step 1

Vector dataset -> euclidean distance


Method: use GDAL. 

Was able to make it work in QGIS. But the rasterize step did not work in the command line. Proximity did.

$ gdal_rasterize -a Shape_Leng -ts 3000 3000 -l philly_street_lines /Users/rbernet/Desktop/philly_street_lines.shp /Users/rbernet/Desktop/raster_testing/test_rasterize.tif

$ gdal_proximity.py /Users/rbernet/Desktop/raster_testing/philly_lines_3.tif /Users/rbernet/Desktop/raster_testing/phil_prox_test2.tif -distunits GEO -of GTiff

Weighted Raster Overlay in python

Goal: Create a web-based weighted raster overlay using open source tools. 
- In the easiest way possible. For a small dataset. In Python

Why: 
1. To learn how this type of analysis might be performed now. 
2. Practice Python and open source tools
3. Understand limitations when datasets get to be too large



Approach.

Step 1. Create a python script that starts with vector data (.shp or geojson) and calculates euclidean distance.
Step 2. After can do this for 1 file. Do it for 3. With same extents.
3: Create static weighted raster overlay. AKA - add them together to create a "prioritization map"
4: Get this on the web....

working in QGIS:

The datasets:

https://drive.google.com/drive/folders/0B8XPTQKFTcIhYU9fUi1pemVTTUU