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I am currently working on a university project that focuses on finding most suitable locations and equipping the Hørsholm Kommune with EV charging stations.

I am, however, not from Denmark and my danish group mates have never worked with GIS programs before.

This makes finding suitable data complicated.

The data I would like to find as .shp file or at least as WFS layer would be:

  • Municipal boundary layers
  • Car registry distribution in the Municipality
  • Population distribution in the Municipality
  • Municipal/ public buildings
  • Attractions/ Points of interests in the Municipality (like the harbor, castles, etc.)
  • Energy grid lines and capacities
  • Parking spaces

I have already looked on multiple websites (https://kortforsyningen.dk, https://gst.dk, https://datafordeler.dk, and https://www.horsholm.dk/om-kommunen/kort-over-kommunen) to find the data. But without great success so far.

Can anyone who has previously worked with data maps in Denmark show me where to look?

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    Shoot an email to the GIS lecturers at the Danish Technical University? One of their largest campuses are in/near Hørsholm – jonnor May 13 at 17:06
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Welcome to Open Data!

The data you are requesting can be broken down by availability from open sources and ones that would need additional efforts and requests from government agencies.

Your best friend to start with is Open Street Map, it will be able to provide the following from your list:

  • Municipal Boundary Layers
  • Parking Locations
  • Powerlines and other utilities
  • Attractions/ Points of interests in the Municipality (like the harbor, castles, etc.)

Getting point of interest data from OpenStreetMap is a good starting point to get the point information and then lead into your polyline data.

The remaining items are going to be difficult to find unless the government provides this through some means:

  • Car registries are normally private information,
  • Population distributions can go either way, you may want to see if each municipality has parcel data availble to work with,
  • Individual parking space locations could be identified by finding aerial imagery and running a machine learning to identify their location
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    Thank you, this has been quite helpful. We are already in contact with the municipality, and they have none of the data at hand. This is why I tried to find the data through other means, but feared it wouldn't be available. I am also not experienced enough to run a machine learning for the identification and will do such by hand. – Philip Gatzlaff May 14 at 7:08

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