linking: better name matching for address-less places

[nominatim.git] / data-sources / us-tiger / README.md

diff --git a/data-sources/us-tiger/README.md b/data-sources/us-tiger/README.md
index dfb44c5ff8f39478b394cee2f89f91d01c8e1ae2..b76150f7510c18948c4b2a82a132a4d8a5e2478d 100644 (file)
--- a/data-sources/us-tiger/README.md
+++ b/data-sources/us-tiger/README.md
@@ -2,19 +2,19 @@
 
 Convert [TIGER](https://www.census.gov/geo/maps-data/data/tiger.html)/Line dataset of the US Census Bureau to SQL files which can be imported by Nominatim. The created tables in the Nominatim database are separate from OpenStreetMap tables and get queried at search time separately.
 
-The dataset gets updated once per year. Downloading is prown to be slow (can take a full day) and converting them can take hours as well.
+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.
 
-Replace '2018' with the current year throughout.
+Replace '2019' with the current year throughout.
 
   1. Install the GDAL library and python bindings and the unzip tool
 
         # Ubuntu:
         sudo apt-get install python3-gdal unzip
 
-  2. Get the TIGER 2018 data. You will need the EDGES files
+  2. Get the TIGER 2019 data. You will need the EDGES files
      (3,233 zip files, 11GB total).
 
-         wget -r ftp://ftp2.census.gov/geo/tiger/TIGER2018/EDGES/
+         wget -r ftp://ftp2.census.gov/geo/tiger/TIGER2019/EDGES/
 
   3. Convert the data into SQL statements. Adjust the file paths in the scripts as needed
 
@@ -22,5 +22,5 @@ Replace '2018' with the current year throughout.
         ./convert.sh <input-path> <output-path>
 
   4. Maybe: package the created files
-
-        tar -czf tiger2018-nominatim-preprocessed.tar.gz tiger
+  
+        tar -czf tiger2019-nominatim-preprocessed.tar.gz tiger