Housing inequality has been increasing since the 1970’s according to the US Census Surveys. Housing becomes relatively more expensive the closer one moves toward a work site. Because families with lower income levels often cannot afford high transportation costs, they are forced to live in inner-city locations in order to be close to employment opportunities. In order to win this spatial competition for housing-near-work sites, low-income families must compensate for high-priced living by accepting smaller housing, lower-quality housing, or both.

After looking at the challenges presented on HackGT’s website, we decided to combine Wayfair’s challenge of addressing the housing inequality crisis through the incorporation of ESRI technology, specifically the ArcGIS platform. After exploring this technology, we found that there is already an ESRI tutorial that identifies areas of high populations of homelessness. We decided that we wanted to provide a resource to provide low-income families/individuals in Georgia with an analytical suggestion as to what counties could provide a higher quality of life through higher employment rates, lower crime, and a variety of other factors. This resource would help them begin their journey to improving their quality of life.

The build process began with aggregating data on factors that influence housing inequality, reducing this data to the county-level for the state of Georgia, visualizing data and formulating possible Value Index algorithms. Then, we extracted the data and joined relevant attributes using Python scripts. After we developed a script to calculate the overall Value Index, we took a heuristic approach to determine formulate weights. Finally, we published a web app to display our findings.

The main challenge we faced was that we didn't have access to the full suite of ArcGIS tools. Our project would have benefited greatly from the "suitability analysis" tool, but instead, we had to develop our suitability analysis in the form of a Python script.

For Hacking Housing Inequality With ESRI's ArcGIS, the logical next steps would be to expand our "Value Index" to the entirety of the US, and then into developing countries. Additionally, more data granularity would allow for geographic recommendations on where to live down to the neighborhood, which would also prove extremely useful.

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