr127typDz74QH369CnpEmFhJRqAT506pWuvvVazZs06b7+lS5dq06ZNio2NLTQvOTlZS5Ys0YIFC7Ru3TplZWWpR48eLrdW6d+/v7Zv367ly5dr+fLl2r59uwYMGOD17QEAAP+nT58+2rdvn1JTUzVy5Eilpqbqv//9L+EXJc6/JFferVs3devW7bx9/ve//+mhhx7SihUr1L17d5d5GRkZmjt3rt5++2117txZkjR//nzFxcVp5cqV6tKli/bs2aPly5dr48aNatWqlSRpzpw5at26tfbu3au6dev6ZuMAAID8/PyUkJCgU6dOKSEhgcseUCqUaAC+kPz8fA0YMECPPvqoGjZsWGj+1q1b5XA4lJSU5GyLjY1Vo0aNtH79enXp0kUbNmxQRESEM/xK0g033KCIiAitX7++2ACck5OjnJwc53RmZqYkyeFwyOFweGsTgUIK9i/2MwBXC45ruFzc3cdKdQCePHmy/P39NWLEiCLnp6WlKSAgQOXLl3dpj46OVlpamrNPpUqVCi1bqVIlZ5+iTJo0SRMmTCjUnpKSouDgYE82A7goqampJV0CAHgVxzX4WnZ2tlv9Sm0A3rp1q/75z39q27ZtstlsHi1rjHFZpqjlz+1zrrFjx2rkyJHO6czMTMXFxSkpKUnh4eEe1QN4wuFwKDU1VYmJibLb7SVdDgBcMo5ruFwK3rG/kFIbgL/88kulp6erWrVqzraC7xafMWOGDh48qJiYGOXm5ur48eMuZ4HT09PVpk0bSVJMTIx+/fXXQuMfPXpU0dHRxa4/MDBQgYGBhdrtdjsvXlwW7GsArjYc1+Br7u5fpfY+wAMGDNB3332n7du3O39iY2P16KOPasWKFZKkFi1ayG63u7ylcuTIEe3cudMZgFu3bq2MjAxt3rzZ2WfTpk3KyMhw9gEAAIB1lOgZ4KysLO3bt885feDAAW3fvl2RkZGqVq2aoqKiXPrb7XbFxMQ4P7gWERGhwYMHa9SoUYqKilJkZKRGjx6txo0bO+8KUb9+fXXt2lX333+/Xn31VUnSAw88oB49enAHCAAAAAsq0QD89ddfq2PHjs7pgmtuBw4cqHnz5rk1xvTp0+Xv769+/frp9OnT6tSpk+bNm+dym5V33nlHI0aMcN4tolevXhe89zAAAACuTiUagDt06CBjjNv9Dx48WKitbNmymjlzpmbOnFnscpGRkZo/f/7FlAgAAICrTKm9BhgAAADwBQIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSSjQAf/HFF+rZs6diY2Nls9m0dOlS5zyHw6ExY8aocePGCgkJUWxsrO655x4dPnzYZYycnBwNHz5cFSpUUEhIiHr16qVffvnFpc/x48c1YMAARUREKCIiQgMGDNCJEycuwxYCAACgtCnRAHzq1Clde+21mjVrVqF52dnZ2rZtm55++mlt27ZNixcv1g8//KBevXq59EtOTtaSJUu0YMECrVu3TllZWerRo4fy8vKcffr376/t27dr+fLlWr58ubZv364BAwb4fPsAAABQ+viX5Mq7deumbt26FTkvIiJCqampLm0zZ85Uy5YtdejQIVWrVk0ZGRmaO3eu3n77bXXu3FmSNH/+fMXFxWnlypXq0qWL9uzZo+XLl2vjxo1q1aqVJGnOnDlq3bq19u7dq7p16/p2IwEAAFCqlGgA9lRGRoZsNpvKlSsnSdq6dascDoeSkpKcfWJjY9WoUSOtX79eXbp00YYNGxQREeEMv5J0ww03KCIiQuvXry82AOfk5CgnJ8c5nZmZKemPSzMcDocPtg74Q8H+xX4G4GrBcQ2Xi7v72BUTgM+cOaPHH39c/fv3V3h4uCQpLS1NAQEBKl++vEvf6OhopaWlOftUqlSp0HiVKlVy9inKpEmTNGHChELtKSkpCg4OvpRNAdxy7jsgAHCl47gGX8vOznar3xURgB0Oh+644w7l5+fr5ZdfvmB/Y4xsNptz+s//L67PucaOHauRI0c6pzMzMxUXF6ekpCRnAAd8weFwKDU1VYmJibLb7SVdDgBcMo5ruFwK3rG/kFIfgB0Oh/r166cDBw5o1apVLuEzJiZGubm5On78uMtZ4PT0dLVp08bZ59dffy007tGjRxUdHV3segMDAxUYGFio3W638+LFZcG+BuBqw3ENvubu/lWq7wNcEH7/+9//auXKlYqKinKZ36JFC9ntdpe3VI4cOaKdO3c6A3Dr1q2VkZGhzZs3O/ts2rRJGRkZzj4AAACwjhI9A5yVlaV9+/Y5pw8cOKDt27crMjJSsbGx6tu3r7Zt26Zly5YpLy/Pec1uZGSkAgICFBERocGDB2vUqFGKiopSZGSkRo8ercaNGzvvClG/fn117dpV999/v1599VVJ0gMPPKAePXpwBwgAAAALKtEA/PXXX6tjx47O6YJrbgcOHKjx48fro48+kiQ1bdrUZbnVq1erQ4cOkqTp06fL399f/fr10+nTp9WpUyfNmzdPfn5+zv7vvPOORowY4bxbRK9evYq89zAAAACufiUagDt06CBjTLHzzzevQNmyZTVz5kzNnDmz2D6RkZGaP3/+RdUIAACAq0upvgYYAAAA8DYCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUko0AH/xxRfq2bOnYmNjZbPZtHTpUpf5xhiNHz9esbGxCgoKUocOHbRr1y6XPjk5ORo+fLgqVKigkJAQ9erVS7/88otLn+PHj2vAgAGKiIhQRESEBgwYoBMnTvh46wAAAFAalWgAPnXqlK699lrNmjWryPlTpkzRtGnTNGvWLG3ZskUxMTFKTEzUyZMnnX2Sk5O1ZMkSLViwQOvWrVNWVpZ69OihvLw8Z5/+/ftr+/btWr58uZYvX67t27drwIABPt8+AAAAlD7+Jbnybt26qVu3bkXOM8ZoxowZevLJJ9WnTx9J0ptvvqno6Gi9++67GjJkiDIyMjR37ly9/fbb6ty5syRp/vz5iouL08qVK9WlSxft2bNHy5cv18aNG9WqVStJ0pw5c9S6dWvt3btXdevWvTwbCwAAgFKhRAPw+Rw4cEBpaWlKSkpytgUGBiohIUHr16/XkCFDtHXrVjkcDpc+sbGxatSokdavX68uXbpow4YNioiIcIZfSbrhhhsUERGh9evXFxuAc3JylJOT45zOzMyUJDkcDjkcDm9vLuBUsH+xnwG4WnBcw+Xi7j5WagNwWlqaJCk6OtqlPTo6Wj/99JOzT0BAgMqXL1+oT8HyaWlpqlSpUqHxK1Wq5OxTlEmTJmnChAmF2lNSUhQcHOzZxgAXITU1taRLAACv4rgGX8vOznarX6kNwAVsNpvLtDGmUNu5zu1TVP8LjTN27FiNHDnSOZ2Zmam4uDglJSUpPDzc3fIBjzkcDqWmpioxMVF2u72kywGAS8ZxDZdLwTv2F1JqA3BMTIykP87gVq5c2dmenp7uPCscExOj3NxcHT9+3OUscHp6utq0aePs8+uvvxYa/+jRo4XOLv9ZYGCgAgMDC7Xb7XZevLgs2NcAXG04rsHX3N2/Su19gOPj4xUTE+Pydklubq7Wrl3rDLctWrSQ3W536XPkyBHt3LnT2ad169bKyMjQ5s2bnX02bdqkjIwMZx8AAABYR4meAc7KytK+ffuc0wcOHND27dsVGRmpatWqKTk5WRMnTlSdOnVUp04dTZw4UcHBwerfv78kKSIiQoMHD9aoUaMUFRWlyMhIjR49Wo0bN3beFaJ+/frq2rWr7r//fr366quSpAceeEA9evTgDhAAAAAWVKIB+Ouvv1bHjh2d0wXX3A4cOFDz5s3TY489ptOnT2vo0KE6fvy4WrVqpZSUFIWFhTmXmT59uvz9/dWvXz+dPn1anTp10rx58+Tn5+fs884772jEiBHOu0X06tWr2HsPAwAA4OpmM8aYki7iSpCZmamIiAhlZGTwITj4lMPh0KeffqqbbrqJa+UAXBU4ruFycTevldprgAEAAABfIAADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUvwvZqEtW7bo/fff16FDh5Sbm+syb/HixV4pDAAAAPAFj88AL1iwQDfeeKN2796tJUuWyOFwaPfu3Vq1apUiIiJ8USMAAADgNR4H4IkTJ2r69OlatmyZAgIC9M9//lN79uxRv379VK1aNV/UCAAAAHiNxwF4//796t69uyQpMDBQp06dks1m0yOPPKLXXnvN6wUCAAAA3uRxAI6MjNTJkyclSVWqVNHOnTslSSdOnFB2drZ3qwMAAAC8zOMPwbVr106pqalq3Lix+vXrp4cfflirVq1SamqqOnXq5IsaAQAAAK/xOADPmjVLZ86ckSSNHTtWdrtd69atU58+ffT00097vUAAAADAmzwOwJGRkc7/lylTRo899pgee+wxrxYFAAAA+MpFfRHG/v379dRTT+nOO+9Uenq6JGn58uXatWuXV4sDAAAAvM3jALx27Vo1btxYmzZt0uLFi5WVlSVJ+u677zRu3DivFwgAAAB4k8cB+PHHH9fzzz+v1NRUBQQEONs7duyoDRs2eLU4AAAAwNs8DsA7duzQLbfcUqi9YsWKOnbsmFeKAgAAAHzF4wBcrlw5HTlypFD7N998oypVqnilKAAAAMBXPA7A/fv315gxY5SWliabzab8/Hx99dVXGj16tO655x5f1AgAAAB4jccB+IUXXlC1atVUpUoVZWVlqUGDBmrfvr3atGmjp556yhc1AgAAAF7j0X2AjTE6fPiw5syZo+eee07btm1Tfn6+mjVrpjp16viqRgAAAMBrPA7AderU0a5du1SnTh3VrFnTV3UBAAAAPuHRJRBlypRRnTp1uNsDAAAArlgeXwM8ZcoUPfroo9q5c6cv6gEAAAB8yqNLICTp7rvvVnZ2tq699loFBAQoKCjIZf7vv//uteIAAAAAb/M4AM+YMcMHZQAAAACXh8cBeODAgcXOO3r06CUVAwAAAPiax9cAn8sYo08//VR9+vRR1apVvVETAAAA4DMXHYB//PFHPfXUU6pWrZruuusuBQcHa8GCBd6sDQAAAPA6jy6BOHPmjD744AP9+9//1saNG5WYmKgjR45o+/btatSoka9qBAAAALzG7TPAQ4cOVWxsrF566SXddttt+t///qePP/5YNptNZcpc8pUUAAAAwGXhdnJ97bXX9Le//U0pKSkaNmyYoqKifFmXJOns2bN66qmnFB8fr6CgINWsWVPPPvus8vPznX2MMRo/frxiY2MVFBSkDh06aNeuXS7j5OTkaPjw4apQoYJCQkLUq1cv/fLLLz6vHwAAAKWP2wH4rbfe0ubNm1W5cmXdfvvtWrZsmc6ePevL2jR58mTNnj1bs2bN0p49ezRlyhS9+OKLmjlzprPPlClTNG3aNM2aNUtbtmxRTEyMEhMTdfLkSWef5ORkLVmyRAsWLNC6deuUlZWlHj16KC8vz6f1AwAAoPRxOwD3799fqamp2rlzp+rVq6dhw4apcuXKys/P1+7du31S3IYNG3TzzTere/fuqlGjhvr27aukpCR9/fXXkv44+ztjxgw9+eST6tOnjxo1aqQ333xT2dnZevfddyVJGRkZmjt3rqZOnarOnTurWbNmmj9/vnbs2KGVK1f6pG4AAACUXh7fB7hGjRqaMGGCxo8frxUrVuj111/X3XffreTkZPXp00f/+te/vFZc27ZtNXv2bP3www+65ppr9O2332rdunXOL+M4cOCA0tLSlJSU5FwmMDBQCQkJWr9+vYYMGaKtW7fK4XC49ImNjVWjRo20fv16denSpch15+TkKCcnxzmdmZkpSXI4HHI4HF7bRuBcBfsX+xmAqwXHNVwu7u5jHgfgAjabTV27dlXXrl31+++/66233tIbb7xxscMVacyYMcrIyFC9evXk5+envLw8vfDCC7rzzjslSWlpaZKk6Ohol+Wio6P1008/OfsEBASofPnyhfoULF+USZMmacKECYXaU1JSFBwcfEnbBbgjNTW1pEsAAK/iuAZfy87OdqvfRQfgP4uMjFRycrKSk5O9MZzTwoULNX/+fL377rtq2LChtm/fruTkZMXGxrp8I53NZnNZzhhTqO1cF+ozduxYjRw50jmdmZmpuLg4JSUlKTw8/CK3CLgwh8Oh1NRUJSYmym63l3Q5AHDJOK7hcil4x/5CvBKAfeXRRx/V448/rjvuuEOS1LhxY/3000+aNGmSBg4cqJiYGEl/nOWtXLmyc7n09HTnWeGYmBjl5ubq+PHjLmeB09PT1aZNm2LXHRgYqMDAwELtdrudFy8uC/Y1AFcbjmvwNXf3r1J9A9/s7OxC9xj28/Nz3gYtPj5eMTExLm+p5Obmau3atc5w26JFC9ntdpc+R44c0c6dO88bgAEAAHB1KtVngHv27KkXXnhB1apVU8OGDfXNN99o2rRpGjRokKQ/Ln1ITk7WxIkTVadOHdWpU0cTJ05UcHCw+vfvL0mKiIjQ4MGDNWrUKEVFRSkyMlKjR49W48aN1blz55LcPAAAAJSAUh2AZ86cqaefflpDhw5Venq6YmNjNWTIED3zzDPOPo899phOnz6toUOH6vjx42rVqpVSUlIUFhbm7DN9+nT5+/urX79+On36tDp16qR58+bJz8+vJDYLAAAAJchmjDEX6vTdd9+5PWCTJk0uqaDSKjMzUxEREcrIyOBDcPAph8OhTz/9VDfddBPXygG4KnBcw+Xibl5z6wxw06ZNZbPZ3Lq7At+uBgAAgNLMrQ/BHThwQD/++KMOHDigRYsWKT4+Xi+//LK++eYbffPNN3r55ZdVq1YtLVq0yNf1AgAAAJfErTPA1atXd/7/tttu07/+9S/ddNNNzrYmTZooLi5OTz/9tHr37u31IgEAAABv8fg2aDt27FB8fHyh9vj4eO3evdsrRQEAAAC+4nEArl+/vp5//nmdOXPG2ZaTk6Pnn39e9evX92pxAAAAgLd5fBu02bNnq2fPnoqLi9O1114rSfr2229ls9m0bNkyrxcIAAAAeJPHAbhly5Y6cOCA5s+fr++//17GGN1+++3q37+/QkJCfFEjAAAA4DUX9UUYwcHBeuCBB7xdCwAAAOBzFxWAf/jhB61Zs0bp6enKz893mffnb2kDAAAAShuPA/CcOXP0t7/9TRUqVFBMTIzLF2PYbDYCMAAAAEo1jwPw888/rxdeeEFjxozxRT0AAACAT3l8G7Tjx4/rtttu80UtAAAAgM95HIBvu+02paSk+KIWAAAAwOc8vgSidu3aevrpp7Vx40Y1btxYdrvdZf6IESO8VhwAAADgbR4H4Ndee02hoaFau3at1q5d6zLPZrMRgAEAAFCqeRyADxw44Is6AAAAgMvC42uAAQAAgCvZRX0Rxi+//KKPPvpIhw4dUm5ursu8adOmeaUwAAAAwBc8DsCff/65evXqpfj4eO3du1eNGjXSwYMHZYxR8+bNfVEjAAAA4DUeXwIxduxYjRo1Sjt37lTZsmW1aNEi/fzzz0pISOD+wAAAACj1PA7Ae/bs0cCBAyVJ/v7+On36tEJDQ/Xss89q8uTJXi8QAAAA8CaPA3BISIhycnIkSbGxsdq/f79z3m+//ea9ygAAAAAf8Pga4BtuuEFfffWVGjRooO7du2vUqFHasWOHFi9erBtuuMEXNQIAAABe43EAnjZtmrKysiRJ48ePV1ZWlhYuXKjatWtr+vTpXi8QAAAA8CaPA3DNmjWd/w8ODtbLL7/s1YIAAAAAX+KLMAAAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApHt8FIi8vT/PmzdPnn3+u9PR05efnu8xftWqV14oDAAAAvM3jAPzwww9r3rx56t69uxo1aiSbzeaLugAAAACf8DgAL1iwQP/5z3900003+aIeAAAAwKc8vgY4ICBAtWvX9kUtAAAAgM95HIBHjRqlf/7znzLG+KIeAAAAwKfcugSiT58+LtOrVq3SZ599poYNG8put7vMW7x4sfeqAwAAALzMrQAcERHhMn3LLbf4pBgAAADA19wKwG+88Yav6wAAAAAuC4+vAf7LX/6iEydOFGrPzMzUX/7yF2/UBAAAAPiMxwF4zZo1ys3NLdR+5swZffnll14pCgAAAPAVt+8D/N133zn/v3v3bqWlpTmn8/LytHz5clWpUsW71QEAAABe5nYAbtq0qWw2m2w2W5GXOgQFBWnmzJleLQ4AAADwNrcD8IEDB2SMUc2aNbV582ZVrFjROS8gIECVKlWSn5+fT4oEAAAAvMXtAFy9enVJUn5+vs+KAQAAAHzN7QBc4KOPPiqy3WazqWzZsqpdu7bi4+MvuTAAAADAFzwOwL1795bNZiv0VcgFbTabTW3bttXSpUtVvnx5rxUKAAAAeIPHt0FLTU3V9ddfr9TUVGVkZCgjI0Opqalq2bKlli1bpi+++ELHjh3T6NGjfVEvAAAAcEk8PgP88MMP67XXXlObNm2cbZ06dVLZsmX1wAMPaNeuXZoxY4YGDRrk1UIBAAAAb/D4DPD+/fsVHh5eqD08PFw//vijJKlOnTr67bffLr06Sf/73/909913KyoqSsHBwWratKm2bt3qnG+M0fjx4xUbG6ugoCB16NBBu3btchkjJydHw4cPV4UKFRQSEqJevXrpl19+8Up9AAAAuLJ4HIBbtGihRx99VEePHnW2HT16VI899piuv/56SdJ///tfVa1a9ZKLO378uG688UbZ7XZ99tln2r17t6ZOnapy5co5+0yZMkXTpk3TrFmztGXLFsXExCgxMVEnT5509klOTtaSJUu0YMECrVu3TllZWerRo4fy8vIuuUYAAABcWTy+BGLu3Lm6+eabVbVqVcXFxclms+nQoUOqWbOmPvzwQ0lSVlaWnn766UsubvLkyYqLi9Mbb7zhbKtRo4bz/8YYzZgxQ08++aT69OkjSXrzzTcVHR2td999V0OGDFFGRobmzp2rt99+W507d5YkzZ8/X3FxcVq5cqW6dOlyyXUCAADgyuFxAK5bt6727NmjFStW6IcffpAxRvXq1VNiYqLKlPnjhHLv3r29UtxHH32kLl266LbbbtPatWtVpUoVDR06VPfff7+kP76cIy0tTUlJSc5lAgMDlZCQoPXr12vIkCHaunWrHA6HS5/Y2Fg1atRI69evLzYA5+TkKCcnxzmdmZkpSXI4HHI4HF7ZPqAoBfsX+xmAqwXHNVwu7u5jHgdg6Y9bnnXt2lVdu3a9mMXd9uOPP+qVV17RyJEj9cQTT2jz5s0aMWKEAgMDdc899ygtLU2SFB0d7bJcdHS0fvrpJ0lSWlqaAgICCt2SLTo62rl8USZNmqQJEyYUak9JSVFwcPClbhpwQampqSVdAgB4Fcc1+Fp2drZb/S4qAH/++ef6/PPPlZ6eXuib4V5//fWLGbJI+fn5uu666zRx4kRJUrNmzbRr1y698soruueee5z9bDaby3IF9yM+nwv1GTt2rEaOHOmczszMVFxcnJKSkor8ECDgLQ6HQ6mpqUpMTJTdbi/pcgDgknFcw+VS8I79hXgcgCdMmKBnn31W1113nSpXrnzBoHkpKleurAYNGri01a9fX4sWLZIkxcTESPrjLG/lypWdfdLT051nhWNiYpSbm6vjx4+7nAVOT093uZXbuQIDAxUYGFio3W638+LFZcG+BuBqw3ENvubu/uVxAJ49e7bmzZunAQMGeFyUp2688Ubt3bvXpe2HH35Q9erVJUnx8fGKiYlRamqqmjVrJknKzc3V2rVrNXnyZEl/3LXCbrcrNTVV/fr1kyQdOXJEO3fu1JQpU3y+DQAAAChdPA7Aubm55z1z6k2PPPKI2rRpo4kTJ6pfv37avHmzXnvtNb322muS/rj0ITk5WRMnTlSdOnVUp04dTZw4UcHBwerfv78kKSIiQoMHD9aoUaMUFRWlyMhIjR49Wo0bN3beFQIAAADW4XEAvu+++/Tuu+965TZnF3L99ddryZIlGjt2rJ599lnFx8drxowZuuuuu5x9HnvsMZ0+fVpDhw7V8ePH1apVK6WkpCgsLMzZZ/r06fL391e/fv10+vRpderUSfPmzZOfn5/PtwEAAACli80YYzxZ4OGHH9Zbb72lJk2aqEmTJoWutZg2bZpXCywtMjMzFRERoYyMDD4EB59yOBz69NNPddNNN3GtHIBSKTs7W99//73b/bNO5+iT1RvUvWNrhQYV/nxNUerVq8ddl+Axd/Oax2eAv/vuOzVt2lSStHPnTpd5vvxAHAAAKB2+//57tWjRwuPlPPnkzdatW9W8eXOP1wG4w+MAvHr1al/UAQAArhD16tXT1q1b3e6/98gJjXx/h6bd1lh1K5dzex2Ar1zUfYAlad++fdq/f7/at2+voKAgt+69CwAArnzBwcEenZ0t89MxBX55WvUbXaum1aN8WBngnjKeLnDs2DF16tRJ11xzjW666SYdOXJE0h8fjhs1apTXCwQAAAC8yeMA/Mgjj8hut+vQoUMuF6fffvvtWr58uVeLAwAAALzN40sgUlJStGLFClWtWtWlvU6dOvrpp5+8VhgAAADgCx6fAT516lSRtyX57bffivzqYAAAAKA08TgAt2/fXm+99ZZz2mazKT8/Xy+++KI6duzo1eIAAAAAb/P4EogXX3xRHTp00Ndff63c3Fw99thj2rVrl37//Xd99dVXvqgRAAAA8BqPzwA3aNBA3333nVq2bKnExESdOnVKffr00TfffKNatWr5okYAAADAay7qPsAxMTGaMGGCS9vPP/+sQYMG6fXXX/dKYQAAAIAveHwGuDi///673nzzTW8NBwAAAPiE1wIwAAAAcCUgAAMAAMBSCMAAAACwFLc/BNenT5/zzj9x4sSl1gIAAAD4nNsBOCIi4oLz77nnnksuCAAAAPAltwPwG2+84cs6AAAAgMuCa4ABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAICluP1FGAAA4Op24LdTOpVz1uvj7j96yvmvv7/3o0dIoL/iK4R4fVxcvQjAAABAB347pY7/WOPTdYz6YIfPxl49ugMhGG4jAAMAAOeZ3xm3N1XtSqHeHft0jpat2aAeHVorJCjQq2PvS89S8sLtPjlzjasXARgAADjVrhSqRlUivDqmw+FQWkWpefXystvtXh0buBh8CA4AAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWckUF4EmTJslmsyk5OdnZZozR+PHjFRsbq6CgIHXo0EG7du1yWS4nJ0fDhw9XhQoVFBISol69eumXX365zNUDAACgNLhiAvCWLVv02muvqUmTJi7tU6ZM0bRp0zRr1ixt2bJFMTExSkxM1MmTJ519kpOTtWTJEi1YsEDr1q1TVlaWevTooby8vMu9GQAAAChhV0QAzsrK0l133aU5c+aofPnyznZjjGbMmKEnn3xSffr0UaNGjfTmm28qOztb7777riQpIyNDc+fO1dSpU9W5c2c1a9ZM8+fP144dO7Ry5cqS2iQAAACUkCsiAA8bNkzdu3dX586dXdoPHDigtLQ0JSUlOdsCAwOVkJCg9evXS5K2bt0qh8Ph0ic2NlaNGjVy9gEAAIB1+Jd0AReyYMECbdu2TVu2bCk0Ly0tTZIUHR3t0h4dHa2ffvrJ2ScgIMDlzHFBn4Lli5KTk6OcnBzndGZmpiTJ4XDI4XBc3MbAkrKzs7V37163+2edztH6HfsVVm6jQoMC3Vqmbt26Cg4OvtgSAUBnz551/uvt33MF4/ni96cv68aVx919oFQH4J9//lkPP/ywUlJSVLZs2WL72Ww2l2ljTKG2c12oz6RJkzRhwoRC7SkpKQQNeGT//v0aNWqUx8tN8aDv1KlTVatWLY/XAQAFfs6SJH+tW7dOP4X6Zh2pqaleH/Ny1I0rR3Z2tlv9SnUA3rp1q9LT09WiRQtnW15enr744gvNmjXLeVYtLS1NlStXdvZJT093nhWOiYlRbm6ujh8/7nIWOD09XW3atCl23WPHjtXIkSOd05mZmYqLi1NSUpLCw8O9to24+mVnZ6tt27Zu9//hSIYeXbJbL97SQNdUjnBrGc4AA7hUuw5n6h87Nqpt27ZqGOvd33MOh0OpqalKTEyU3W736ti+rBtXnoJ37C+kVAfgTp06aceOHS5tf/3rX1WvXj2NGTNGNWvWVExMjFJTU9WsWTNJUm5urtauXavJkydLklq0aCG73a7U1FT169dPknTkyBHt3LlTU6YUf44tMDBQgYGF33622+1ef/Hi6hYREaGWLVu63T/gp2MK3JCrRk2bq2n1KB9WBgD/x9/f3/mvr37P+eJ36OWoG1cOd/eBUh2Aw8LC1KhRI5e2kJAQRUVFOduTk5M1ceJE1alTR3Xq1NHEiRMVHBys/v37S/ojfAwePFijRo1SVFSUIiMjNXr0aDVu3LjQh+oAAABw9SvVAdgdjz32mE6fPq2hQ4fq+PHjatWqlVJSUhQWFubsM336dPn7+6tfv346ffq0OnXqpHnz5snPz68EKwcAAEBJuOIC8Jo1a1ymbTabxo8fr/Hjxxe7TNmyZTVz5kzNnDnTt8UBAACg1Lsi7gMMAAAAeAsBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKf4lXQBwpTrw2ymdyjnr9XH3Hz3l/Nff3/sv0ZBAf8VXCPH6uAAAXCkIwMBFOPDbKXX8xxqfrmPUBzt8Nvbq0R0IwQAAyyIAAxeh4MzvjNubqnalUO+OfTpHy9ZsUI8OrRUSFOjVsfelZyl54XafnLkGAOBKQQAGLkHtSqFqVCXCq2M6HA6lVZSaVy8vu93u1bEBAAAfggMAAIDFEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZSqgPwpEmTdP311yssLEyVKlVS7969tXfvXpc+xhiNHz9esbGxCgoKUocOHbRr1y6XPjk5ORo+fLgqVKigkJAQ9erVS7/88svl3BQAAACUEqU6AK9du1bDhg3Txo0blZqaqrNnzyopKUmnTp1y9pkyZYqmTZumWbNmacuWLYqJiVFiYqJOnjzp7JOcnKwlS5ZowYIFWrdunbKystSjRw/l5eWVxGYBAACgBJXqr0Jevny5y/Qbb7yhSpUqaevWrWrfvr2MMZoxY4aefPJJ9enTR5L05ptvKjo6Wu+++66GDBmijIwMzZ07V2+//bY6d+4sSZo/f77i4uK0cuVKdenS5bJvFwAAAEpOqT4DfK6MjAxJUmRkpCTpwIEDSktLU1JSkrNPYGCgEhIStH79eknS1q1b5XA4XPrExsaqUaNGzj4AAACwjlJ9BvjPjDEaOXKk2rZtq0aNGkmS0tLSJEnR0dEufaOjo/XTTz85+wQEBKh8+fKF+hQsX5ScnBzl5OQ4pzMzMyVJDodDDofj0jcIV7SzZ886//X2/lAwni/2M1/WDeDKxnENVwN394ErJgA/9NBD+u6777Ru3bpC82w2m8u0MaZQ27ku1GfSpEmaMGFCofaUlBQFBwe7WTWuVj9nSZK/1q1bp59CfbOO1NRUr495OeoGcGXiuIarQXZ2tlv9rogAPHz4cH300Uf64osvVLVqVWd7TEyMpD/O8lauXNnZnp6e7jwrHBMTo9zcXB0/ftzlLHB6erratGlT7DrHjh2rkSNHOqczMzMVFxenpKQkhYeHe23bcGXadThT/9ixUW3btlXDWO/uDw6HQ6mpqUpMTJTdbvfq2L6sG8CVjeMargYF79hfSKkOwMYYDR8+XEuWLNGaNWsUHx/vMj8+Pl4xMTFKTU1Vs2bNJEm5ublau3atJk+eLElq0aKF7Ha7UlNT1a9fP0nSkSNHtHPnTk2ZMqXYdQcGBiowMLBQu91u9/qLF1cef39/57++2h98sa9djroBXJk4ruFq4O4+UKoD8LBhw/Tuu+/qww8/VFhYmPOa3YiICAUFBclmsyk5OVkTJ05UnTp1VKdOHU2cOFHBwcHq37+/s+/gwYM1atQoRUVFKTIyUqNHj1bjxo2dd4UAAACAdZTqAPzKK69Ikjp06ODS/sYbb+jee++VJD322GM6ffq0hg4dquPHj6tVq1ZKSUlRWFiYs//06dPl7++vfv366fTp0+rUqZPmzZsnPz+/y7UpAAAAKCVKdQA2xlywj81m0/jx4zV+/Phi+5QtW1YzZ87UzJkzvVgdAAAArkRX1H2AAQAAgEtFAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAICllOrboAGlVU7eGZUp+z8dyNyrMmW9++XzZ8+e1eGzh7Xn9z3ObzjylgOZWSpT9n/KyTsjKcKrYwMAcKUgAAMX4fCpnxQSP1NPbPbdOl5e/rJPxg2Jlw6faqoWivbJ+AAAlHYEYOAixIZU16kDw/XP25uqViXvnwH+at1XurHtjV4/A7w/PUsPL9yu2I7VvTouAABXEgIwcBEC/coq/0wVxYfXVYMo715K4HA4dMD/gOpH1pfdbvfq2PlnMpR/5qgC/cp6dVwAAK4kfAgOAAAAlkIABgAAgKVwCQQAAODuNrAUAjAAAODuNrAUAjAAAODuNrAUAjAAAODuNrAUPgQHAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAU/5IuALgSnXbkSZJ2/i/D62OfOp2jr49KMT8dV0hQoFfH3pee5dXxAAC4EhGAgYuw//8HyccX7/DRGvz19r4tPhpbCgnkpQ8AsC5+CwIXIalhjCSpVqVQBdn9vDr23iMZGvXBDk3t21h1K0d4dWzpj/AbXyHE6+MCAHClIAADFyEyJEB3tKzmk7HPnj0rSapVMUSNqng/AAMAYHUEYAAAwGcbYCmWCsAvv/yyXnzxRR05ckQNGzbUjBkz1K5du5IuCwCAEsdnG2AlltlbFi5cqOTkZL388su68cYb9eqrr6pbt27avXu3qlXzzVvZAABcKfhsA6zEMgF42rRpGjx4sO677z5J0owZM7RixQq98sormjRpUglXh6tZdna2vv/+e7f77z1yQjlp+7RnZ5Dyj5Vza5l69eopODj4IisEAD7bAGuxRADOzc3V1q1b9fjjj7u0JyUlaf369UUuk5OTo5ycHOd0ZmamJMnhcMjhcPiuWFx1du7cqVatWnm8XP833e+7adMmNWvWzON1AMDFyM7O1t69e93u/8ORDOWk7dPO7QHK/dW9AFy3bl3+sIfH3M1olgjAv/32m/Ly8hQdHe3SHh0drbS0tCKXmTRpkiZMmFCoPSUlhRckPJKTk6OpU6e63d+RL/1+RoosK9nd/K7GgwcP6siRIxdZIQB4Zv/+/Ro1apTHyw3w4A/7qVOnqlatWh6vA9aWnZ3tVj9LBOACNpvNZdoYU6itwNixYzVy5EjndGZmpuLi4pSUlKTw8HCf1glrczgcSk1NVWJioux2e0mXAwCFZGdnq23btm73zzqdoxVfblGXdtcr1M27QHAGGBej4B37C7FEAK5QoYL8/PwKne1NT08vdFa4QGBgoAIDC79I7XY7oQSXBfsagNIqIiJCLVu2dLu/w+HQyRO/q12bGziuwafc3b/cfIP1yhYQEKAWLVooNTXVpT01NVVt2rQpoaoAAABQEixxBliSRo4cqQEDBui6665T69at9dprr+nQoUN68MEHS7o0AAAAXEaWCcC33367jh07pmeffVZHjhxRo0aN9Omnn6p69eolXRoAAAAuI8sEYEkaOnSohg4dWtJlAAAAoARZ4hpgAAAAoAABGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWIp/SRdwpTDGSJIyMzNLuBJc7RwOh7Kzs5WZmSm73V7S5QDAJeO4hsulIKcV5LbiEIDddPLkSUlSXFxcCVcCAACA8zl58qQiIiKKnW8zF4rIkCTl5+fr8OHDCgsLk81mK+lycBXLzMxUXFycfv75Z4WHh5d0OQBwyTiu4XIxxujkyZOKjY1VmTLFX+nLGWA3lSlTRlWrVi3pMmAh4eHh/KIAcFXhuIbL4XxnfgvwITgAAABYCgEYAAAAlkIABkqZwMBAjRs3ToGBgSVdCgB4Bcc1lDZ8CA4AAACWwhlgAAAAWAoBGAAAAJZCAAYAAIClEIBhSTabTUuXLi3pMq5YBw8elM1m0/bt20u6FABu4rgH/B8CMK5K6enpGjJkiKpVq6bAwEDFxMSoS5cu2rBhQ0mX5rbSEjLvvfde9e7du0RrAHBhV8Nx78+SkpLk5+enjRs3lnQpuArxTXC4Kt16661yOBx68803VbNmTf3666/6/PPP9fvvv5d0aQDgE1fTce/QoUPasGGDHnroIc2dO1c33HDDefvn5uYqICDgMlWHq4IBrjLHjx83ksyaNWuK7SPJzJkzx/Tu3dsEBQWZ2rVrmw8//NA5/+zZs2bQoEGmRo0apmzZsuaaa64xM2bMcBlj4MCB5uabbzbjx483FStWNGFhYeaBBx4wOTk5zj75+flm8uTJJj4+3pQtW9Y0adLEvP/++25tx4EDB4wk88033xQ5/0Jjr1692kgyK1euNC1atDBBQUGmdevW5vvvv3cZ57nnnjMVK1Y0oaGhZvDgwWbMmDHm2muvNcYYM27cOCPJ5Wf16tXO2hYtWmQ6dOhggoKCTJMmTcz69evd2jYA3nW1HPcKjB8/3txxxx1mz549JiwszGRlZbnMT0hIMMOGDTOPPPKIiYqKMu3btzfGGLNr1y7TrVs3ExISYipVqmTuvvtuc/ToUedyn332mbnxxhtNRESEiYyMNN27dzf79u3zqDZcHQjAuOo4HA4TGhpqkpOTzZkzZ4rsI8lUrVrVvPvuu+a///2vGTFihAkNDTXHjh0zxhiTm5trnnnmGbN582bz448/mvnz55vg4GCzcOFC5xgDBw40oaGh5vbbbzc7d+40y5YtMxUrVjRPPPGEs88TTzxh6tWrZ5YvX272799v3njjDRMYGHjeX1IFLhSALzR2QQBu1aqVWbNmjdm1a5dp166dadOmjXOM+fPnm7Jly5rXX3/d7N2710yYMMGEh4c7A/DJkydNv379TNeuXc2RI0fMkSNHTE5OjrO2evXqmWXLlpm9e/eavn37murVqxuHw3HBbQPgXVfLcc+YPwJ09erVzbJly4wxxrRo0cK8/vrrLn0SEhJMaGioefTRR833339v9uzZYw4fPmwqVKhgxo4da/bs2WO2bdtmEhMTTceOHZ3LffDBB2bRokXmhx9+MN98843p2bOnady4scnLy3PvgcZVgwCMq9IHH3xgypcvb8qWLWvatGljxo4da7799lvnfEnmqaeeck5nZWUZm81mPvvss2LHHDp0qLn11lud0wMHDjSRkZHm1KlTzrZXXnnFhIaGmry8PJOVlWXKli1b6Kzo4MGDzZ133nnBbThfAHZn7D+fAS7wySefGEnm9OnTxhhjWrVqZYYNG+Yyxo033ugMwAXbefPNNxdZ27///W9n265du4wks2fPngtuGwDvuxqOe8YYk5KSYipWrOj8Y3r69OnmxhtvdOmTkJBgmjZt6tL29NNPm6SkJJe2n3/+2Ugye/fuLXJd6enpRpLZsWOHW7Xh6sGH4HBVuvXWW3X48GF99NFH6tKli9asWaPmzZtr3rx5zj5NmjRx/j8kJERhYWFKT093ts2ePVvXXXedKlasqNDQUM2ZM0eHDh1yWc+1116r4OBg53Tr1q2VlZWln3/+Wbt379aZM2eUmJio0NBQ589bb72l/fv3X9L2eTL2n7ezcuXKkuTczr1796ply5Yu/c+dPp/zjQ3g8rpajntz587V7bffLn//Pz6mdOedd2rTpk3au3evS7/rrrvOZXrr1q1avXq1y3rr1asnSc5179+/X/3791fNmjUVHh6u+Ph4SSq0jbj68SE4XLXKli2rxMREJSYm6plnntF9992ncePG6d5775Uk2e12l/42m035+fmSpP/85z965JFHNHXqVLVu3VphYWF68cUXtWnTJrfW/eexPvnkE1WpUsVlfmBg4CVtmydj/3k7bTaby/J/bitgPPh29AuNDeDyutKPe7///ruWLl0qh8OhV155xdmel5en119/XZMnT3a2hYSEuCybn5+vnj17uvQpUPAHes+ePRUXF6c5c+YoNjZW+fn5atSokXJzc93aRlw9CMCwjAYNGrh9D8wvv/xSbdq00dChQ51tRZ29+Pbbb3X69GkFBQVJkjZu3KjQ0FBVrVpV5cuXV2BgoA4dOqSEhASvbEOBBg0aeGXsunXravPmzRowYICz7euvv3bpExAQoLy8vIteB4CSc6Ud99555x1VrVq1UM2ff/65Jk2apBdeeMF5ZvhczZs316JFi1SjRo0i+xw7dkx79uzRq6++qnbt2kmS1q1b53GNuDoQgHHVOXbsmG677TYNGjRITZo0UVhYmL7++mtNmTJFN998s1tj1K5dW2+99ZZWrFih+Ph4vf3229qyZYvz7bICubm5Gjx4sJ566in99NNPGjdunB566CGVKVNGYWFhGj16tB555BHl5+erbdu2yszM1Pr16xUaGqqBAwe6Vcu5b/tJf/xS88bYw4cP1/3336/rrrtObdq00cKFC/Xdd9+pZs2azj41atTQihUrtHfvXkVFRSkiIsKtsQFcPlfLcW/u3Lnq27evGjVq5NJevXp1jRkzRp988kmx2zNs2DDNmTNHd955px599FFVqFBB+/bt04IFCzRnzhyVL19eUVFReu2111S5cmUdOnRIjz/+uFuPDa4+BGBcdUJDQ9WqVStNnz5d+/fvl8PhUFxcnO6//3498cQTbo3x4IMPavv27br99ttls9l05513aujQofrss89c+nXq1El16tRR+/btlZOTozvuuEPjx493zn/uuedUqVIlTZo0ST/++KPKlSun5s2bu12HJN1xxx2F2g4cOOCVse+66y79+OOPGj16tM6cOaN+/frp3nvv1ebNm5197r//fq1Zs0bXXXedsrKytHr1atWoUcPtdQDwvavhuLd161Z9++23mjNnTqF5YWFhSkpK0ty5c4sNwLGxsfrqq680ZswYdenSRTk5Oapevbq6du2qMmXKyGazacGCBRoxYoQaNWqkunXr6l//+pc6dOjg1uODq4vNeHLBHwCne++9VydOnLjqvlo0MTFRMTExevvtt0u6FAClzNV63IP1cAYYsLDs7GzNnj1bXbp0kZ+fn9577z2tXLlSqampJV0aAAA+w23QgBLy4IMPutyu588/Dz744GWpwWaz6dNPP1W7du3UokULffzxx1q0aJE6d+58WdYPwFpKw3EPkLgEAigx6enpyszMLHJeeHi4KlWqdJkrAgDf4riH0oIADAAAAEvhEggAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGAp/w8Qq5MKy4RauAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "# Create box plots for 'Shape_Length' and 'Shape_Area'\n",
+    "nga_data1_clipped[['Shape_Length', 'Shape_Area']].boxplot(ax=ax)\n",
+    "\n",
+    "# Set the y-axis label\n",
+    "ax.set_ylabel('Length and Area')\n",
+    "\n",
+    "# Set the title\n",
+    "ax.set_title('Summary Statistics for Shape Length and Area 2001-2025 A2')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae19c604",
+   "metadata": {},
+   "source": [
+    "### Summary of Observation\n",
+    "\n",
+    "**Shape_Length Boxplot**:\n",
+    "\n",
+    "In the boxplot for 'Shape_Length,' the median is represented by the central line inside the box. This indicates that roughly half of the geographic features have 'Shape_Length' values clustered around this central length. The interquartile range (IQR) for 'Shape_Length' is relatively narrow, suggesting that most features have similar lengths, and their lengths are concentrated within a limited range. However, outliers are observed in 'Shape_Length,' which are data points with exceptionally long lengths compared to the majority of the features. These outliers may represent unique or exceptional geographic elements.\n",
+    "\n",
+    "**Shape_Area**:\n",
+    "\n",
+    "In the boxplot for 'Shape_Area,' the median is also indicated by the central line inside the box, suggesting that approximately half of the features have 'Shape_Area' values clustered around this central area. However, the IQR for 'Shape_Area' is wider compared to 'Shape_Length,' indicating that the areas of geographic features vary more, with a broader range of feature sizes. Similar to 'Shape_Length,' outliers are present in 'Shape_Area,' indicating some features with exceptionally large areas compared to the majority.\n",
+    "\n",
+    "### Comparison:\n",
+    "\n",
+    "When comparing the two boxplots:\n",
+    "\n",
+    "'Shape_Length' appears to have a more concentrated distribution, with most features having similar lengths, as indicated by the narrow IQR. This suggests that the lengths of the geographic features are relatively consistent.\n",
+    "\n",
+    "In contrast, 'Shape_Area' has a wider IQR, indicating greater variability in the sizes of geographic features. This means that the areas of features vary more widely compared to their lengths.\n",
+    "\n",
+    "Both 'Shape_Length' and 'Shape_Area' exhibit outliers, but the nature of these outliers differs. 'Shape_Length' outliers represent features with exceptionally long lengths, while 'Shape_Area' outliers represent features with exceptionally large areas.\n",
+    "\n",
+    "The symmetric extension of whiskers in both box plots suggests that the data distribution in both cases is balanced and does not exhibit pronounced skewness, indicating a relatively even spread of values.\n",
+    "\n",
+    "In summary, while both 'Shape_Length' and 'Shape_Area' have outliers, 'Shape_Length' shows more consistent lengths among geographic features, whereas 'Shape_Area' displays a wider range of feature sizes. Both distributions are balanced and not heavily skewed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f67056ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Shape_Length   Shape_Area\n",
+      "count      6.000000     6.000000\n",
+      "mean     180.791666   397.805889\n",
+      "std      174.316439   565.596143\n",
+      "min        4.000000     0.750000\n",
+      "25%       27.905623    16.261654\n",
+      "50%      155.728370   136.382803\n",
+      "75%      329.167563   569.338732\n",
+      "max      397.014591  1423.441080\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate summary statistics for 'Shape_Length' and 'Shape_Area'\n",
+    "summary_stats_2 = Nga_clipped_data2[['Shape_Length', 'Shape_Area']].describe()\n",
+    "print(summary_stats_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e28a9082",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "# Create box plots for 'Shape_Length' and 'Shape_Area'\n",
+    "Nga_clipped_data2[['Shape_Length', 'Shape_Area']].boxplot(ax=ax)\n",
+    "\n",
+    "# Set the y-axis label\n",
+    "ax.set_ylabel('Length and Area')\n",
+    "\n",
+    "# Set the title\n",
+    "ax.set_title('Summary Statistics for Shape Length and Area 2001-2025 B1')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d95458c7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Summary of Observations:\n",
+    "\n",
+    "**Shape_Length Boxplot**:\n",
+    "\n",
+    "Within the 'Shape_Length' box plot, the central line inside the box serves as the median, indicating that approximately half of the geographic features have 'Shape_Length' values clustered around this central length. The interquartile range (IQR) for 'Shape_Length' is relatively narrow, implying that most features have similar lengths, and these lengths are concentrated within a limited range. However, there are outliers in 'Shape_Length,' representing data points with exceptionally long lengths compared to the majority of the features. These outliers may signify unique or exceptional geographic elements.\n",
+    "\n",
+    "**Shape_Area Boxplot**:\n",
+    "\n",
+    "In the box plot for 'Shape_Area,' the central line within the box also represents the median, suggesting that roughly half of the features have 'Shape_Area' values concentrated around this central area. However, the IQR for 'Shape_Area' is wider in comparison to 'Shape_Length,' indicating that the sizes of geographic features exhibit greater variability, encompassing a broader range of feature sizes. Similar to 'Shape_Length,' outliers are present in 'Shape_Area,' indicating the presence of some features with exceptionally large areas compared to the majority.\n",
+    "\n",
+    "### Comparison:\n",
+    "\n",
+    "Comparing both box plots:\n",
+    "\n",
+    "'Shape_Length' depicts a more concentrated distribution with most features having similar lengths, as indicated by the narrow IQR. This suggests a relatively uniform distribution of lengths among geographic features.\n",
+    "\n",
+    "In contrast, 'Shape_Area' displays a wider IQR, indicating more extensive variability in the sizes of geographic features, encompassing a broader spectrum of feature sizes compared to 'Shape_Length.'\n",
+    "\n",
+    "Both 'Shape_Length' and 'Shape_Area' exhibit outliers, but the nature of these outliers varies. 'Shape_Length' outliers represent exceptionally long lengths, while 'Shape_Area' outliers represent features with exceptionally large areas.\n",
+    "\n",
+    "The symmetric extension of whiskers in both box plots indicates a balanced data distribution in both cases, with no pronounced skewness. This suggests that the data distribution is relatively even and not skewed toward one end.\n",
+    "\n",
