adding numbers to order the code

Borough_Potential/01_exploreClimateChange.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c187a10",
+   "metadata": {
+    "id": "7c187a10"
+   },
+   "outputs": [],
+   "source": [
+    "import ee\n",
+    "import google"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd4fc922",
+   "metadata": {
+    "id": "bd4fc922"
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cd2789f",
+   "metadata": {
+    "id": "0cd2789f"
+   },
+   "outputs": [],
+   "source": [
+    "ee.Authenticate()\n",
+    "ee.Initialize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4276104f",
+   "metadata": {
+    "id": "4276104f"
+   },
+   "source": [
+    "Seems like climate scientists love their 30 year averages. Go ahead and compare 1980-2010 with 2010-present ?\n",
+    "What about 2000-2010 and 2010-2020?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ab7a03b",
+   "metadata": {
+    "id": "4ab7a03b"
+   },
+   "outputs": [],
+   "source": [
+    "daily = ee.ImageCollection(\"ECMWF/ERA5_LAND/DAILY_AGGR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adda23c9",
+   "metadata": {
+    "id": "adda23c9"
+   },
+   "outputs": [],
+   "source": [
+    "daily1980_2010 = daily.filterDate('1980-01-01', '2009-12-31').select('temperature_2m').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e42c2b47",
+   "metadata": {
+    "id": "e42c2b47"
+   },
+   "outputs": [],
+   "source": [
+    "daily2010_now = daily.filterDate('2010-01-01', '2023-12-31').select('temperature_2m').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4646c48",
+   "metadata": {
+    "id": "f4646c48"
+   },
+   "outputs": [],
+   "source": [
+    "counties = ee.FeatureCollection(\"TIGER/2018/Counties\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c204758e",
+   "metadata": {
+    "id": "c204758e"
+   },
+   "outputs": [],
+   "source": [
+    "ak = counties.filter(ee.Filter.eq('STATEFP', '02'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2342d633",
+   "metadata": {
+    "id": "2342d633"
+   },
+   "outputs": [],
+   "source": [
+    "ak.size().getInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c189b31",
+   "metadata": {
+    "id": "9c189b31"
+   },
+   "outputs": [],
+   "source": [
+    "prewarming = daily1980_2010.reduceRegions(collection=ak, reducer=ee.Reducer.mean(), scale=11132)\n",
+    "prewarming = prewarming.select(['NAMELSAD', 'mean'])\n",
+    "prewarming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f0ad67f",
+   "metadata": {
+    "id": "8f0ad67f"
+   },
+   "outputs": [],
+   "source": [
+    "postwarming = daily2010_now.reduceRegions(collection=ak, reducer=ee.Reducer.mean(), scale=11132)\n",
+    "postwarming = postwarming.select(['NAMELSAD', 'mean'])\n",
+    "postwarming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "614b8242",
+   "metadata": {
+    "id": "614b8242"
+   },
+   "outputs": [],
+   "source": [
+    "pre_boroughs = []\n",
+    "pre_temperature = []\n",
+    "preL = prewarming.toList(30)\n",
+    "\n",
+    "for i in range(prewarming.size().getInfo()):\n",
+    "    pre_temperature.append(ee.Feature(preL.get(i)).get('mean').getInfo())\n",
+    "    pre_boroughs.append(ee.Feature(preL.get(i)).get('NAMELSAD').getInfo())\n",
+    "\n",
+    "pre_temperature, pre_boroughs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04eeef2a",
+   "metadata": {
+    "id": "04eeef2a"
+   },
+   "outputs": [],
+   "source": [
+    "post_boroughs = []\n",
+    "post_temperature = []\n",
+    "postL = postwarming.toList(30)\n",
+    "\n",
+    "for i in range(postwarming.size().getInfo()):\n",
+    "    post_temperature.append(ee.Feature(postL.get(i)).get('mean').getInfo())\n",
+    "    post_boroughs.append(ee.Feature(postL.get(i)).get('NAMELSAD').getInfo())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eab88dc8",
+   "metadata": {
+    "id": "eab88dc8"
+   },
+   "outputs": [],
+   "source": [
+    "pre_boroughs == post_boroughs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28d7610a",
+   "metadata": {
+    "id": "28d7610a"
+   },
+   "outputs": [],
+   "source": [
+    "pre_temperature_f = [(temp - 273.15) * 1.8 + 32 for temp in pre_temperature]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bba9ab51",
+   "metadata": {
+    "id": "bba9ab51"
+   },
+   "outputs": [],
+   "source": [
+    "post_temperature_f = [(temp - 273.15) * 1.8 + 32 for temp in post_temperature]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d974623",
+   "metadata": {
+    "id": "5d974623"
+   },
+   "outputs": [],
+   "source": [
+    "difference = [post - pre for post, pre in zip(post_temperature_f, pre_temperature_f)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2e7f68a",
+   "metadata": {
+    "id": "d2e7f68a"
+   },
+   "outputs": [],
+   "source": [
+    "difference_greater2 = [temp > 2 for temp in difference]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "075e55e0",
+   "metadata": {
+    "id": "075e55e0"
+   },
+   "outputs": [],
+   "source": [
+    "print([b for a, b in zip(difference_greater2, pre_boroughs) if a])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aeae7c8e",
+   "metadata": {
+    "id": "aeae7c8e"
+   },
+   "outputs": [],
+   "source": [
+    "difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a60906b",
+   "metadata": {
+    "id": "9a60906b"
+   },
+   "outputs": [],
+   "source": [
+    "sum(difference) / len(difference)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}