See installation.md for instructions for installation of necessary software and where to download the input data.
The data processing workflow for producing the enriched OSM road network for bicycle LTS analysis consist of a number of Python and PostgreSQL scripts.
All SQL scripts are run through Python. The SQL scripts are labelled with a number referencing the Python script in which they are used (for example, 00_create_db.py makes use of 00_create_db.sql and 03_match_osm_geodk.py makes use of all SQL scripts starting with 03x_xxx.sql).
The Python scripts must be run in numerical order. They can either be run one-by-one (in this way, intermediate results can be examined) or alternatively, navigate to the scripts folder in this repository, activate the conda environment dk_bike_network and run:
python scripts/run_all_scripts.pyAn important part of the workflow is the conflation of OSM and GeoDanmark data on bicycle tracks and lanes.
For further details see network_matching.md.
See low_traffic_stress_critera.md for further details on the LTS classification.
The final outcome of the data procesing is 2 data sets: A table with OSM road network edges enriched with GeoDanmark data and classified with an LTS value and additional cycling characteristics, and a corresponding node data set.
One the workflow is completed both data sets are exported to the data/processed/ folder.
For an overview of the included columns in the data, that are not originally part of the OSM data set, see bicycle_classification.md. See the documentation for PgRouting for explanations of remaining columns.