Applications in GIS: Location Analysis

Hello GIS'ers,
      Welcome to this weeks look at location analysis. Here I am taking on the persona of a GIS analyst working for some high end clients who are moving to the Alachua county area and have some particular criteria to meet for determining where to buy a house. This backstory plays into this weeks key objectives seen below:

• Create a basemap using a basemap layer.
• Perform proximity analyses using Euclidean Distance Tool
• Convert features to raster's and Reclassify raster data
• Utilize ModelBuilder to perform multiple processes at once
• Conduct analysis using Weighted Overlay tool: isolate suitable result areas.
• Compile and explain results clearly and effectively with cartographically polished maps.
• Provide useful feedback for clients with professional deliverables.

These objectives were accomplished by first generating maps looking at individual variables and how they are distributed around the Alachua county area. The 4 criteria being looked at in the below map are: Euclidean distance (straight line as the crow flies) from both the University of Florida in Gainesville, and the North Florida Regional Medical Center, north west of Gainesville. Then looking at the percentage of home owners by census tract, and percentage of population of census tract aged 40-49. These were the criteria outlined by the clients and individually analyzed below. Each area corresponding to the higher percentages of the described attributes were given a suitability ranking show on the scale. The higher number indicates higher preference. 

The distance analysis performed utilized the distance tool to create the concentric circles at 2.5 miles from the center point of UF and NFRMC respectively. This took a point input and generated a raster file. The rasters were then reclassified from a distance input to a simple number, ranked higher for closer to the origin. The two percentage maps were based off generating a normalized classification of the total population or ownership found in a tract divided by total population or by total homes and classified to show the darker areas as having higher percentage. These were also taken from base feature class to raster data that was reclassified from percentages to represent a 1- 9 scale, 9 being most desirable. The key difference between the above and next set of analysis is that all of these rasters were combined together, given a specific weight for desirability and presented below. 

The weighted overlays above take the same 4 inputs from the first map and given each of them a weighted value. In the upper right all 4 rasters have been given equal consideration at 25% each. Largely this provided a map that has areas closer to the center being more favorable than areas farther out as seen with the first map specifically in the distance based analysis. The second map assumes a couple new factors. The clients do not want to make a long commute from the outlying areas. Looking at the first set of maps we can see that the inputs from the age analysis put more emphasis on farther away census tracts. As a result I modified my weights to favor the distance to the work places (UF and NFRMC) to account for 60% of total preference, gave the other two factors which are still roughly equal 20% each, but also added the two highest ranks of the age factor to a restricted category meaning they wont be considered. This very effectively eliminated all but central areas around the center of Alachua County while highlighting the most desired areas specifically. You can see that almost all of the preferred areas on this map were also more or less ideal on the original, but now they stand out more specifically. 

Thank you for taking this look at location analysis with me. It is definitely a useful aspect of GIS that can be used in any number of important distance based multi-criteria decisions, not just the realm of looking for new property locations.