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goodtables.io

Extract of municipal bounderies from swissBOUNDARIES3D

This is a list of the current set of official municipalities ("Gemeinden") in Switzerland, compiled by cividi. The metadata was collected by us through web searches and public contributions, and is provided as "best effort", without any guarantee of correctness.

To add new datasets or make modifications, please visit our GitHub repository and contact us via Issues or Pull Request.

Preparation

A GeoPackage was created based on the Shapefile obtained from [data.geo.admin.ch] via opendata.swiss. Derived from the GeoPackage a CSV, Preview CSV and two GeoJSON derivates are created.

For validation a schema and rules are available in the schema section of the Datapackage.

Run make in the root folder to fetch and convert the data. You need to have ogr2ogr, awk and curl commands available on your system. Note on Windwows: make sure to have both ogr2ogr, e.g. if you have QGIS installed C:\Program Files\QGIS 3.8\bin, in your system path as well as the GDAL_DATA variable, e.g. to C:\Program Files\QGIS 3.8\share\gdal, set.

If you also want to run the validation step with make all-check you need to have goodtables installed.

Preview Data Package

Validation

This Datapackage can be validated by running goodtables validate datapackage.json or on goodtables.io.

Validation is validatinf the datapackage itself and the referenced data based on the schema section in datapackage.json.

Service

This repository contains a minimalist backend service API based on the Falcon framework and Pandas DataPackage Reader. To run:

cd api
virtualenv env
. env/bin/activate
pip install -Ur requirements.txt
python server.py

At this point you should see the message "Serving on port..." Test the API using a REST client such as RESTer with queries such as:

http://localhost:8000/tree?NAME=Hüttwilen

Which gives you the entry for 'Hüttwilen'. You can adjust the amount of output with a page and per_page parameter in your query.

License

This package is licensed by its maintainers under the Open Data Commons Attribution License.

If you intended to use these data in a public or commercial product, please check the data sources themselves for any specific restrictions.

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  • Makefile 68.2%
  • Python 31.8%