diff --git a/README.md b/README.md
index a44593d..7583c8a 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ tables are separate from OpenStreetMap tables and get queried at search time sep
 The dataset gets updated once per year. Downloading is prone to be slow (can take a full day) and converting
 them can take hours as well. There's a mirror on https://downloads.opencagedata.com/public/
 
-Replace '2023' with the current year throughout.
+Replace '2024' with the current year throughout.
 
   1. Install the GDAL library and python bindings and the unzip tool
 
@@ -19,18 +19,31 @@ Replace '2023' with the current year throughout.
         pip3 install -r requirements.txt
         ```
 
-  2. Get the TIGER 2023 data. You will need the EDGES files
+  2. Get the TIGER 2024 data. You will need the EDGES files
      (3,235 zip files, 11GB total).
 
-         wget -r ftp://ftp2.census.gov/geo/tiger/TIGER2023/EDGES/
+         wget -r ftp://ftp2.census.gov/geo/tiger/TIGER2024/EDGES/
+
+
+     Alternatively
+
+         ```bash
+         curl 'https://www2.census.gov/geo/tiger/TIGER2024/EDGES/' | grep -o 'tl_[^"]*.zip' | sort -u > filelist.txt
+         # 3235 filelist.txt
+         cat filelist.txt | sed -e 's!^!https://www2.census.gov/geo/tiger/TIGER2024/EDGES/!' | xargs -n 1 wget
+         ```
 
   3. Convert the data into CSV files. Adjust the file paths in the scripts as needed
 
+        ```bash
         ./convert.sh <input-path> <output-path>
+         ```
 
   4. Maybe: package the created files
   
-        tar -czf tiger2023-nominatim-preprocessed.csv.tar.gz tiger
+        ```bash
+        tar -czf tiger2024-nominatim-preprocessed.csv.tar.gz tiger
+         ```
 
 
 US Postcodes