Vector analysis of trees in Utrecht

Using data from Gemeente Utrecht to identify and analyse trees across Utrecht

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What does this map tell us?

This map essentially visualises all of the trees in Utrecht, sourced from open data from Gemeente Utrecht. There are several layers, each visualising their own set of data:

  • AmerikaanseEikOnly: This layer only highlights Northern Red Oak trees in Utrecht
  • CP_Wijken_SummarizeWithin: This layer is a choropleth map representing the amount of trees in different districts in Utrecht based on CP (count of points - where points are trees).
  • RA_Wijken_SummarizeWithin: This layer is another choropleth map representing the amount of trees in different districts in Utrecht based on RA (rate of area).
  • Grenzen Utrecht - Wijken: This layer shows the different districts, neighbourhoods, and areas in Utrecht. If you click on any point highlighted in this layer, you can see a lot of information on the tree that it represents such as the type of tree and who owns it.
From this map, we can see that the Vleuten - De Meern neighbourhood has the most amount of trees and the Binnenstad area has the least amount of trees. This can be seen by looking at the choropleth maps and the kernel density maps because it was quite simple to see that the Binnenstad area was the least dense and the lightest coloured, meaning that it had the fewest count of points (or trees).

How did I make this map?

To create this map, I used ArcGIS online data to obtain data about trees ("Bomenkaart"), the Municipalities provinces ("Gemeenten Provincie Utrecht"), and the boundaries ("Grenzen Utrecht"). After obtaining these data sets, I created a layer highlighting just my trees (Amerikaanse Eik/Northern American Red Oak). Then, I analysed the Wijken layer to count all of the trees in each district based on their count of points and I also analysed the same layer to calculate the rate area. After I created both of these layers, I created two choropleth maps based on the respective attributes. Finally, I used the "Kernel Density" tool in ArcGIS Pro to visualise the densities of all trees in Utrecht (shown in Green) and of Amerikaanse Eik as well (shown in Purple).

Skills I learnt as a result:

  • Using geoprocessing tools: I obtained a proficiency in using certain geoprocessing tools to export features, calculate rate area, calculate fields, etc. All of these skills helped me analyse the point data that was obtained from Gemeente Utrecht to create these maps.
  • Using ArcGIS: for this assignment, we switched to using ArcGIS instead of QGIS.
  • Uploading maps to Esri-Online: I learnt how to export the entire map (with its respective layers) onto Esri-Online and how to make an interactive app in which people can hide and show different layers with a clear legend.