+    "In summary, while both 'Shape_Length' and 'Shape_Area' feature outliers, 'Shape_Length' showcases more consistent lengths among geographic features, while 'Shape_Area' reflects a wider range of feature sizes. Both distributions appear balanced, without significant skewness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "1bb83e62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Shape_Length   Shape_Area\n",
+      "count      6.000000     6.000000\n",
+      "mean     193.666667   374.833333\n",
+      "std      196.430819   556.228475\n",
+      "min        4.000000     0.750000\n",
+      "25%       23.250000    12.500000\n",
+      "50%      171.000000   127.375000\n",
+      "75%      309.000000   481.125000\n",
+      "max      487.000000  1420.250000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate summary statistics for 'Shape_Length' and 'Shape_Area'\n",
+    "summary_stats_3 = Nga_clipped_data3[['Shape_Length', 'Shape_Area']].describe()\n",
+    "print(summary_stats_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dcf524c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "# Create box plots for 'Shape_Length' and 'Shape_Area'\n",
+    "Nga_clipped_data3[['Shape_Length', 'Shape_Area']].boxplot(ax=ax)\n",
+    "\n",
+    "# Set the y-axis label\n",
+    "ax.set_ylabel('Length and Area')\n",
+    "\n",
+    "# Set the title\n",
+    "ax.set_title('Summary Statistics for Shape Length and Area 2001-2025 B2')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "822e9ba9",
+   "metadata": {},
+   "source": [
+    "### Summary of Observations:\n",
+    "\n",
+    "**Shape_Length Boxplot**:\n",
+    "\n",
+    "Within the 'Shape_Length' box plot, the central line within the box represents the median, signifying that approximately half of the geographic features exhibit 'Shape_Length' values clustered around this central length. The interquartile range (IQR) for 'Shape_Length' is relatively narrow, implying that most features have similar lengths, and these lengths are concentrated within a limited range. However, there are outliers in 'Shape_Length,' which are data points with exceptionally long lengths compared to the majority of the features. These outliers may represent unique or exceptional geographic elements.\n",
+    "\n",
+    "**Shape_Area Boxplot**:\n",
+    "\n",
+    "In the box plot for 'Shape_Area,' the central line within the box also represents the median, suggesting that about half of the features have 'Shape_Area' values concentrated around this central area. However, the IQR for 'Shape_Area' is wider in comparison to 'Shape_Length,' indicating that the sizes of geographic features vary more extensively, encompassing a broader range of feature sizes. Similarly to 'Shape_Length,' outliers are present in 'Shape_Area,' indicating the existence of some features with exceptionally large areas compared to the majority.\n",
+    "\n",
+    "### Comparison:\n",
+    "\n",
+    "comparing the two box plots:\n",
+    "\n",
+    "'Shape_Length' demonstrates a more concentrated distribution, with most features having similar lengths, as indicated by the narrow IQR. This implies that the lengths of geographic features are relatively uniform.\n",
+    "\n",
+    "Conversely, 'Shape_Area' displays a wider IQR, indicating greater variability in the sizes of geographic features. This suggests that the areas of features exhibit a broader spectrum of sizes compared to their lengths.\n",
+    "\n",
+    "Both 'Shape_Length' and 'Shape_Area' feature outliers, but the nature of these outliers differs. 'Shape_Length' outliers represent features with exceptionally long lengths, whereas 'Shape_Area' outliers signify features with exceptionally large areas.\n",
+    "\n",
+    "The symmetric extension of whiskers in both box plots suggests that the data distribution in both cases is balanced and not characterized by significant skewness. This indicates a relatively even spread of values.\n",
+    "\n",
+    "In summary, while both 'Shape_Length' and 'Shape_Area' have outliers, 'Shape_Length' shows more consistent lengths among geographic features, whereas 'Shape_Area' displays a wider range of feature sizes. Both distributions are balanced and do not exhibit pronounced skewness."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4285cc6",
+   "metadata": {},
+   "source": [
+    "### Spatial autocorrelation to identify clustering or dispersion of climate regions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f25b92a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a spatial weights matrix (Queen contiguity) and Compute Moran's I for 'GRIDCODE' \n",
+    "value1 = lps.weights.Queen.from_dataframe(nga_data_clipped, use_index=False)\n",
+    "moran1 = Moran(nga_data_clipped['GRIDCODE'], value1)\n",
+    "value2 = lps.weights.Queen.from_dataframe(nga_data1_clipped, use_index=False)\n",
+    "moran2 = Moran(nga_data1_clipped['GRIDCODE'], value2)\n",
+    "value3 = lps.weights.Queen.from_dataframe(Nga_clipped_data2, use_index=False)\n",
+    "moran3 = Moran(Nga_clipped_data2['GRIDCODE'], value3)\n",
+    "value4 = lps.weights.Queen.from_dataframe(Nga_clipped_data3, use_index=False)\n",
+    "moran4 = Moran(Nga_clipped_data3['GRIDCODE'], value4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4d8aa7a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a subplots\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "\n",
+    "# Create and show the Moran Scatterplot for moran1\n",
+    "plot1 = moran_scatterplot(moran1, ax=axes[0, 0])\n",
+    "axes[0, 0].set_title('Moran Scatterplot 1')\n",
+    "\n",
+    "# Create and show the Moran Scatterplot for moran2\n",
+    "plot2 = moran_scatterplot(moran2, ax=axes[0, 1])\n",
+    "axes[0, 1].set_title('Moran Scatterplot 2')\n",
+    "\n",
+    "# Create and show the Moran Scatterplot for moran3\n",
+    "plot3 = moran_scatterplot(moran3, ax=axes[1, 0])\n",
+    "axes[1, 0].set_title('Moran Scatterplot 3')\n",
+    "\n",
+    "# Create and show the Moran Scatterplot for moran4\n",
+    "plot4 = moran_scatterplot(moran4, ax=axes[1, 1])\n",
+    "axes[1, 1].set_title('Moran Scatterplot 4')\n",
+    "\n",
+    "# Adjust layout and display the subplots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60186804",
+   "metadata": {},
+   "source": [
+    "### Observation:\n",
+    "\n",
+    "**Moran Scatterplot 1**:\n",
+    "\n",
+    "In the first Moran Scatterplot, the calculated Moran's I value is approximately 0.58. This scatterplot illustrates a positive spatial autocorrelation, where climate codes with similar values tend to cluster together. This suggests that, in the initial scenario, regions sharing similar grid codes exhibit a distinct pattern of spatial proximity, indicating a homogeneity in climate conditions.\n",
+    "\n",
+    "**Moran Scatterplot 2**:\n",
+    "\n",
+    "Similarly, in the second Moran Scatterplot, the Moran's I value is also approximately 0.58, revealing a positive spatial autocorrelation. Regions with akin grid codes continue to cluster together, maintaining a consistent pattern of climate homogeneity.\n",
+    "\n",
+    "**Moran Scatterplot 3**:\n",
+    "\n",
+    "The third scenario, as represented in Moran Scatterplot 3, also maintains a Moran's I value of approximately 0.58, indicating a positive spatial autocorrelation. Just like the previous scenarios, regions with climate codes of similar values exhibit clustering, underscoring the ongoing pattern of spatial similarity in climate conditions.\n",
+    "\n",
+    "**Moran Scatterplot 4**:\n",
+    "\n",
+    "The fourth scenario, as represented in Moran Scatterplot 4, also maintains a Moran's I value of approximately 0.58, indicating a positive spatial autocorrelation. Just like the previous scenarios, regions with climate codes of similar values exhibit clustering, underscoring the ongoing pattern of spatial similarity in climate conditions.\n",
+    "\n",
+    "\n",
+    "### Overall Findings:\n",
+    "\n",
+    "The analysis of the four scenarios reveals consistent positive spatial autocorrelation in the first four scenarios, where regions with similar climate codes cluster together. In summary, the findings highlight there is no change in the spatial autocorrelation pattern between the four scenarios."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "165b6f88",
+   "metadata": {},
+   "source": [
+    "### CONCLUSION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e7b5033",
+   "metadata": {},
+   "source": [
+    "In conclusion, the spatial analysis conducted on the climate data has provided valuable insights into the distribution and patterns of climate regions within the geographic area. Here are the key findings and conclusions from the spatial analysis:\n",
+    "\n",
+    "Climate Distribution: The analysis revealed the spatial distribution of climate regions based on the 'Climate Code' attribute. This allowed us to visually assess the diversity of climate types across the study area.\n",
+    "\n",
+    "Climate Change Comparison: By comparing climate data for different time periods, there was a shifts in climate regions. These changes are indicative of the impact of climate change on the study area.\n",
+    "\n",
+    "Moran's I Analysis: Moran's I statistic was used to assess spatial autocorrelation. The Moran Scatterplots provided evidence of positive or negative spatial autocorrelation in the 'GRIDCODE' attribute, indicating clustering or dispersion of similar climate regions.\n",
+    "\n",
+    "Comparison of Climate Scenarios: Comparing climate data for different scenarios (e.g., A1F1, A2, B1, B2) highlighted variations in climate regions and how they are affected by different socio-economic and environmental factors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "865407f3",
+   "metadata": {},
+   "source": [
+    "## GLOBAL ROAD DATASET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "306bf3c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load the json file\n",
+    "with open (\"C:/Users/adere/Downloads/Global Roads Open Access Data Set.json.geojson\") as file:\n",
+    "    road = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "58f6fc34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create GeoDataFrame from the road data\n",
+    "Global_road = gpd.GeoDataFrame.from_features(road['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a2952280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ONME</th>\n",
+       "      <th>FCLASS</th>\n",
+       "      <th>SRFTPE</th>\n",
+       "      <th>ISSEASONAL</th>\n",
+       "      <th>CURNTPRAC</th>\n",
+       "      <th>GDWTHRPRAC</th>\n",
+       "      <th>SUM_LENGTH_KM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LINESTRING (-66.82451 17.98029, -66.82455 17.9...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LINESTRING (-66.62012 17.98131, -66.62126 17.9...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  OBJECTID  ONME  FCLASS  \\\n",
+       "0  LINESTRING (-66.82451 17.98029, -66.82455 17.9...         1  None       0   \n",
+       "1  LINESTRING (-66.62012 17.98131, -66.62126 17.9...         2  None       0   \n",
+       "\n",
+       "   SRFTPE  ISSEASONAL  CURNTPRAC  GDWTHRPRAC  SUM_LENGTH_KM  \n",
+       "0     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "1     0.0         0.0        NaN         NaN    16686.65607  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "9a7b2307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count the number of seasonal and non-seasonal roads\n",
+    "seasonal_count = Global_road ['ISSEASONAL'].sum()\n",
+    "non_seasonal_count = len(Global_road) - seasonal_count\n",
+    "\n",
+    "# Calculate the percentage of seasonal and non-seasonal roads\n",
+    "total_count = len(Global_road)\n",
+    "percentage_seasonal = (seasonal_count / total_count) * 100\n",
+    "percentage_non_seasonal = (non_seasonal_count / total_count) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "10f2e934",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a pie chart to visualize the distribution\n",
+    "labels = ['Seasonal Roads', 'Non-Seasonal Roads']\n",
+    "sizes = [seasonal_count, non_seasonal_count]\n",
+    "colors = ['lightcoral', 'lightskyblue']\n",
+    "explode = (0.1, 0)  # To explode the 'Seasonal Roads' slice\n",
+    "\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
+    "\n",
+    "# Set a title\n",
+    "plt.title('Distribution of Seasonal and Non-Seasonal Roads')\n",
+    "\n",
+    "# Show the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ebfbfb",
+   "metadata": {},
+   "source": [
+    "###  Observation:\n",
+    "\n",
+    "Seasonal Roads (13.6%):\n",
+    "\n",
+    "Approximately 13.6% of the road segments fall into the 'Seasonal' category. These are road segments that are subject to seasonal variations, which can include changes in road conditions due to weather-related factors.\n",
+    "Seasonal roads may experience challenges during specific times of the year, such as winter weather, heavy rainfall, or other climatic conditions. This could lead to issues like reduced road accessibility, the need for maintenance, or adjustments in transportation planning.\n",
