This tool is taking
- a geoJson generated by overpass turbo
- a geoTiff file with values (such as this geoTiff which contains views for a tile in OpenStreetMap)
and creates a copy of the geoJson, now containing the value at the given location of the item in the geoTiff, as well as being ranked by this value.
Requires GDAL to be installed on the executing machine.
To use, copy a geoTiff, a geoJson and the aggregate.py script in the same directory and execute the script.
Remarks:
- If you want to create your own overpass query, make sure to set the out statement to "out center;"
Takes in the generated file from aggregate.py and the qrank.csv from QRank, and ranks all features with a wikidata tag.
This script takes the supplied geojson, adds the qrank value for all features with a wikidata tag. If a feature does not contain a wikidata tag, it is discarded. The tags qrank_rank and osm_views_rank are also added, which are numeric ranks, the highest viewed feature respectively being ranked 1.
It outputs:
- output-qrank.geojson, sorted by qrank, the highest value first.
- output-tile-logs-synced.geojson, sorted by OSM Tile Views, the highest value first.
Additionally, it adds the qrank property to the supplied geojson where a wikidata tag is found in a feature.
These two files can be used to compare the two methods of ranking (QRank and OSM Tile Views).
Takes a geoJson with objects containing at least the osm_views and optionally the qrank property and combines the values to a new rank property. Two files will then be created, containing and sorted by this rank property.
This is for testing the effective combination of OSM Tile Counts and QRank.
A web app based on React which can be used to View Value vs Rank distribution of the files generated by generateRanking.py