This project consists of scripts to calculate and evaluate distances between specific building types, e.g., farms It was created to analyze the geographic distribution of farms in Germany, but could be adapted to calculate other objects in arbitrary regions.
- python3
- python3 packages (installation inside venv, see 'Project Setup')
- OSMPythonTools (https://github.com/mocnik-science/osm-python-tools)
- Shapely
- Pandas
- Geopandas
- Numpy
- Geopy
- folium
- Mapclassify
Create local venv:
$ python3 -m venv ./venv
Activate venv:
$ source ./venv/bin/activate
Install dependencies:
$ pip install OSMPythonTools shapely pandas numpy geopy folium mapclassify
The distance-statistics.py retrieves coordinates of farmyards in Germany via
OverPass, filters the retrieved areas (remove farmyards without buildings), and
selecting one building as a representative farm.
With this farm it calculates
distance statistic files.
Retrieval and calculation is restricted to county-level of Germany.
You can change parts of the code to select regions
Activate venv:
$ source ./venv/bin/activate
Run main script (the script quite take some time):
$ python3 distance-statistics.py
This will create and filling following folders with results:
distance_matrices/CSV-files (county-level) containing N² farm-to-farm distance matricesevaluation/evaluation/complete_statistics/GeoJSON-files (state-level) containing concatenated statistics of counties, including geometries of all farmsevaluation/count_of_farms_in_radius/CSV-files (county-level) containing count of neighbors in [1,2..9] km radius for each farmevaluation/minimum_distances/CSV-files (county-level) containing the single minimum distance to another farm (deprecated)evaluation/minimum_five_dists/CSV-files (county-level) containing the five minimum distances to other farms (this supersedes minimum_distances)
And following directories for debugging and caching purpose (speed up on next run)
tmp/intermediate calculation artefacts, like retrieved farm-building geometries.osm_cache/cached data from Overpass
The public overpass server has rate limits. For setting up an own OverPass-Server, see: https://wiki.openstreetmap.org/wiki/Overpass_API/Installation or https://hub.docker.com/r/wiktorn/overpass-api/
After setting up you have to change the following line at the begin of
distance-statistics.py to the desired server address:
overpass_endpoint = "http://overpass-api.de/api/"
For example to:
overpass_endpoint = "http://127.0.0.1:12345/api/"
There exists two Jupyter Notebook files that support evaluation:
statistics.ipynbPresents statistics, foremost count of farms with n [n=1,2..5] minimum neighbours in m [m=1,2..5km] radiusclustering.ipynbCreates clusters including plots
See https://github.com/tyrasd/osmtogeojson for installation.
Create dir for intermediate files:
mkdir json
Show existing cached data:
ls .osm_cache
Use for example "overpass-50d9235b285ba1adddb68f96b061de39efa68db8":
export OVERPASS_CACHE=overpass-50d9235b285ba1adddb68f96b061de39efa68db8
Create formatting required by other tools like osmtogeojson with jq:
cat .osm_cache/${OVERPASS_CACHE} | jq > json/${OVERPASS_CACHE}.json
Remove top-level elements, as osmtogeojson cannot interpret them:
vim json/${OVERPASS_CACHE}.json
Convert Json to GeoJson:
osmtogeojson json/${OVERPASS_CACHE}.json > json/${OVERPASS_CACHE}.geojson
This work was created at Science and Technology for Peace and Security (PEASEC), Technical University of Darmstadt, www.peasec.de, and supported by funds of the German Government’s Special Purpose Fund held at Landwirtschaftliche Rentenbank in the projects Geobox-II and AgriRegio.
- Contributors under those funds:
- Franz Kuntke
Licensed under either of Apache License, Version 2.0 or MIT license at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in distance-statistics by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.