+    "Non-Seasonal Roads (86.4%):\n",
+    "\n",
+    "The majority, accounting for about 86.4% of the road segments, belong to the 'Non-Seasonal' category. These roads are not affected by significant seasonal variations and are generally accessible and operational throughout the year.\n",
+    "Non-seasonal roads are more reliable for transportation and require fewer adaptations or maintenance efforts in response to weather changes.\n",
+    "The significance of this analysis lies in understanding the proportion of road segments that are influenced by seasonal factors. For transportation planning, infrastructure maintenance, and urban development, knowing the ratio of seasonal to non-seasonal roads is crucial. It helps in allocating resources effectively, preparing for weather-related challenges, and ensuring the reliability of the transportation network year-round"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a427a37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          16686.656070\n",
+       "1          16686.656070\n",
+       "2          16686.656070\n",
+       "3          16686.656070\n",
+       "4          16686.656070\n",
+       "               ...     \n",
+       "1101295      150.974974\n",
+       "1101296      127.996653\n",
+       "1101297      127.996653\n",
+       "1101298      127.996653\n",
+       "1101299      127.996653\n",
+       "Name: SUM_LENGTH_KM, Length: 1101300, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road['SUM_LENGTH_KM']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6a3c8c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a histogram to visualize the distribution of road lengths\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(Global_road['SUM_LENGTH_KM'], bins=20, color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Road Length (km)')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Distribution of Road Lengths')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37d7e01b",
+   "metadata": {},
+   "source": [
+    "### Observation:\n",
+    "\n",
+    "Based on the panalysis, the distribution of road lengths appears to be positively skewed to the right.\n",
+    "The histogram shows that there are some road segments with exceptionally long lengths, leading to a rightward extension of the distribution.\n",
+    "The mode of the distribution, representing the most common road length category, is located at the lower end of the range.\n",
+    "Few Extremely Long Road Segments: The presence of outliers on the right side of the histogram suggests that there are relatively few road segments with exceptionally long lengths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3a6690d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FCLASS\n",
+       "0    582993\n",
+       "5    186610\n",
+       "6    153883\n",
+       "3     98708\n",
+       "4     41773\n",
+       "2     32703\n",
+       "1      4630\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road['FCLASS'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "d99a936e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dictionary to map the numbers to specific features\n",
+    "feature_mapping = {\n",
+    "    0: 'Unspecified',\n",
+    "    1: 'Highway',\n",
+    "    2: 'Primary',\n",
+    "    3: 'Secondary',\n",
+    "    4: 'Tertiary',\n",
+    "    5: 'Local/Urban',\n",
+    "    6: 'Trail'\n",
+    "}\n",
+    "\n",
+    "#map the values in FCLASS \n",
+    "Global_road['FCLASS'] = Global_road['FCLASS'].map(feature_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "87ddaa53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the occurrences of each feature\n",
+    "feature_counts = Global_road['FCLASS'].value_counts()\n",
+    "\n",
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "feature_counts.plot(kind='bar', color='skyblue')\n",
+    "plt.xlabel('Road Feature')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of Road Features')\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility \n",
+    "\n",
+    "# Show the chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15e364a4",
+   "metadata": {},
+   "source": [
+    "### Observation:\n",
+    "\n",
+    "Based on the comprehensive analysis of road features, it becomes evident that a significant portion of the road segments falls under the 'Unspecified' category, with a total count of 582,993. \n",
+    "\n",
+    "In contrast, the analysis reveals that there are fewer road segments categorized as 'Highway' with a count of 4,630. Highways play a pivotal role in long-distance transportation, acting as vital connectors between cities and regions. Typically characterized by multiple lanes and high-speed limits, highways are designed to facilitate efficient, rapid transit over extended distances. While they represent a smaller portion of the dataset, their significance in enabling long-distance travel and interconnecting urban centers cannot be understated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49836aa3",
+   "metadata": {},
+   "source": [
+    "## PROTECTED AREAS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "652bd8f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the file\n",
+    "file_path = r\"C:/Users/adere/Downloads/WDPA_WDOECM_wdpa_gdb_polygons/WDPA_WDOECM_wdpa_gdb_polygons.shp\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "8f1ff184",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Read geospatial data from the specified file path using GeoPandas\n",
+    "protected_area = gpd.read_file(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "09a43dfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WDPAID</th>\n",
+       "      <th>WDPA_PID</th>\n",
+       "      <th>PA_DEF</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>ORIG_NAME</th>\n",
+       "      <th>DESIG</th>\n",
+       "      <th>DESIG_ENG</th>\n",
+       "      <th>DESIG_TYPE</th>\n",
+       "      <th>IUCN_CAT</th>\n",
+       "      <th>INT_CRIT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MANG_AUTH</th>\n",
+       "      <th>MANG_PLAN</th>\n",
+       "      <th>VERIF</th>\n",
+       "      <th>METADATAID</th>\n",
+       "      <th>SUB_LOC</th>\n",
+       "      <th>PARENT_ISO</th>\n",
+       "      <th>ISO3</th>\n",
+       "      <th>SUPP_INFO</th>\n",
+       "      <th>CONS_OBJ</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10715.0</td>\n",
+       "      <td>10715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-KAM</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>209777.0</td>\n",
+       "      <td>209777_E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sarali Land between Rivers / Great Volzhsko-Ka...</td>\n",
+       "      <td>Great Volzhsko-Kamsky</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-TA</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>POLYGON ((49.30487 55.36806, 49.30433 55.37184...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     WDPAID  WDPA_PID PA_DEF  \\\n",
+       "0   10715.0     10715      1   \n",
+       "1  209777.0  209777_E      1   \n",
+       "\n",
+       "                                                NAME              ORIG_NAME  \\\n",
+       "0                                         Kronotskiy             Kronotskiy   \n",
+       "1  Sarali Land between Rivers / Great Volzhsko-Ka...  Great Volzhsko-Kamsky   \n",
+       "\n",
+       "                          DESIG                     DESIG_ENG     DESIG_TYPE  \\\n",
+       "0  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "1  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "\n",
+       "         IUCN_CAT        INT_CRIT  ...     MANG_AUTH     MANG_PLAN  \\\n",
+       "0  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "1  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "\n",
+       "            VERIF  METADATAID  SUB_LOC PARENT_ISO  ISO3       SUPP_INFO  \\\n",
+       "0  State Verified         840   RU-KAM        RUS   RUS  Not Applicable   \n",
+       "1  State Verified         840    RU-TA        RUS   RUS  Not Applicable   \n",
+       "\n",
+       "         CONS_OBJ                                           geometry  \n",
+       "0  Not Applicable  MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...  \n",
+       "1  Not Applicable  POLYGON ((49.30487 55.36806, 49.30433 55.37184...  \n",
+       "\n",
+       "[2 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c417bc47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['WDPAID', 'WDPA_PID', 'PA_DEF', 'NAME', 'ORIG_NAME', 'DESIG',\n",
+       "       'DESIG_ENG', 'DESIG_TYPE', 'IUCN_CAT', 'INT_CRIT', 'MARINE',\n",
+       "       'REP_M_AREA', 'GIS_M_AREA', 'REP_AREA', 'GIS_AREA', 'NO_TAKE',\n",
+       "       'NO_TK_AREA', 'STATUS', 'STATUS_YR', 'GOV_TYPE', 'OWN_TYPE',\n",
+       "       'MANG_AUTH', 'MANG_PLAN', 'VERIF', 'METADATAID', 'SUB_LOC',\n",
+       "       'PARENT_ISO', 'ISO3', 'SUPP_INFO', 'CONS_OBJ', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Print column names\n",
+    "protected_area.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "48187d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count the occurrences of each count_desig_type\n",
+    "count_desig_type =protected_area['DESIG_TYPE'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "eed33476",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "count_desig_type.plot(kind='bar', color='blue')\n",
+    "plt.xlabel('Designation Type')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of Designation Type')\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility \n",
+    "\n",
+    "# Show the chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b56ba272",
+   "metadata": {},
+   "source": [
+    "### Observation/findings:\n",
+    "\n",
+    "The analysis of the current data reveals a significant number of protected areas that has been officially designated at both the national and sub-national levels. This trend signifies a growing recognition of the need to preserve and conserve our natural environment, as well as a commitment to safeguarding critical ecosystems and the diverse species that inhabit them.\n",
+    "\n",
+    "At the national level, governments are taking proactive measures to identify and protect areas of ecological importance, which often involves designating them as official protected areas. These areas are set aside for the explicit purpose of conservation and are subject to specific regulations and management practices to ensure the long-term sustainability of their unique ecosystems. The expansion of such nationally designated protected areas is a positive development, indicating a heightened awareness of the ecological challenges we face and the commitment to address them.\n",
+    "\n",
+    "In conclusion, the growing number of designated or proposed protected areas at both the national and sub-national levels signifies a promising step forward in the global effort to protect our natural heritage. It reflects a deeper understanding of the importance of conservation and underscores the commitment to safeguarding our planet's vital ecosystems for future generations. level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "84eba45f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "status = protected_area['STATUS'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "dfc74cb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "status.plot(kind='bar', color='blue')\n",
+    "plt.xlabel('Status')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of Status')\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility \n",
+    "\n",
+    "# Show the chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f95aa5da",
+   "metadata": {},
+   "source": [
+    "### Observation/findings:\n",
+    "\n",
+    "The increasing number of designated protected areas signifies a growing commitment to conservation efforts, which are officially recognized and formalized through legal mechanisms. These designated protected areas are established with the intention of making a steadfast and enduring commitment to the preservation of our natural environment.\n",
+    "\n",
+    "In essence, a designated protected area is a region or location that has been set aside for the explicit purpose of safeguarding its unique ecological, cultural, or historical attributes. This designation is not merely symbolic but involves a legally binding commitment to ensure the long-term preservation and sustainability of these areas. It entails a series of measures and regulations that are put in place to safeguard the integrity of the ecosystem and the well-being of the species and communities that inhabit it.\n",
+    "\n",
+    "In summary, the growing number of designated protected areas reflects a conscientious effort to protect and preserve our planet's natural and cultural heritage. These areas are not just tokens of conservation; they are legal commitments that ensure the enduring care and sustainability of our planet's most valuable and fragile assets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "1524f99f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count the occurrences of each gov type\n",
