This is a combination of the distance to the main urban areas and distance to tarmac roads. I removed all of the tracks and trails from my road file as this layer is supposed to represent a general cost layer where proximity to humans is detrimental to jaguars and they would rather be in the low cost areas to the far left and upper regions of the map.
Some things to remember where trying to generate these types of maps:
1. the euclidean distance method seems to be the most appropriate:
Spatial Analyst - Distance - Euclidean Distance
3. the output will only be generate for the exact extent of the data you are performing the function on. For instance, if you have polyline data, then the distance will extend only until the smallest rectangle that includes all the lines, and NOT the surrounding NoData areas. Therefore you need to do the distance function on a larger dataset and then clip it to the area you want if this is a problem.
4. In my case, this was not possible so I converted my polyline shape file (roads) to a raster first, then extended the extent to the area required by my model and then performed the distance function.
5. To get the map above, I combined distance maps for urban areas and roads.
6. My urban areas were actually polygons and the distance function worked just fine with this.
7. As I said earlier, I decided to remove the small roads and tracks from my roads dataset before calculating the distances as these do not impact negatively on the movement of my agents (jaguars).
8. Once I had the two datasets that were each of the correct size and extent, I simply added the layers together:
Spatial Analyst - Math - Plus
9. Adding the two layers together creates a new layer that matches the size of the smallest input raster, so I actually added together the roadProximity file (purely for the model area) together with the urbanProximity file for the whole of Belize.
So beautiful! Can I use it for a fun graphic in my Intro to GIS class?
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