I am new to spatial analysis and would appreciate some general direction on a project I am attempting, outlined below (I am starting from scratch).
GOAL: To find the best locations to install 2000 feet of sidewalks in my hometown in order to connect the most households to the Central Business District (CBD), where "connected" means within 1.2 miles walking of the CBD. I have shapefiles showing existing structures (households), roads, and sidewalks (already installed).
Here is my proposed solution / thought-process:
- Convert the in-place sidewalk network into a database of nodes that are connected by weights (i.e. distances). Is there a way to directly do this in QGIS (or other program) by clicking on all intersections?
- Calculate the number of households that are within 1.2 miles walking of the Central Business District (e.g. a lat-long point or polygon) using the routing capabilities of pgRouting or something else. This will be the base case "household access" value.
- Using the road layer as a guide, randomly place an additional 2000 feet (say, in 10 foot segments) of sidewalks onto the sidewalk layer. This is the equivalent of constructing a bunch of new sidewalks arbitrarily.
- Re-calculate the nodes and weights using the new pedestrian network as in (1), and then re-calculate the number of households that are now within 1.2 miles of the CBD as in (2). It should increase with the additional sidewalks. Save the locations of the additional sidewalks and the associated "household access" value to a file (e.g. spreadsheet).
- Repeat steps (3) and (4) 10000 times, similar to a Monte Carlo simulation. Using the 10000 sets of data points, choose the sidewalk placement locations that maximize the number of households within 1.2 miles of the CBD.
Does this thought process sound realistic? Does anyone have any suggestions?
-- I would like to accomplish this using some combination of QGIS and R, however I am open to learning PostGIS and/or Python (or anything else) to achieve the goal. Any thoughts or direction will be helpful. Many thanks!