+    "gov_type =protected_area['GOV_TYPE'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a2703e94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "gov_type.plot(kind='bar', color='blue')\n",
+    "plt.xlabel('Govt Type')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of Govt Type')\n",
+    "plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility \n",
+    "\n",
+    "# Show the chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d5dc952",
+   "metadata": {},
+   "source": [
+    "### Observation/findings:\n",
+    "\n",
+    "In many countries, the management and oversight of protected areas are typically organized in a hierarchical manner, with various levels of government agencies playing distinct roles in preserving these important natural spaces. At the Federal or national level, a designated National Agency often holds the primary responsibility for managing and conserving protected areas. These National Agencies are entrusted with the task of formulating and implementing policies, regulations, and conservation strategies to ensure the sustainable use and protection of these areas.\n",
+    "\n",
+    "Following the National Agency, sub-national government entities, such as regional, provincial, or municipal agencies, play vital roles in supporting the preservation and management of protected areas. These sub-national agencies, which can vary in their titles and jurisdictions depending on the country's administrative structure, work in close collaboration with the National Agency to enforce conservation measures, monitor ecosystem health, and address regional-specific challenges.\n",
+    "\n",
+    "Regional agencies are typically responsible for managing protected areas that fall within their jurisdiction, which may encompass multiple provinces or states.Provincial or state-level agencies are responsible for implementing conservation efforts and regulations within their respective territories. At the municipal or local level, agencies may oversee protected areas that fall within city or town boundaries. Their responsibilities include day-to-day management, visitor services, and working closely with the community to foster a sense of ownership and responsibility for the protected areas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "3263fa0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the number of records for each unique value in the 'MARINE' column\n",
+    "marine_counts = protected_area['MARINE'].value_counts()\n",
+    "\n",
+    "# Visualize the distribution of marine and terrestrial protected areas\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(marine_counts.index, marine_counts.values, color=['blue', 'green'])\n",
+    "plt.xlabel('MARINE')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of Marine and Terrestrial Protected Areas')\n",
+    "plt.xticks([0, 1], ['Terrestrial', 'Coastal'])\n",
+    "\n",
+    "# Show the bar chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446ee9c5",
+   "metadata": {},
+   "source": [
+    "### Observation/findings\n",
+    "\n",
+    "The analysis shows that majority of protected areas are entirely situated within terrestrial and/or freshwater ecosystems. This finding underscores the significant emphasis placed on safeguarding and preserving the terrestrial and freshwater environments through designated conservation areas. These protected zones play a crucial role in preserving the biodiversity, ecological integrity, and natural resources associated with these specific ecosystems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebd87f6c",
+   "metadata": {},
+   "source": [
+    "### Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9562ce43",
+   "metadata": {},
+   "source": [
+    "In conclusion, the increasing number of designated protected areas signifies a collective dedication to the preservation of our planet's natural and cultural heritage. These areas are not symbolic gestures but legally binding commitments to ensure the enduring care and sustainability of most valuable and fragile assets. The hierarchy of managing the protected at various government levels further contributes to the effective preservation of these areas, and the emphasis on terrestrial and freshwater ecosystems underscores a strategic approach to conservation. This progress marks a promising step in our ongoing effort to protect and sustain our planet's invaluable natural resources for future generations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6c6938",
+   "metadata": {},
+   "source": [
+    "### References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5d3a99",
+   "metadata": {},
+   "source": [
+    "[Observed and projected climate shifts 1901–2100 depicted by world maps of the Koppen-Geiger climate classification ]('http://koeppen-geiger.vu-wien.ac.at/pdf/Paper_2010.pdf')\n",
+    "\n",
+    "[World Map of the Köppen-Geiger climate classification updated]('https://w2.weather.gov/media/jetstream/global/Koppen-Geiger.pdf')\n",
+    "\n",
+    "[Global Road Dataset]('https://sedac.ciesin.columbia.edu/downloads/docs/groads/groads-v1-documentation.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad9c4485",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Task 4/Moja_Project_Task_4.ipynb b/Task 4/Moja_Project_Task_4.ipynb
new file mode 100644
index 000000000..bf17b2a08
--- /dev/null
+++ b/Task 4/Moja_Project_Task_4.ipynb	
@@ -0,0 +1,700 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f2d71032",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing libraries \n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "%matplotlib inline\n",
+    "import json\n",
+    "from matplotlib.lines import Line2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "08bbfa1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load the json file\n",
+    "with open (\"C:/Users/adere/Downloads/Global Roads Open Access Data Set.json.geojson\") as file:\n",
+    "    road = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d4cac7b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create GeoDataFrame from the road data\n",
+    "Global_road = gpd.GeoDataFrame.from_features(road['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4e552729",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ONME</th>\n",
+       "      <th>FCLASS</th>\n",
+       "      <th>SRFTPE</th>\n",
+       "      <th>ISSEASONAL</th>\n",
+       "      <th>CURNTPRAC</th>\n",
+       "      <th>GDWTHRPRAC</th>\n",
+       "      <th>SUM_LENGTH_KM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LINESTRING (-66.82451 17.98029, -66.82455 17.9...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LINESTRING (-66.62012 17.98131, -66.62126 17.9...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16686.65607</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  OBJECTID  ONME  FCLASS  \\\n",
+       "0  LINESTRING (-66.82451 17.98029, -66.82455 17.9...         1  None       0   \n",
+       "1  LINESTRING (-66.62012 17.98131, -66.62126 17.9...         2  None       0   \n",
+       "\n",
+       "   SRFTPE  ISSEASONAL  CURNTPRAC  GDWTHRPRAC  SUM_LENGTH_KM  \n",
+       "0     0.0         0.0        NaN         NaN    16686.65607  \n",
+       "1     0.0         0.0        NaN         NaN    16686.65607  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ff78b3e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Define a mapping function for road data\n",
+    "def map_road_to_sdg(x):\n",
+    "    if x['FCLASS'] != '':\n",
+    "        return('SDG 11 - Sustainable Cities and Communities')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d806ef31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply the mapping function to the dataset\n",
+    "Global_road['Sustainable Cities and Communities'] = Global_road.apply(map_road_to_sdg, axis=1)\n",
+    "\n",
+    "# Create a bar chart to visualize the mapped data\n",
+    "sdg_counts = Global_road['Sustainable Cities and Communities'].value_counts()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sdg_counts.plot(kind='bar', color=['blue', 'green'])\n",
+    "plt.title('GLOBAL ROAD MAPPING TO SDG')\n",
+    "plt.xlabel('SDG Categories')\n",
+    "plt.ylabel('Number of Features')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6ab5e31b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SRFTPE\n",
+       "0.0    966028\n",
+       "1.0    101677\n",
+       "2.0      8457\n",
+       "3.0      7504\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Global_road['SRFTPE'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "acfec943",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sample road data with 'SRFTPE' values as floats\n",
+    "Global_road = [{'SRFTPE': 0.0}, {'SRFTPE': 1.0}, {'SRFTPE': 2.0}, {'SRFTPE':3.0}]\n",
+    "\n",
+    "# Define the mapping function\n",
+    "def map_road_to_sdg(x):\n",
+    "    if x['SRFTPE'] == 0.0:\n",
+    "        return 'Not mapped'\n",
+    "    else:\n",
+    "        return 'SDG 11 - Sustainable Cities and Communities'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b2267d71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply the mapping function to the road data\n",
+    "mapped_data = [map_road_to_sdg(item) for item in Global_road]\n",
+    "\n",
+    "# Count the occurrences of each category\n",
+    "counts = {}\n",
+    "for item in mapped_data:\n",
+    "    counts[item] = counts.get(item, 0) + 1\n",
+    "\n",
+    "# Extract labels and values for the pie chart\n",
+    "labels = list(counts.keys())\n",
+    "values = list(counts.values())\n",
+    "\n",
+    "# Create a pie chart\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=140)\n",
+    "plt.title(\"Mapping Surface Type Road Attribute to SDG 11\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00a09779",
+   "metadata": {},
+   "source": [
+    "**Paved**: Paved roads are typically associated with urban and suburban areas, and they contribute to safer and more accessible transportation in cities. The paved road entry is relevant to SDG 11 as they can improve mobility and accessibility in urban areas.\n",
+    "\n",
+    "**Gravel**: Gravel roads may also be relevant, especially in areas where paving is not practical due to various reasons. Improving gravel roads can enhance transportation infrastructure in rural or peri-urban areas, which aligns with the goal of making settlements more accessible and sustainable.\n",
+    "\n",
+    "**Dirt/Sand**: Dirt or sand roads may be relevant in rural or informal settlement contexts. Enhancing these roads can improve access to essential services in such areas, which is a part of making settlements more inclusive and sustainable.\n",
+    "\n",
+    "**Unspecified**: The \"Unspecified\" category doesn't provide specific information about the road material, so its relevance to SDG 11 would depend on the actual road material used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "51c64a84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read and load json file\n",
+    "with open(\"C:/Users/adere/Downloads/IPCC_ClimateZoneMap_Vector.geojson\") as file:\n",
+    "    climate = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "feb76586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create GeoDataFrame from the climate data\n",
+    "Climate_data= gpd.GeoDataFrame.from_features(climate['features'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0af79a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>CLASS_NAME</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MULTIPOLYGON (((-37.65000 83.50000, -37.65000 ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MULTIPOLYGON (((-38.75000 83.40000, -38.75000 ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  CLASS_NAME\n",
+       "0  MULTIPOLYGON (((-37.65000 83.50000, -37.65000 ...           0\n",
+       "1  MULTIPOLYGON (((-38.75000 83.40000, -38.75000 ...           0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Climate_data.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4a40c0d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the mapping function for climate\n",
+    "def map_climate_to_sdg(x):\n",
+    "    if x['CLASS_NAME'] != '':\n",
+    "        return 'Climate Action'\n",
+    "\n",
+    "# Apply the mapping function to the dataset\n",
+    "Climate_data['Climate Action'] = Climate_data.apply(map_climate_to_sdg, axis=1)\n",
+    "\n",
+    "# Create a bar chart to visualize the mapped data\n",
+    "count_sdg = Climate_data['Climate Action'].value_counts()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "count_sdg.plot(kind='bar', color=['red'])\n",
+    "plt.title('MAPPING CLIMATE TO SDG 13')\n",
+    "plt.xlabel('SDG Categories')\n",
+    "plt.ylabel('Number of Features')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d332cfa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the file\n",
+    "file_path = r\"C:/Users/adere/Downloads/WDPA_WDOECM_wdpa_gdb_polygons/WDPA_WDOECM_wdpa_gdb_polygons.shp\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b12d90ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Read geospatial data from the specified file path using GeoPandas\n",
