Identifying mountain biking suitability in urban forests using ArcGIS Pro's ModelBuilder
In this exercise (a part of the Advanced GIS Analysis course), the task was to create a geoprocessing tool in ArcGIS Pro's ModelBuilder that allows a user of the tool to identify forests suitable for recreation with their own data. Since people’s perceptions of what makes a forest suitable for recreation are based on subjective preferences, I had to use fuzzy overlay techniques to model the likelihood of recreation suitability based on site characteristics represented as cell values in different forest raster layers. The analysis was focused specifically on forests in cities and urban areas (urban forests). I chose to look into mountain biking potential, or suitability, in the city of Gothenburg. The model I created using the ModelBuilder can be seen below along with the step-by-step workflow.
The model is based on four main criteria:
Slope variability (between 0 and 17%)
Optimal tree volume density (between 80 and 200 m3/ha)
Area size (the bigger the better)
Path/Track extent (the longer the better)
The final result shows a raster symbolized with a color ramp that indicates the minimum (unsuitable) and maximum (suitable) values. For example, if the highest value is 1, it means that the forest has fulfilled all the criteria perfectly. However, the highest value could also be, for example, 0.23. This means that the forest is the most suitable relative to the rest, although far from perfect. In using this tool, it is also crucial that the datasets do not temporally deviate too much from each other as forests, especially in urban settings, are always changing. Below is an example of the resulting raster.