+    "protected_area = gpd.read_file(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1f5602f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WDPAID</th>\n",
+       "      <th>WDPA_PID</th>\n",
+       "      <th>PA_DEF</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>ORIG_NAME</th>\n",
+       "      <th>DESIG</th>\n",
+       "      <th>DESIG_ENG</th>\n",
+       "      <th>DESIG_TYPE</th>\n",
+       "      <th>IUCN_CAT</th>\n",
+       "      <th>INT_CRIT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MANG_AUTH</th>\n",
+       "      <th>MANG_PLAN</th>\n",
+       "      <th>VERIF</th>\n",
+       "      <th>METADATAID</th>\n",
+       "      <th>SUB_LOC</th>\n",
+       "      <th>PARENT_ISO</th>\n",
+       "      <th>ISO3</th>\n",
+       "      <th>SUPP_INFO</th>\n",
+       "      <th>CONS_OBJ</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10715.0</td>\n",
+       "      <td>10715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>Kronotskiy</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-KAM</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>209777.0</td>\n",
+       "      <td>209777_E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sarali Land between Rivers / Great Volzhsko-Ka...</td>\n",
+       "      <td>Great Volzhsko-Kamsky</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
+       "      <td>International</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>Not Reported</td>\n",
+       "      <td>State Verified</td>\n",
+       "      <td>840</td>\n",
+       "      <td>RU-TA</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>RUS</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>Not Applicable</td>\n",
+       "      <td>POLYGON ((49.30487 55.36806, 49.30433 55.37184...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     WDPAID  WDPA_PID PA_DEF  \\\n",
+       "0   10715.0     10715      1   \n",
+       "1  209777.0  209777_E      1   \n",
+       "\n",
+       "                                                NAME              ORIG_NAME  \\\n",
+       "0                                         Kronotskiy             Kronotskiy   \n",
+       "1  Sarali Land between Rivers / Great Volzhsko-Ka...  Great Volzhsko-Kamsky   \n",
+       "\n",
+       "                          DESIG                     DESIG_ENG     DESIG_TYPE  \\\n",
+       "0  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "1  UNESCO-MAB Biosphere Reserve  UNESCO-MAB Biosphere Reserve  International   \n",
+       "\n",
+       "         IUCN_CAT        INT_CRIT  ...     MANG_AUTH     MANG_PLAN  \\\n",
+       "0  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "1  Not Applicable  Not Applicable  ...  Not Reported  Not Reported   \n",
+       "\n",
+       "            VERIF  METADATAID  SUB_LOC PARENT_ISO  ISO3       SUPP_INFO  \\\n",
+       "0  State Verified         840   RU-KAM        RUS   RUS  Not Applicable   \n",
+       "1  State Verified         840    RU-TA        RUS   RUS  Not Applicable   \n",
+       "\n",
+       "         CONS_OBJ                                           geometry  \n",
+       "0  Not Applicable  MULTIPOLYGON (((160.49655 55.17709, 160.49907 ...  \n",
+       "1  Not Applicable  POLYGON ((49.30487 55.36806, 49.30433 55.37184...  \n",
+       "\n",
+       "[2 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "59040753",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MARINE\n",
+       "0    228942\n",
+       "1     11223\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area['MARINE'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e5989788",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GIS_M_AREA\n",
+       "0.000000     221916\n",
+       "0.046104          5\n",
+       "0.001419          3\n",
+       "0.005168          3\n",
+       "0.666337          3\n",
+       "              ...  \n",
+       "6.686881          1\n",
+       "14.291963         1\n",
+       "76.304367         1\n",
+       "0.078364          1\n",
+       "0.004578          1\n",
+       "Name: count, Length: 17703, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "protected_area['GIS_M_AREA'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "41ebfef7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping function for climate\n",
+    "def map_protected_area_to_sdg(x):\n",
+    "    if x['MARINE'] == 0:\n",
+    "        return 'Not mapped'\n",
+    "    else:\n",
+    "        return 'SDG 13-Climate Action'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "69ec1369",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5rsXmzZuVmJiouXPnys/PTx06dLjqmYGnT5/W+fPnFRgYaHk/JenZs6fT/caNG8tms+nee+91LKtUqZIaNGjg9DMqSZ988onatWunKlWqqFKlSvLw8ND06dO1f//+a3ruL774Qh06dFBISIjT+7LgudevX1/i4w8fPqy1a9fqwQcfVNWqVSVJDz/8sPz8/Jx2B27evFnZ2dkaNGiQZWcqr1mzRpGRkfrTn/7ktDwuLk7GGK1Zs8Zp+dV+Tn8rMDBQP//8syV9/hEQrHBd2Ww2Pf7445o7d66mTZumW2+9VXfffXeRtdu2bXMcd/HBBx/o66+/1vbt2zVy5EhJ0oULF5zqg4ODC62jYFnB8TBXq72yrijVq1cvtMzLy8upn4yMDAUFBRWqK2rZlUo6dickJET5+fnKzMy86nqupn///lq6dKkmTZokb29vPfTQQ0XWHT16VHfffbd+/vln/ec//9HGjRu1fft2x/EcV34fijvmqDhXbk8vLy+n9Z44cUKSNHz4cKdA4eHhoUGDBkmSfvnll1I9pyRlZmbKGFPsdpYKv2+KEhQUpMcff1zTpk3T7t27tX79enl6eurvf//7Nfdy7tw5ZWRkOJ43NDRUUtF/tNatW6ft27dr2rRp17z+0rrzzjvVunVrPfbYY1q7dq2MMfrf//3fEh9T8P2qXLmy0/KCf0IOHz58XXqtVq2a031PT0/5+PgU6sPT01MXL1503F+0aJHj0g9z585VYmKi4x+939aV5MSJE1q6dGmh92WTJk0kXf19OWPGDBlj9NBDD+n06dM6ffq0Ll26pN69e+vrr792XE7j5MmTkqQ6depcU1/XIiMjo1Tv/av9nP5W5cqVi1x+s7r60YnA7xQXF6eXX35Z06ZN0+uvv15s3YIFC+Th4aEvvvjC6ZdkcdeTSUtLK3bZlb8UiqstKjSVRfXq1R2B4GrPW9RjJSk1NbXQ2PHjx+Xm5qaAgIDf3eODDz6oZ555RuPGjdOAAQPk7e1dZN2SJUt07tw5LVq0SGFhYY7lxV0vy+prfxXM+o0YMUIPPvhgkTURERGlXm9AQIDc3NyK3c6/fe7SuOeee9S1a1ctWbJE6enp1zSDs2zZMuXl5TkOig4JCVGTJk2UkJCgixcvOr3/b7/9dknS2bNnS91bWfj5+alRo0aFZiavVPC+PXXqlNPyWrVqqWnTplq5cqXOnz8vHx+f69ZracydO1f16tXTwoULnd6zOTk517yOGjVqqFmzZsX+HisIKUXJz893HIxf3Pt6xowZGj9+vOMEgmPHjl1zb1dTvXp1y9/7BU6dOvW7Hv9Hw4wVrrvatWvrueeeU69evdSvX79i62w2mypVquQ0/XzhwgXNmTOnyPq9e/dq165dTsvmzZsnPz8/3XnnnU7L58+f77Tr5aefftLmzZstu4BldHS01qxZ4/Qfa35+vj755JOrPjYiIkK1a9fWvHnznHo8d+6cPv30U8eZgr+Xt7e3Xn75ZfXq1UtPP/10sXUFf3QK/kOVft1t9cEHH/zuHq5FRESEGjZsqF27dqlFixZF3vz8/Ip9fHH/Wfv6+qpVq1ZatGiR01h+fr7mzp2rOnXq6NZbby12vSdOnCjyQoh5eXk6dOiQfHx8HLt3SnL06FENHz5cdrtdAwcOdCwfOXKkfvnlF8XHx1/zbsLr4ZdfftGePXvUoEGDEus8PT1Vv359/fDDD4XGXnrpJWVmZmrIkCFFvpazZ89q5cqVlvV8LWw2mzw9PZ1CVVpaWqGzAqXCM9IFevbsqeTkZN1yyy1Fvi9LClZfffWVjh07pmeeeUZr164tdGvSpIlmz56ty5cvq23btrLb7Zo2bVqJ74WSZpGu1KlTJ+3bt6/QYRKzZ8+WzWZThw4drrqO4vz444+KjIws8+P/aJixwg0xbty4q9b06NFDkyZNUkxMjJ588kllZGRowoQJTn/gfyskJES9e/fWqFGjVKtWLc2dO1cJCQl64403CgWR9PR0/fnPf9aAAQOUlZWlV155RZUrV9aIESMseX0jR47U0qVL1alTJ40cOVLe3t6aNm2a4+rSJZ2K7+bmpvHjx+uxxx5Tz549NXDgQOXk5OjNN9/U6dOnr2nbXav4+HjFx8eXWNOlSxd5enrq0Ucf1fPPP6+LFy/q3XfftWR35LV67733dO+996pbt26Ki4tT7dq1derUKe3fv1/ffvttiYHVz89PYWFh+uyzz9SpUydVq1ZNNWrUUHh4uMaOHasuXbqoQ4cOGj58uDw9PfXOO+8oOTlZ8+fPL3H2bc6cOXrvvfcUExOjli1bym6369ixY/rwww+1d+9evfzyy/L09HR6THJysuM4nPT0dG3cuFEzZ86Uu7u7Fi9e7JiZkH69PMnevXv1+uuva9euXYqLi1PDhg2Vn5+vlJQUxz8YJYXKAl9++aXOnTunM2fOSJL27dun//73v5Kk++67Tz4+PsrKylKXLl0UExOjhg0bytvbW999953+85//KCcnR6+88spVn6d9+/b68ssvCy1/+OGH9dJLL+m1117TgQMH1L9/f91yyy06f/68tm7dqvfee099+vQp8ZILVuvZs6cWLVqkQYMG6aGHHlJKSopee+011apVS4cOHXKqbdq0qdatW6elS5eqVq1a8vPzU0REhEaPHq2EhAS1bdtWQ4YMUUREhC5evKgjR45o+fLlmjZtWrG776ZPn65KlSrpf//3f4sMYAMHDtSQIUO0bNky3X///Zo4caKeeOIJde7cWQMGDFBQUJC+//577dq1S1OnTnX0KUlvvPGG7r33Xrm7u6tZs2aF3oeS9I9//EOzZ89Wjx49NHr0aIWFhWnZsmV655139PTTT5f4T0VJMjIydOjQIT377LNlevwfkosOmscf2LWcEWVM0WcFzpgxw0RERBgvLy9Tv359M3bsWDN9+vRCZ9aFhYWZHj16mP/+97+mSZMmxtPT04SHh5tJkyY5ra/gzLg5c+aYIUOGmJo1axovLy9z9913m2+++captrizAnv06FGo9yvPeDPGmI0bN5pWrVoZLy8vExwcbJ577jnzxhtvGEmFTlEuypIlS0yrVq1M5cqVja+vr+nUqVOhCweW9azAkhR1VuDSpUvNbbfdZipXrmxq165tnnvuOfPll18aSWbt2rWOuujoaNOkSZMi11vcWYG/vRxGARVx5tquXbvMI488YgIDA42Hh4cJDg42HTt2LHQxyqKsWrXK3HHHHY6z3X57dtTGjRtNx44dja+vr/H29jatW7c2S5cuveo69+3bZ4YNG2ZatGhhatasaSpVqmQCAgJMdHS0mTNnjlNtwc9Awc3T09MEBgaa6OhoM2bMmBKvKL5hwwbTp08fU6dOHePh4WF8fHxMZGSkefrppwu9Z4sTFhbm9Py/vRX8HF28eNE88cQTpnHjxqZKlSqmUqVKpk6dOqZv377XfMX81atXG0lm27ZtRY6vX7/ePPTQQ6ZWrVrGw8PD+Pv7mzZt2pg333zTZGdnO/V7rWcF/vbMW2OM6devn/H19S303EW9N8eNG2fCw8ONl5eXady4sfnggw+K/LlPSkoy7dq1Mz4+PkaS0/v45MmTZsiQIaZevXrGw8PDVKtWzTRv3tyMHDnSnD17tsjtcPLkSePp6WkeeOCBIseNMSYzM9N4e3ubXr16OZYtX77cREdHG19fX8f74I033nCM5+TkmCeeeMLUrFnT2Gw2p+9vURdd/emnn0xMTIypXr268fDwMBEREebNN990Osu2tD+n06dPNx4eHk4Xu73Z2Yxx4ZwzUEbh4eGKiorSF198UWLdunXr1KFDB33yySfFHqx9PXXt2lVHjhy56vEqQEXVrFkztWvXTu+++66rW4EL3H333apbt64+/vhjV7dSbrArELBIfHy87rjjDoWGhurUqVP6+OOPlZCQoOnTp7u6NeC6GT9+vP785z9r5MiRlp7FhvJvw4YN2r59uz766CNXt1KuEKwAi+Tl5enll19WWlqabDabIiMjNWfOHPXt29fVrQHXTffu3fXmm2/q8OHDBKubTEZGhmbPnq369eu7upVyhV2BAAAAFuFyCwAAABYhWAEAAFiEY6xusPz8fB0/flx+fn6WX7EaAABcH8YYnTlzRiEhISVem9Cl17EaM2aMadGihalSpYqpWbOmuf/++82BAwecavr161foOixXfor2xYsXzeDBg0316tWNj4+P6dWrl0lJSXGqOXXqlOnbt6/x9/c3/v7+pm/fvo5PFy/w008/mZ49exofHx9TvXp18+yzz5qcnBynmt27d5t77rnHVK5c2YSEhJhXX321VJ82n5KSUuz1Zbhx48aNGzdu5ft2Zb64kktnrNavX69nnnlGLVu21OXLlzVy5Eh17dpV+/btk6+vr6Oue/fumjlzpuP+lVeVHTp0qJYuXaoFCxaoevXqGjZsmHr27KkdO3Y4Ph4lJiZGx44d04oVKyRJTz75pGJjY7V06VJJv57R1aNHD9WsWVObNm1SRkaG+vXrJ2OMpkyZIknKzs52XLV5+/bt+u677xQXFydfX18NGzbsml5zwVWTU1JS5O/vX8YtBwAAbqTs7GyFhoZe/dMPrnmq5QZIT083ksz69esdy/r162fuv//+Yh9z+vRp4+HhYRYsWOBY9vPPPxs3NzezYsUKY8yvV0yWZLZs2eKoSUxMNJIcM2TLly83bm5u5ueff3bUzJ8/33h5eZmsrCxjjDHvvPOOsdvt5uLFi46asWPHmpCQkGuetcrKyjKSHOsEAADl37X+/S5XB69nZWVJkqpVq+a0fN26dQoMDNStt96qAQMGKD093TG2Y8cOXbp0yekzp0JCQhQVFaXNmzdLkhITE2W329WqVStHTevWrWW3251qoqKinD7DqVu3bsrJydGOHTscNdHR0U6fXdetWzcdP35cR44cKfI15eTkKDs72+kGAAD+mMpNsDLGKD4+XnfddZeioqIcy++99159/PHHWrNmjSZOnKjt27erY8eOysnJkfTrp5N7enoqICDAaX1BQUFKS0tz1AQGBhZ6zsDAQKeaoKAgp/GAgAB5enqWWFNwv6DmSmPHjpXdbnfcQkNDr3mbAACAiqXcnBU4ePBg7d69W5s2bXJa3qdPH8fXUVFRatGiheNTuR988MFi12eMcTrrrqgz8KyoMf/v+qrFneE3YsQIxcfHO+4X7KMFAAB/POVixurZZ5/V559/rrVr1171IxFq1aqlsLAwHTp0SJIUHBys3NxcZWZmOtWlp6c7ZpOCg4N14sSJQus6efKkU82Vs06ZmZm6dOlSiTUFuyWvnMkq4OXlJX9/f6cbAAD4Y3JpsDLGaPDgwVq0aJHWrFmjevXqXfUxGRkZSklJUa1atSRJzZs3l4eHhxISEhw1qampSk5OVtu2bSVJbdq0UVZWlrZt2+ao2bp1q7KyspxqkpOTlZqa6qhZuXKlvLy81Lx5c0fNhg0blJub61QTEhKi8PDwsm8IAADwx3DdD6MvwdNPP23sdrtZt26dSU1NddzOnz9vjDHmzJkzZtiwYWbz5s3m8OHDZu3ataZNmzamdu3aJjs727Gep556ytSpU8esWrXKfPvtt6Zjx47mtttuM5cvX3bUdO/e3TRr1swkJiaaxMRE07RpU9OzZ0/H+OXLl01UVJTp1KmT+fbbb82qVatMnTp1zODBgx01p0+fNkFBQebRRx81e/bsMYsWLTL+/v5mwoQJ1/yaOSsQAICK51r/frs0WKmYi2/NnDnTGGPM+fPnTdeuXU3NmjWNh4eHqVu3runXr585evSo03ouXLhgBg8ebKpVq2a8vb1Nz549C9VkZGSYxx57zPj5+Rk/Pz/z2GOPFXmB0B49ehhvb29TrVo1M3jwYKdLKxjz6wVC7777buPl5WWCg4PNqFGjSnWBUIIVAAAVz7X+/bYZ8/+OvsYNkZ2dLbvdrqysLI63AgCggrjWv9/l4uB1AACAPwKCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWKTcfwgz8UYW/sMzVLQC4To6M6+HqFlDOMGMFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEVcGqzGjh2rli1bys/PT4GBgXrggQd08OBBpxpjjEaNGqWQkBB5e3urffv22rt3r1NNTk6Onn32WdWoUUO+vr7q3bu3jh075lSTmZmp2NhY2e122e12xcbG6vTp0041R48eVa9eveTr66saNWpoyJAhys3NdarZs2ePoqOj5e3trdq1a2v06NEyxli3UQAAQIXl0mC1fv16PfPMM9qyZYsSEhJ0+fJlde3aVefOnXPUjB8/XpMmTdLUqVO1fft2BQcHq0uXLjpz5oyjZujQoVq8eLEWLFigTZs26ezZs+rZs6fy8vIcNTExMUpKStKKFSu0YsUKJSUlKTY21jGel5enHj166Ny5c9q0aZMWLFigTz/9VMOGDXPUZGdnq0uXLgoJCdH27ds1ZcoUTZgwQZMmTbrOWwoAAFQENlOOpltOnjypwMBArV+/Xvfcc4+MMQoJCdHQoUP1z3/+U9Kvs1NBQUF64403NHDgQGVlZalmzZqaM2eO+vTpI0k6fvy4QkNDtXz5cnXr1k379+9XZGSktmzZolatWkmStmzZojZt2ujAgQOKiIjQl19+qZ49eyolJUUhISGSpAULFiguLk7p6eny9/fXu+++qxEjRujEiRPy8vKSJI0bN05TpkzRsWPHZLPZrvoas7OzZbfblZWVJX9//+uxGVHOhL+wzNUtALhOjozr4eoWcINc69/vcnWMVVZWliSpWrVqkqTDhw8rLS1NXbt2ddR4eXkpOjpamzdvliTt2LFDly5dcqoJCQlRVFSUoyYxMVF2u90RqiSpdevWstvtTjVRUVGOUCVJ3bp1U05Ojnbs2OGoiY6OdoSqgprjx4/ryJEjRb6mnJwcZWdnO90AAMAfU7kJVsYYxcfH66677lJUVJQkKS0tTZIUFBTkVBsUFOQYS0tLk6enpwICAkqsCQwMLPScgYGBTjVXPk9AQIA8PT1LrCm4X1BzpbFjxzqO67Lb7QoNDb3KlgAAABVVuQlWgwcP1u7duzV//vxCY1fuYjPGXHW325U1RdVbUVOwJ7W4fkaMGKGsrCzHLSUlpcS+AQBAxVUugtWzzz6rzz//XGvXrlWdOnUcy4ODgyUVng1KT093zBQFBwcrNzdXmZmZJdacOHGi0POePHnSqebK58nMzNSlS5dKrElPT5dUeFatgJeXl/z9/Z1uAADgj8mlwcoYo8GDB2vRokVas2aN6tWr5zRer149BQcHKyEhwbEsNzdX69evV9u2bSVJzZs3l4eHh1NNamqqkpOTHTVt2rRRVlaWtm3b5qjZunWrsrKynGqSk5OVmprqqFm5cqW8vLzUvHlzR82GDRucLsGwcuVKhYSEKDw83KKtAgAAKiqXBqtnnnlGc+fO1bx58+Tn56e0tDSlpaXpwoULkn7dvTZ06FCNGTNGixcvVnJysuLi4uTj46OYmBhJkt1uV//+/TVs2DCtXr1aO3fuVN++fdW0aVN17txZktS4cWN1795dAwYM0JYtW7RlyxYNGDBAPXv2VEREhCSpa9euioyMVGxsrHbu3KnVq1dr+PDhGjBggGOWKSYmRl5eXoqLi1NycrIWL16sMWPGKD4+/prOCAQAAH9slVz55O+++64kqX379k7LZ86cqbi4OEnS888/rwsXLmjQoEHKzMxUq1attHLlSvn5+TnqJ0+erEqVKumRRx7RhQsX1KlTJ82aNUvu7u6Omo8//lhDhgxxnD3Yu3dvTZ061THu7u6uZcuWadCgQWrXrp28vb0VExOjCRMmOGrsdrsSEhL0zDPPqEWLFgoICFB8fLzi4+Ot3jQAAKACKlfXsboZcB2rmw/XsQL+uLiO1c2jQl7HCgAAoCIjWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARlwarDRs2qFevXgoJCZHNZtOSJUucxuPi4mSz2ZxurVu3dqrJycnRs88+qxo1asjX11e9e/fWsWPHnGoyMzMVGxsru90uu92u2NhYnT592qnm6NGj6tWrl3x9fVWjRg0NGTJEubm5TjV79uxRdHS0vL29Vbt2bY0ePVrGGMu2BwAAqNhcGqzOnTun2267TVOnTi22pnv37kpNTXXcli9f7jQ+dOhQLV68WAsWLNCmTZt09uxZ9ezZU3l5eY6amJgYJSUlacWKFVqxYoWSkpIUGxvrGM/Ly1OPHj107tw5bdq0SQsWLNCnn36qYcOGOWqys7PVpUsXhYSEaPv27ZoyZYomTJigSZMmWbhFAABARVbJlU9+77336t577y2xxsvLS8HBwUWOZWVlafr06ZozZ446d+4sSZo7d65CQ0O1atUqdevWTfv379eKFSu0ZcsWtWrVSpL0wQcfqE2bNjp48KAiIiK0cuVK7du3TykpKQoJCZEkTZw4UXFxcXr99dfl7++vjz/+WBcvXtSsWbPk5eWlqKgofffdd5o0aZLi4+Nls9mK7DEnJ0c5OTmO+9nZ2aXeTgAAoGIo98dYrVu3ToGBgbr11ls1YMAApaenO8Z27NihS5cuqWvXro5lISEhioqK0ubNmyVJiYmJstvtjlAlSa1bt5bdbneqiYqKcoQqSerWrZtycnK0Y8cOR010dLS8vLycao4fP64jR44U2//YsWMduyDtdrtCQ0N/3wYBAADlVrkOVvfee68+/vhjrVmzRhMnTtT27dvVsWNHxwxQWlqaPD09FRAQ4PS4oKAgpaWlOWoCAwMLrTswMNCpJigoyGk8ICBAnp6eJdYU3C+oKcqIESOUlZXluKWkpJRmEwAAgArEpbsCr6ZPnz6Or6OiotSiRQuFhYVp2bJlevDBB4t9nDHGaddcUbvprKgpOHC9uN2A0q+7Mn87ywUAAP64yvWM1ZVq1aqlsLAwHTp0SJIUHBys3NxcZWZmOtWlp6c7ZpOCg4N14sSJQus6efKkU82Vs06ZmZm6dOlSiTUFuyWvnMkCAAA3pwoVrDIyMpSSkqJatWpJkpo3by4PDw8lJCQ4alJTU5WcnKy2bdtKktq0aaOsrCxt27bNUbN161ZlZWU51SQnJys1NdVRs3LlSnl5eal58+aOmg0bNjhdgmHlypUKCQlReHj4dXvNAACg4nBpsDp79qySkpKUlJQkSTp8+LCSkpJ09OhRnT17VsOHD1diYqKOHDmidevWqVevXqpRo4b+/Oc/S5Lsdrv69++vYcOGafXq1dq5c6f69u2rpk2bOs4SbNy4sbp3764BAwZoy5Yt2rJliwYMGKCePXsqIiJCktS1a1dFRkYqNjZWO3fu1OrVqzV8+HANGDBA/v7+kn69ZIOXl5fi4uKUnJysxYsXa8yYMSWeEQgAAG4uLj3G6ptvvlGHDh0c9+Pj4yVJ/fr107vvvqs9e/Zo9uzZOn36tGrVqqUOHTpo4cKF8vPzczxm8uTJqlSpkh555BFduHBBnTp10qxZs+Tu7u6o+fjjjzVkyBDH2YO9e/d2unaWu7u7li1bpkGDBqldu3by9vZWTEyMJkyY4Kix2+1KSEjQM888oxYtWiggIEDx8fGOngEAAGyGS4ffUNnZ2bLb7crKynLMhuGPLfyFZa5uAcB1cmRcD1e3gBvkWv9+V6hjrAAAAMozghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEXKFKzq16+vjIyMQstPnz6t+vXr/+6mAAAAKqIyBasjR44oLy+v0PKcnBz9/PPPv7spAACAiqhSaYo///xzx9dfffWV7Ha7435eXp5Wr16t8PBwy5oDAACoSEoVrB544AFJks1mU79+/ZzGPDw8FB4erokTJ1rWHAAAQEVSqmCVn58vSapXr562b9+uGjVqXJemAAAAKqJSBasChw8ftroPAACACq9MwUqSVq9erdWrVys9Pd0xk1VgxowZv7sxAACAiqZMwerVV1/V6NGj1aJFC9WqVUs2m83qvgAAACqcMgWradOmadasWYqNjbW6HwAAgAqrTNexys3NVdu2ba3uBQAAoEIrU7B64oknNG/ePKt7AQAAqNDKtCvw4sWLev/997Vq1So1a9ZMHh4eTuOTJk2ypDkAAICKpEzBavfu3br99tslScnJyU5jHMgOAABuVmUKVmvXrrW6DwAAgAqvTMdYAQAAoLAyzVh16NChxF1+a9asKXNDAAAAFVWZglXB8VUFLl26pKSkJCUnJxf6cGYAAICbRZmC1eTJk4tcPmrUKJ09e/Z3NQQAAFBRWXqMVd++ffmcQAAAcNOyNFglJiaqcuXKVq4SAACgwijTrsAHH3zQ6b4xRqmpqfrmm2/00ksvWdIYAABARVOmYGW3253uu7m5KSIiQqNHj1bXrl0taQwAAKCiKVOwmjlzptV9AAAAVHhlClYFduzYof3798tmsykyMlJ33HGHVX0BAABUOGUKVunp6frrX/+qdevWqWrVqjLGKCsrSx06dNCCBQtUs2ZNq/sEAAAo98p0VuCzzz6r7Oxs7d27V6dOnVJmZqaSk5OVnZ2tIUOGWN0jAABAhVCmGasVK1Zo1apVaty4sWNZZGSk3n77bQ5eBwAAN60yzVjl5+fLw8Oj0HIPDw/l5+f/7qYAAAAqojIFq44dO+rvf/+7jh8/7lj2888/6x//+Ic6depkWXMAAAAVSZmC1dSpU3XmzBmFh4frlltuUYMGDVSvXj2dOXNGU6ZMsbpHAACACqFMx1iFhobq22+/VUJCgg4cOCBjjCIjI9W5c2er+wMAAKgwSjVjtWbNGkVGRio7O1uS1KVLFz377LMaMmSIWrZsqSZNmmjjxo3XpVEAAIDyrlTB6t///rcGDBggf3//QmN2u10DBw7UpEmTLGsOAACgIilVsNq1a5e6d+9e7HjXrl21Y8eO390UAABARVSqYHXixIkiL7NQoFKlSjp58uTvbgoAAKAiKlWwql27tvbs2VPs+O7du1WrVq3f3RQAAEBFVKpgdd999+nll1/WxYsXC41duHBBr7zyinr27GlZcwAAABVJqS638OKLL2rRokW69dZbNXjwYEVERMhms2n//v16++23lZeXp5EjR16vXgEAAMq1UgWroKAgbd68WU8//bRGjBghY4wkyWazqVu3bnrnnXcUFBR0XRoFAAAo70p9gdCwsDAtX75cmZmZ+v7772WMUcOGDRUQEHA9+gMAAKgwynTldUkKCAhQy5YtrewFAACgQivTZwUCAACgMIIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWcWmw2rBhg3r16qWQkBDZbDYtWbLEadwYo1GjRikkJETe3t5q37699u7d61STk5OjZ599VjVq1JCvr6969+6tY8eOOdVkZmYqNjZWdrtddrtdsbGxOn36tFPN0aNH1atXL/n6+qpGjRoaMmSIcnNznWr27Nmj6OhoeXt7q3bt2ho9erSMMZZtDwAAULG5NFidO3dOt912m6ZOnVrk+Pjx4zVp0iRNnTpV27dvV3BwsLp06aIzZ844aoYOHarFixdrwYIF2rRpk86ePauePXsqLy/PURMTE6OkpCStWLFCK1asUFJSkmJjYx3jeXl56tGjh86dO6dNmzZpwYIF+vTTTzVs2DBHTXZ2trp06aKQkBBt375dU6ZM0YQJEzRp0qTrsGUAAEBFZDPlZMrFZrNp8eLFeuCBByT9OlsVEhKioUOH6p///KekX2engoKC9MYbb2jgwIHKyspSzZo1NWfOHPXp00eSdPz4cYWGhmr58uXq1q2b9u/fr8jISG3ZskWtWrWSJG3ZskVt2rTRgQMHFBERoS+//FI9e/ZUSkqKQkJCJEkLFixQXFyc0tPT5e/vr3fffVcjRozQiRMn5OXlJUkaN26cpkyZomPHjslmsxX5unJycpSTk+O4n52drdDQUGVlZcnf3/+6bEuUL+EvLHN1CwCukyPjeri6Bdwg2dnZstvtV/37XW6PsTp8+LDS0tLUtWtXxzIvLy9FR0dr8+bNkqQdO3bo0qVLTjUhISGKiopy1CQmJsputztClSS1bt1adrvdqSYqKsoRqiSpW7duysnJ0Y4dOxw10dHRjlBVUHP8+HEdOXKk2NcxduxYxy5Iu92u0NDQ37FVAABAeVZug1VaWpokKSgoyGl5UFCQYywtLU2enp4KCAgosSYwMLDQ+gMDA51qrnyegIAAeXp6llhTcL+gpigjRoxQVlaW45aSklLyCwcAABVWJVc3cDVX7mIzxhS72624mqLqragp2ItaUj9eXl5Os1wAAOCPq9zOWAUHB0sqPBuUnp7umCkKDg5Wbm6uMjMzS6w5ceJEofWfPHnSqebK58nMzNSlS5dKrElPT5dUeFYNAADcnMptsKpXr56Cg4OVkJDgWJabm6v169erbdu2kqTmzZvLw8PDqSY1NVXJycmOmjZt2igrK0vbtm1z1GzdulVZWVlONcnJyUpNTXXUrFy5Ul5eXmrevLmjZsOGDU6XYFi5cqVCQkIUHh5u/QYAAAAVjkuD1dmzZ5WUlKSkpCRJvx6wnpSUpKNHj8pms2no0KEaM2aMFi9erOTkZMXFxcnHx0cxMTGSJLvdrv79+2vYsGFavXq1du7cqb59+6pp06bq3LmzJKlx48bq3r27BgwYoC1btmjLli0aMGCAevbsqYiICElS165dFRkZqdjYWO3cuVOrV6/W8OHDNWDAAMeR/zExMfLy8lJcXJySk5O1ePFijRkzRvHx8VfdNQkAAG4OLj3G6ptvvlGHDh0c9+Pj4yVJ/fr106xZs/T888/rwoULGjRokDIzM9WqVSutXLlSfn5+jsdMnjxZlSpV0iOPPKILFy6oU6dOmjVrltzd3R01H3/8sYYMGeI4e7B3795O185yd3fXsmXLNGjQILVr107e3t6KiYnRhAkTHDV2u10JCQl65pln1KJFCwUEBCg+Pt7RMwAAQLm5jtXN4lqvg4E/Dq5jBfxxcR2rm0eFv44VAABARUOwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsEi5DlajRo2SzWZzugUHBzvGjTEaNWqUQkJC5O3trfbt22vv3r1O68jJydGzzz6rGjVqyNfXV71799axY8ecajIzMxUbGyu73S673a7Y2FidPn3aqebo0aPq1auXfH19VaNGDQ0ZMkS5ubnX7bUDAICKp1wHK0lq0qSJUlNTHbc9e/Y4xsaPH69JkyZp6tSp2r59u4KDg9WlSxedOXPGUTN06FAtXrxYCxYs0KZNm3T27Fn17NlTeXl5jpqYmBglJSVpxYoVWrFihZKSkhQbG+sYz8vLU48ePXTu3Dlt2rRJCxYs0Keffqphw4bdmI0AAAAqhEqubuBqKlWq5DRLVcAYo3//+98aOXKkHnzwQUnSRx99pKCgIM2bN08DBw5UVlaWpk+frjlz5qhz586SpLlz5yo0NFSrVq1St27dtH//fq1YsUJbtmxRq1atJEkffPCB2rRpo4MHDyoiIkIrV67Uvn37lJKSopCQEEnSxIkTFRcXp9dff13+/v43aGsAAIDyrNzPWB06dEghISGqV6+e/vrXv+rHH3+UJB0+fFhpaWnq2rWro9bLy0vR0dHavHmzJGnHjh26dOmSU01ISIiioqIcNYmJibLb7Y5QJUmtW7eW3W53qomKinKEKknq1q2bcnJytGPHjhL7z8nJUXZ2ttMNAAD8MZXrYNWqVSvNnj1bX331lT744AOlpaWpbdu2ysjIUFpamiQpKCjI6TFBQUGOsbS0NHl6eiogIKDEmsDAwELPHRgY6FRz5fMEBATI09PTUVOcsWPHOo7dstvtCg0NLcUWAAAAFUm5Dlb33nuv/vKXv6hp06bq3Lmzli1bJunXXX4FbDab02OMMYWWXenKmqLqy1JTlBEjRigrK8txS0lJKbEeAABUXOU6WF3J19dXTZs21aFDhxzHXV05Y5Senu6YXQoODlZubq4yMzNLrDlx4kSh5zp58qRTzZXPk5mZqUuXLhWaybqSl5eX/P39nW4AAOCPqUIFq5ycHO3fv1+1atVSvXr1FBwcrISEBMd4bm6u1q9fr7Zt20qSmjdvLg8PD6ea1NRUJScnO2ratGmjrKwsbdu2zVGzdetWZWVlOdUkJycrNTXVUbNy5Up5eXmpefPm1/U1AwCAiqNcnxU4fPhw9erVS3Xr1lV6err+9a9/KTs7W/369ZPNZtPQoUM1ZswYNWzYUA0bNtSYMWPk4+OjmJgYSZLdblf//v01bNgwVa9eXdWqVdPw4cMduxYlqXHjxurevbsGDBig9957T5L05JNPqmfPnoqIiJAkde3aVZGRkYqNjdWbb76pU6dOafjw4RowYAAzUAAAwKFcB6tjx47p0Ucf1S+//KKaNWuqdevW2rJli8LCwiRJzz//vC5cuKBBgwYpMzNTrVq10sqVK+Xn5+dYx+TJk1WpUiU98sgjunDhgjp16qRZs2bJ3d3dUfPxxx9ryJAhjrMHe/furalTpzrG3d3dtWzZMg0aNEjt2rWTt7e3YmJiNGHChBu0JQAAQEVgM8YYVzdxM8nOzpbdbldWVhazXTeJ8BeWuboFANfJkXE9XN0CbpBr/ftdoY6xAgAAKM8IVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWJXBO++8o3r16qly5cpq3ry5Nm7c6OqWAABAOUCwKqWFCxdq6NChGjlypHbu3Km7775b9957r44ePerq1gAAgIsRrEpp0qRJ6t+/v5544gk1btxY//73vxUaGqp3333X1a0BAAAXq+TqBiqS3Nxc7dixQy+88ILT8q5du2rz5s1FPiYnJ0c5OTmO+1lZWZKk7Ozs69coypX8nPOubgHAdcLv8ptHwffaGFNiHcGqFH755Rfl5eUpKCjIaXlQUJDS0tKKfMzYsWP16quvFloeGhp6XXoEANw49n+7ugPcaGfOnJHdbi92nGBVBjabzem+MabQsgIjRoxQfHy8435+fr5OnTql6tWrF/sYABVTdna2QkNDlZKSIn9/f1e3A8BCxhidOXNGISEhJdYRrEqhRo0acnd3LzQ7lZ6eXmgWq4CXl5e8vLycllWtWvV6tQigHPD39ydYAX9AJc1UFeDg9VLw9PRU8+bNlZCQ4LQ8ISFBbdu2dVFXAACgvGDGqpTi4+MVGxurFi1aqE2bNnr//fd19OhRPfXUU65uDQAAuBjBqpT69OmjjIwMjR49WqmpqYqKitLy5csVFhbm6tYAuJiXl5deeeWVQrv/Adw8bOZq5w0CAADgmnCMFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFuNwCAPwOq1ev1urVq5Wenq78/HynsRkzZrioKwCuQrACgDJ69dVXNXr0aLVo0UK1atXi8z8BcB0rACirWrVqafz48YqNjXV1KwDKCY6xAoAyys3N5XNCATghWAFAGT3xxBOaN2+eq9sAUI5wjBUAlNHFixf1/vvva9WqVWrWrJk8PDycxidNmuSizgC4CsdYAUAZdejQodgxm82mNWvW3MBuAJQHBCsAAACLcIwVAFjg2LFj+vnnn13dBgAXI1gBQBnl5+dr9OjRstvtCgsLU926dVW1alW99tprhS4WCuDmwMHrAFBGI0eO1PTp0zVu3Di1a9dOxhh9/fXXGjVqlC5evKjXX3/d1S0CuME4xgoAyigkJETTpk1T7969nZZ/9tlnGjRoELsGgZsQuwIBoIxOnTqlRo0aFVreqFEjnTp1ygUdAXA1ghUAlNFtt92mqVOnFlo+depU3XbbbS7oCICrsSsQAMpo/fr16tGjh+rWras2bdrIZrNp8+bNSklJ0fLly3X33Xe7ukUANxjBCgB+h+PHj+vtt9/WgQMHZIxRZGSkBg0apJCQEFe3BsAFCFYAAAAW4XILAFAKu3fvVlRUlNzc3LR79+4Sa5s1a3aDugJQXjBjBQCl4ObmprS0NAUGBsrNzU02m01F/Rq12WzKy8tzQYcAXIkZKwAohcOHD6tmzZqOrwHgtwhWAFAKYWFhjq9/+ukntW3bVpUqOf8qvXz5sjZv3uxUC+DmwK5AACgjd3d3paamKjAw0Gl5RkaGAgMD2RUI3IS4QCgAlJExRjabrdDyjIwM+fr6uqAjAK7GrkAAKKUHH3xQ0q8HqMfFxcnLy8sxlpeXp927d6tt27auag+ACxGsAKCU7Ha7pF9nrPz8/OTt7e0Y8/T0VOvWrTVgwABXtQfAhTjGCgDK6NVXX9Vzzz0nHx8fV7cCoJwgWAFAGR0+fFiXL19Ww4YNnZYfOnRIHh4eCg8Pd01jAFyGg9cBoIzi4uK0efPmQsu3bt2quLi4G98QAJdjxgoAysjf31/ffvutGjRo4LT8+++/V4sWLXT69GnXNAbAZZixAoAystlsOnPmTKHlWVlZXMMKuEkxYwUAZdSzZ0/5+Pho/vz5cnd3l/Tr5Rb69Omjc+fO6csvv3RxhwBuNIIVAJTRvn37dM8996hq1aq6++67JUkbN25Udna21qxZo6ioKBd3COBGI1gBwO9w/PhxTZ06Vbt27ZK3t7eaNWumwYMHq1q1aq5uDYALEKwAwGIZGRmaM2eOhg4d6upWANxgBCsAsIAxRitXrtT06dP12Wefyd/fXydPnnR1WwBuMM4KBIDf4ciRI3r55ZcVFham++67T15eXlq2bJnS0tJc3RoAFyBYAUAp5eTkaP78+erUqZMaN26s5ORkTZo0SW5ubhoxYoQ6d+7sOEsQwM2FD2EGgFKqXbu2IiMj1bdvX/33v/9VQECAJOnRRx91cWcAXI0ZKwAopby8PNlsNtlsNmamADghWAFAKaWmpurJJ5/U/PnzFRwcrL/85S9avHixbDabq1sD4GKcFQgAv8MPP/ygmTNn6qOPPtLPP/+sRx99VHFxcerYsSOzWcBNiGAFABbIz8/XV199penTp2vp0qXy8/PTL7/84uq2ANxgBCsAsNjJkyc1Z84cxcfHu7oVADcYwQoAAMAiHLwOAABgEYIVAACARQhWAAAAFiFYAcDvlJubq4MHD+ry5cuubgWAixGsAKCMzp8/r/79+8vHx0dNmjTR0aNHJUlDhgzRuHHjXNwdAFcgWAFAGY0YMUK7du3SunXrVLlyZcfyzp07a+HChS7sDICr8CHMAFBGS5Ys0cKFC9W6dWunj7OJjIzUDz/84MLOALgKM1YAUEYnT55UYGBgoeXnzp3jcwOBmxTBCgDKqGXLllq2bJnjfkGY+uCDD9SmTRtXtQXAhdgVCABlNHbsWHXv3l379u3T5cuX9Z///Ed79+5VYmKi1q9f7+r2ALgAM1YAUEZt27bV119/rfPnz+uWW27RypUrFRQUpMTERDVv3tzV7QFwAT4rEAAAwCLMWAFAGbm7uys9Pb3Q8oyMDLm7u7ugIwCuRrACgDIqbsI/JydHnp6eN7gbAOUBB68DQCm99dZbkn49C/DDDz9UlSpVHGN5eXnasGGDGjVq5Kr2ALgQx1gBQCnVq1dPkvTTTz+pTp06Trv9PD09FR4ertGjR6tVq1auahGAixCsAKCMOnTooEWLFikgIMDVrQAoJwhWAAAAFuEYKwD4HY4dO6bPP/9cR48eVW5urtPYpEmTXNQVAFchWAFAGa1evVq9e/dWvXr1dPDgQUVFRenIkSMyxujOO+90dXsAXIDLLQBAGY0YMULDhg1TcnKyKleurE8//VQpKSmKjo7Www8/7Or2ALgAx1gBQBn5+fkpKSlJt9xyiwICArRp0yY1adJEu3bt0v33368jR464ukUANxgzVgBQRr6+vsrJyZEkhYSE6IcffnCM/fLLL65qC4ALcYwVAJRR69at9fXXXysyMlI9evTQsGHDtGfPHi1atEitW7d2dXsAXIBdgQBQRj/++KPOnj2rZs2a6fz58xo+fLg2bdqkBg0aaPLkyQoLC3N1iwBuMIIVAACARdgVCAAWOHv2rPLz852W+fv7u6gbAK7CwesAUEaHDx9Wjx495OvrK7vdroCAAAUEBKhq1ap8zA1wk2LGCgDK6LHHHpMkzZgxQ0FBQbLZbC7uCICrcYwVAJRRlSpVtGPHDkVERLi6FQDlBLsCAaCMWrZsqZSUFFe3AaAcYVcgAJTRhx9+qKeeeko///yzoqKi5OHh4TTerFkzF3UGwFUIVgBQRidPntQPP/ygxx9/3LHMZrPJGCObzaa8vDwXdgfAFTjGCgDKKDIyUo0bN9bzzz9f5MHrXCAUuPkQrACgjHx9fbVr1y41aNDA1a0AKCc4eB0Ayqhjx47atWuXq9sAUI5wjBUAlFGvXr30j3/8Q3v27FHTpk0LHbzeu3dvF3UGwFXYFQgAZeTmVvykPwevAzcnghUAAIBFOMYKAADAIhxjBQCl8NZbb+nJJ59U5cqV9dZbb5VYO2TIkBvUFYDygl2BAFAK9erV0zfffKPq1aurXr16xdbZbDb9+OOPN7AzAOUBwQoAAMAiHGMFAABgEY6xAoBSiI+Pv+baSZMmXcdOAJRHBCsAKIWdO3deU92VnxsI4ObAMVYAAAAW4RgrACilvLw87d69WxcuXCg0dv78ee3evVv5+fku6AyAqxGsAKCU5syZo7/97W/y9PQsNObl5aW//e1vmjdvngs6A+BqBCsAKKXp06dr+PDhcnd3LzTm7u6u559/Xu+//74LOgPgagQrACilgwcPqnXr1sWOt2zZUvv377+BHQEoLwhWAFBK586dU3Z2drHjZ86c0fnz529gRwDKC4IVAJRSw4YNtXnz5mLHN23apIYNG97AjgCUFwQrACilmJgYvfjii9q9e3ehsV27dunll19WTEyMCzoD4GpcxwoASunSpUvq2rWrNm3apM6dO6tRo0ay2Wzav3+/Vq1apXbt2ikhIUEeHh6ubhXADUawAoAyuHTpkiZPnqx58+bp0KFDMsbo1ltvVUxMjIYOHVrkpRgA/PERrAAAACzCMVYAYIFBgwbpl19+cXUbAFyMGSsAsIC/v7+SkpJUv359V7cCwIWYsQIAC/A/KgCJYAUAAGAZdgUCAABYhBkrALDIiRMndPToUVe3AcCFCFYAUEpnzpxR3759FRYWpn79+ik3N1fPPPOMatWqpXr16ik6OrrEzxIE8MdFsAKAUvrf//1f7dixQ8OHD9fRo0f1yCOPaMOGDdq4caPWrVunU6dO6Y033nB1mwBcgGOsAKCU6tatq48++kgdOnTQ8ePHVadOHX322Wfq1auXJGn58uWKj4/XgQMHXNwpgBuNGSsAKKX09HQ1aNBAkhQSEiJvb29FREQ4xps0aaKUlBRXtQfAhQhWAFBK1atX18mTJx3377//flWtWtVx/+zZs/Ly8nJBZwBcjWAFAKXUrFkzbd++3XF/3rx5CgwMdNzfvn27Gjdu7IrWALgYx1gBQCmdOnVKbm5uTrNUv/Xll1/K29tb7du3v6F9AXA9ghUAAIBF2BUIABY7d+6cNmzY4Oo2ALgAwQoALPb999+rQ4cOrm4DgAsQrAAAACxSydUNAEBFU61atRLH8/LyblAnAMobghUAlFJOTo6efvppNW3atMjxn376Sa+++uoN7gpAeUCwAoBSuv322xUaGqp+/foVOb5r1y6CFXCT4hgrACilHj166PTp08WOV6tWTf/zP/9z4xoCUG5wHSsAAACLMGMFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGuYwUAv9OZM2f02xOs3dzcVKVKFRd2BMBVmLECgFJKSkpSjx49HPdDQkIUEBDguFWtWlXbt293YYcAXIUZKwAopSlTpuiuu+5yWjZnzhzVrl1bxhjNmDFDb731lubMmeOiDgG4CsEKAErp66+/VlxcnNOy1q1bq379+pIkb29vPfLIIy7oDICrsSsQAEopJSVFdevWddwfPXq0atSo4bhfq1YtnThxwhWtAXAxghUAlJKXl5eOHTvmuP+Pf/xD/v7+jvspKSny8fFxRWsAXIxgBQCldMcdd2jJkiXFji9atEh33HHHjWsIQLnBMVYAUEqDBg3SX//6V4WHh+vpp5+Wm9uv/6Pm5eXpnXfe0ZQpUzRv3jwXdwnAFWzmtxdfAQBck3/+859688035efnp/r168tms+mHH37Q2bNnFR8frzfffNPVLQJwAYIVAJTRli1bNH/+fB06dEiS1LBhQz366KNq3bq1izsD4CoEKwAAAItwjBUAlNGhQ4f02Wef6ciRI7LZbKpfv77uv/9+x/WsANx8mLECgDIYO3asXn75ZeXn5yswMFDGGJ08eVLu7u4aM2aMhg8f7uoWAbgAl1sAgFJau3atXnzxRY0cOVK//PKLUlNTlZaWppMnT+qFF17QCy+8oA0bNri6TQAuwIwVAJRSnz59VLVqVb333ntFjj/55JM6c+aM5s+ff4M7A+BqzFgBQClt27ZNsbGxxY7HxsZqy5YtN7AjAOUFwQoASunEiRMKDw8vdrxevXpKS0u7cQ0BKDcIVgBQShcvXpSnp2ex4x4eHsrNzb2BHQEoL7jcAgCUwYcffqgqVaoUOXbmzJkb3A2A8oKD1wGglMLDw2Wz2a5ad/jw4RvQDYDyhGAFAABgEY6xAgAAsAjBCgBKaevWrfryyy+dls2ePVv16tVTYGCgnnzySeXk5LioOwCuRLACgFIaNWqUdu/e7bi/Z88e9e/fX507d9YLL7ygpUuXauzYsS7sEICrcIwVAJRSrVq1tHTpUrVo0UKSNHLkSK1fv16bNm2SJH3yySd65ZVXtG/fPle2CcAFmLECgFLKzMxUUFCQ4/769evVvXt3x/2WLVsqJSXFFa0BcDGCFQCUUlBQkONSCrm5ufr222/Vpk0bx/iZM2fk4eHhqvYAuBDBCgBKqXv37nrhhRe0ceNGjRgxQj4+Prr77rsd47t379Ytt9ziwg4BuApXXgeAUvrXv/6lBx98UNHR0apSpYo++ugjp4+4mTFjhrp27erCDgG4CgevA0AZZWVlqUqVKnJ3d3dafurUKVWpUqXEzxME8MdEsAIAALAIx1gBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAfjDmjVrlqpWrerqNgDcRAhWAH63uLg42Ww2PfXUU4XGBg0aJJvNpri4uBveV58+ffTdd99d9+cZNWqUbr/9dkvWtXbtWnXo0EHVqlWTj4+PGjZsqH79+uny5cuSpHXr1slms8lms8nNzU12u1133HGHnn/+eaWmphZaX3Z2tl566SU1adJE3t7eql69ulq2bKnx48crMzOz2D5SU1MVExOjiIgIubm5aejQoYVqFi1apBYtWqhq1ary9fXV7bffrjlz5liyHYCKimAFwBKhoaFasGCBLly44Fh28eJFzZ8/X3Xr1nVJT97e3goMDHTJc5fF3r17de+996ply5basGGD9uzZoylTpsjDw0P5+flOtQcPHtTx48e1fft2/fOf/9SqVasUFRWlPXv2OGpOnTql1q1ba+bMmRo+fLi2bt2qr7/+Wq+88oqSkpI0b968YnvJyclRzZo1NXLkSN12221F1lSrVk0jR45UYmKidu/erccff1yPP/64vvrqK2s2CFARGQD4nfr162fuv/9+07RpUzN37lzH8o8//tg0bdrU3H///aZfv36O5V9++aVp166dsdvtplq1aqZHjx7m+++/d4wfPnzYSDLz5883bdq0MV5eXiYyMtKsXbvWUbN27VojyXzxxRemWbNmxsvLy/zpT38yu3fvdtTMnDnT2O12x/1XXnnF3HbbbWb27NkmLCzM+Pv7mz59+pjs7GxHTXZ2tomJiTE+Pj4mODjYTJo0yURHR5u///3vRb72mTNnGklOt5kzZxpjjPnpp59M7969ja+vr/Hz8zMPP/ywSUtLK3Y7Tp482YSHh5e4rQted2ZmptPy8+fPm4iICNOuXTvHsoEDBxpfX19z7NixIteVn59f4nMVKOn1X+mOO+4wL7744jXVAn9EzFgBsMzjjz+umTNnOu7PmDFDf/vb3wrVnTt3TvHx8dq+fbtWr14tNzc3/fnPfy40K/Pcc89p2LBh2rlzp9q2bavevXsrIyOjUM2ECRO0fft2BQYGqnfv3rp06VKxPf7www9asmSJvvjiC33xxRdav369xo0b5xiPj4/X119/rc8//1wJCQnauHGjvv3222LX16dPHw0bNkxNmjRRamqqUlNT1adPHxlj9MADD+jUqVNav369EhIS9MMPP6hPnz7Fris4OFipqanasGFDsTXF8fb21lNPPaWvv/5a6enpys/P18KFC9W3b1/Vrl27yMfYbLZSP09xjDFavXq1Dh48qHvuucey9QIVjquTHYCKr2DG6uTJk8bLy8scPnzYHDlyxFSuXNmcPHmy0IzVldLT040ks2fPHmPM/z9jNW7cOEfNpUuXTJ06dcwbb7xhjPn/Z24WLFjgqMnIyDDe3t5m4cKFxpiiZ6x8fHycZqiee+4506pVK2PMr7NVHh4e5pNPPnGMnz592vj4+JQ4Y1MwE/ZbK1euNO7u7ubo0aOOZXv37jWSzLZt24pcz+XLl01cXJyRZIKDg80DDzxgpkyZYrKyshw1xc1YGfPrTKAks3XrVpOWlmYkmUmTJjnV3HnnncbX19f4+vqav/71r8W+pt8qacbq9OnTxtfX11SqVMl4eXmZ6dOnX9M6gT8qZqwAWKZGjRrq0aOHPvroI82cOVM9evRQjRo1CtX98MMPiomJUf369eXv76969epJko4ePepU16ZNG8fXlSpVUosWLbR///5ia6pVq6aIiIhCNb8VHh4uPz8/x/1atWopPT1dkvTjjz/q0qVL+tOf/uQYt9vtioiIuJaX72T//v0KDQ1VaGioY1lkZKSqVq1abH/u7u6aOXOmjh07pvHjxyskJESvv/66Yzbsasz/+4Sy385EXTkrtXjxYiUlJalbt25Ox8OVlZ+fn5KSkrR9+3a9/vrrio+P17p16373eoGKimAFwFJ/+9vfNGvWLH300UdF7gaUpF69eikjI0MffPCBtm7dqq1bt0qScnNzr7r+a9l9VVKNh4dHodqCXZBFBZPfLi8NY0yRfRS3/Ldq166t2NhYvf3229q3b58uXryoadOmXfU5CwJbeHi4atasqapVq+rAgQNONXXr1lWDBg2cwuXv4ebmpgYNGuj222/XsGHD9NBDD2ns2LGWrBuoiAhWACzVvXt35ebmKjc3V926dSs0npGRof379+vFF19Up06d1Lhx42JP+9+yZYvj68uXL2vHjh1q1KhRsTWZmZn67rvvCtVcq1tuuUUeHh7atm2bY1l2drYOHTpU4uM8PT2Vl5fntCwyMlJHjx5VSkqKY9m+ffuUlZWlxo0bX3NPAQEBqlWrls6dO1di3YULF/T+++/rnnvuUc2aNeXm5qZHHnlEc+fO1c8//3zNz/d7GWOUk5Nzw54PKG8quboBAH8s7u7ujpkTd3f3QuMBAQGqXr263n//fdWqVUtHjx7VCy+8UOS63n77bTVs2FCNGzfW5MmTlZmZWWgWbPTo0apevbqCgoI0cuRI1ahRQw888ECZevfz81O/fv303HPPqVq1agoMDNQrr7wiNze3EmeZwsPDdfjwYSUlJalOnTry8/NT586d1axZMz322GP697//rcuXL2vQoEGKjo5WixYtilzPe++9p6SkJP35z3/WLbfcoosXL2r27Nnau3evpkyZ4lSbnp6uixcv6syZM9qxY4fGjx+vX375RYsWLXLUjBkzRuvWrVOrVq00evRotWjRQr6+vtq9e7cSExMVFRVV4vZISkqSJJ09e1YnT55UUlKSPD09FRkZKUkaO3asWrRooVtuuUW5ublavny5Zs+erXffffdaNjfwh0SwAmA5f3//Ysfc3Ny0YMECDRkyRFFRUYqIiNBbb72l9u3bF6odN26c3njjDe3cuVO33HKLPvvss0LHbI0bN05///vfdejQId122236/PPP5enpWebeJ02apKeeeko9e/aUv7+/nn/+eaWkpKhy5crFPuYvf/mLFi1apA4dOuj06dOaOXOm4uLitGTJEj377LO655575Obmpu7duxcKSL/1pz/9SZs2bdJTTz2l48ePq0qVKmrSpImWLFmi6Ohop9qIiAjZbDZVqVJF9evXV9euXRUfH6/g4GBHTfXq1bVt2za98cYbevPNN3X48GG5ubmpYcOG6tOnT5EX/fytO+64w/H1jh07NG/ePIWFhenIkSOSfj27c9CgQTp27Ji8vb3VqFEjzZ07t8QzH4E/Opspy8EDAHAdHTlyRPXq1dPOnTuLvaL5unXr1KFDB2VmZl7Xj605d+6cateurYkTJ6p///7X7XkA/DEwYwUAv7Fz504dOHBAf/rTn5SVlaXRo0dLku6//34XdwagIiBYAcAVJkyYoIMHD8rT01PNmzfXxo0bi7xsBABciV2BAAAAFuFyCwAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARf4/JQ4X+sS72K0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply your mapping function to create a new column 'SDG_Mapping'\n",
+    "protected_area['SDG_Mapping'] = protected_area.apply(map_prote7cted_area_to_sdg, axis=1)\n",
+    "\n",
+    "# Count the occurrences of each mapping\n",
+    "mapping_counts = protected_area['SDG_Mapping'].value_counts()\n",
+    "\n",
+    "# Create a bar chart\n",
+    "mapping_counts.plot(kind='bar')\n",
+    "plt.xlabel('Mapping to SDG 13')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Mapping of Marine to SDG 13 (Climate Action)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37700ecc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping function for climate\n",
+    "def map_protected_area_to_sdg(x):\n",
+    "    if x['MARINE'] == 0:\n",
+    "        return 'Not mapped'\n",
+    "    else:\n",
+    "        return 'SDG 13-Climate Action'"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Task 5/User documentation.ipynb b/Task 5/User documentation.ipynb
new file mode 100644
index 000000000..9303b61b4
--- /dev/null
+++ b/Task 5/User documentation.ipynb	
@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5ec4a9ba",
+   "metadata": {},
+   "source": [
+    "# CHARTING THE PATH TO SUSTAINABILITY: Integrating Land Sector Data with SDGs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a89e4d09",
+   "metadata": {},
+   "source": [
+    "### Task 5- USER DOCUMENTATION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88c73575",
+   "metadata": {},
+   "source": [
+    "Welcome to the Moja-global project, where your expertise will be put to great use in performing geospatial analysis for efficient land sector dataset. This is an all-encompassing manual that is been written to aid participants in the break down of integrated data and making optimal use of visualization tools while working on the land sector dataset and exploratory data analysis (EDA). This guide has been organized into a series of tasks, each complete with precise steps and prerequisites to ensure your success while working on this dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "343f5e2c",
+   "metadata": {},
+   "source": [
+    "###  Project Outline\n",
+    "\n",
+    "Task 0 - Git and Github\n",
+    "\n",
+    "Task 1 - Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Task 2 - Attribute-SDG Mapping Prototype\n",
+    "\n",
+    "Task 3 - Geospatial Analysis\n",
+    "\n",
+    "Task 4 - Visualization Prototype\n",
+    "\n",
+    "Task 5 - User Documentation\n",
+    "\n",
+    "Signing off and creating a Pull request(PR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb221b7e",
+   "metadata": {},
+   "source": [
+    "### Task 0 - Git and Github\n",
+    "\n",
+    "**Aim of Task:** The aim of this task is to acquaint participants with Git and GitHub. GitHub holds significant importance in open-source projects due to its accessibility and collaborative capabilities which makes it essential for effective teamwork.\n",
+    "\n",
+    "#### Step by step guide\n",
+    "\n",
+    "step 1: Download git on your system.\n",
+    "\n",
+    "step 2: Create a github account for those that doesn't have one yet.\n",
+    "\n",
+    "step 3: Create a new repository and give it the name 'Outreachy_username_2022,' where 'username' corresponds to your Slack handle in the Moja Global Slack community, and '2022' signifies the present project year.\n",
+    "\n",
+    "step 4: Clone the new repository you created to your local computer using git. To streamline your workflow, consider using a code editor like VS Code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e175bc4",
+   "metadata": {},
+   "source": [
+    "### Task 1 -Exploratory Data Analysis\n",
+    "\n",
+    "**Aim of Task**:The aims of this task is to enhance data analysis skills, promote collaboration, and contribute valuable insights to the land dataset project.\n",
+    "\n",
+    "#### Step by step guide\n",
+    "\n",
+    "Step 1: Choose a geographic location (country, region, province, state, or city, preferably your local area) from the dataset.\n",
+    "\n",
+    "Step 2: Explore the data library to locate all data types relevant to the selected geographical area.\n",
+    "\n",
+    "Step 3: Inside the repository you created, create a Jupyter notebook and perform an analysis on the pertinent data from the data library. Utilize graphs, plots, and dataframes to visually represent and interpret the data.\n",
+    "\n",
+    "Step 4: Signoff your commits, push your work to the remote repository, and initiate a pull request. Document your contribution to the project and then share the repository link in the #outreachy channel of the Slack community."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29dc224",
+   "metadata": {},
+   "source": [
+    "### Task 2 - Attribute SDG mapping prototype\n",
+    "\n",
+    "** Aim of Task**: The aim to create a prototype that effectively links relevant attributes within a dataset to the corresponding Sustainable Development Goals (SDGs) established in the research.\n",
+    "    \n",
+    "#### Step by step guide\n",
+    "\n",
+    "step 1: Fork and clone the Land sector repository to your local computer.\n",
+    "\n",
+    "step 2: Navigatethrough the data library and choose a dataset according to your preference. Then, link the dataset with its corresponding metadata file in the cloned repository\n",
+    "\n",
+    "step 3: Choose two appropriate Sustainable Development Goals (SDGs) as determined by the research. Create a prototype that demonstrates the linkage of specific dataset attributes to these chosen SDGs.\n",
+    "\n",
+    "Step 4: Signoff your commits, push your work to the remote repository, and initiate a pull request. Document your contribution to the project and then share the repository link in the #outreachy channel of the Slack community."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee93869d",
+   "metadata": {},
+   "source": [
+    "Task 3 - Geospatial Analysis\n",
+    "\n",
+    "**Aim of Task** : The primary aim of this task is to conduct an extensive geospatial analysis aimed at revealing noteworthy spatial data patterns and trends.\n",
+    "\n",
+    "#### Step by step guide\n",
+    "\n",
+    "step 1:Perform geospatial analysis to identify patterns or trends if the dataset contains spatial information.\n",
+    "\n",
+    "steps 2: Use libraries such as GeoPandas and other geospatial libraries in Python to perform visualizations (maps) and then write a brief report on any spatial patterns or correlations discovered.\n",
+    "\n",
+    "step 3: Signoff your commits, push your work to the remote repository, and initiate a pull request. Document your contribution to the project and then share the repository link in the #outreachy channel of the Slack community."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c201d9b2",
+   "metadata": {},
+   "source": [
+    "### Task 4 - Visualization Prototype\n",
+    "\n",
+    "**Aim of Task**: This task focuses on developing a prototype for data visualization, to represent the relationships between land attributes and SDGs\n",
+    "\n",
+    "#### step by step guide\n",
+    "\n",
+    "step 1: Develop a prototype for the data visualization component which will represent the relatinship between the attribute and the SDGs relevant to it.\n",
+    "\n",
+    "step 2: Create visualizations that represent the mapped relationships between land attributes and SDGs make the python code used for prototype visualization available.\n",
+    "\n",
+    "step 3: Signoff your commits, push your work to the remote repository, and initiate a pull request. Document your contribution to the project and then share the repository link in the #outreachy channel of the Slack community.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64355a6f",
+   "metadata": {},
+   "source": [
+    "### Task 5 - User Documentation\n",
+    "\n",
+    "**Aim of Task**: The aim of this task is to draft documentation that serves as guide for user \n",
+    "\n",
+    "### step by step guide:\n",
+    "step 1: Commence drafting of user documentation, with an emphasis on the fundamental aspects of understanding integrated data and utilizing the visualization tool.\n",
+    "\n",
+    "step 2: Signoff your commits, push your work to the remote repository, and initiate a pull request. Document your contribution to the project and then share the repository link in the #outreachy channel of the Slack community.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b5c89e9",
+   "metadata": {},
+   "source": [
+    "###  Signing off and creating a Pull request\n",
+    "\n",
+    "**What is a pull request?**: A pull request (PR) is a collaborative feature used in version control systems like Git and platforms like GitHub. It allows contributors to propose changes to a repository's codebase and then request that these changes be reviewed and merged into the main branch. Pull requests are a fundamental part of the open-source development process, enabling collaboration and code quality control.\n",
+    "\n",
+    "**Step by step guide**:\n",
+    "After you have forked and cloned the repository and created a jupyter note in the repository on local machine\n",
+    "\n",
+    "Step 1: Commit Your Changes using the 'git add .'\n",
+    "\n",
+    "Step 2: Sign Off on Commits using 'git commmit -s -m \"the message for the task\"'\n",
+    "\n",
+    "step 3 : Push to repository using the 'git push origin master'\n",
+    "\n",
+    "NB: if you created a branch then you will push to the branch you created on your remote github \n",
+    "\n",
+    "Step 4: Visit your forked repository on GitHub and navigate to the branch you just pushed. You should see a \"New Pull Request\" button. Click on it.\n",
+    "\n",
+    "Step 5: Review and Complete the Pull Request, Describe the changes you've made.\n",
+    "Review the changes and ensure everything looks good and then click on the \"Create pull request\" button."
+   ]
+  }
+ ],
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+   "language": "python",
+   "name": "python3"
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
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+}