Public
Edited
May 12, 2023
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# Discussion
Before beginning this project, what I had stated above did not seem all that intensive. State borders would be easy to find, the census had median income for all states going back almost two decades, and the Department of Defense released annual reports stating what the MHA rate would be for each rank, in each zone. Without a doubt the biggest issue I had was finding an actual map for the MHA zones. Department of Defense did not have one available, Defense Travel Management did not have one either. I reached out to the creator of the tableau map I cited and did not hear back from them unfortunately. The only source I found was from the ArcGIS online catalogue and it was a .lyrx file, meaning I could not edit it, export it, or do anything with it directly. Eventually I figured out I could clip it from the map of the United States I used for state borders, but it then reduced it from MHA by zone to MHA by state. So instead of being able to utilize the over 300 unique MHA zones, I was limited to one for each state. But it would suffice. I performed a relational join to get the attributes from the .lyrx layer onto my new one and carried on.
From here I did run into a myriad of other issues but none of them panned out so I won't discuss them in detail. Suffice to say, I ended up with a frakensteined version of my original goal. It would be really great to be able to project and layer the geojsons ontop of eachother and see the comparison but due to my limited skillset with javascript and html it could not pan out in time. Perhaps I will continue to troubleshoot over the summer.
While performing the research for this assignment, I found that the MHA rates, that is how they are calculated, are similiar to the Consumer Unit Shelter index provided by the Bureau of Labor Stastics. In short, this value is how much a household spends on rent/mortgage per month and utilities. MHA is calculated to cover the rent/mortgage and utilites for the area the beneficiary is living in. BLS says that the average american spends roughly 30% of their annual income on housing and utilities, so the formula turns out to be (ANNUAL_INCOME*0.3)/12 to figure out the monthly contribution. Applying this to each state provided a nice comparison to MHA. Turns out its pretty darn accurate. I was not able to visualize this, but I can say that they are close enough to each other to result in only a plus or minus 5% difference across all zones/states. For the actual visualization aspect of this project, fixed interval based on minimum and maximum values observed in the data set helped to ensure a consistent width for each interval. It also allowed me a consistent way to explore the data, most viewers should be ab le to compare and analyze the different intervals easily. As for the time range, these are the years of my service. Technically, I served from 2015 to 2020 but I spent most of 2015 in a training status and so my service didn't effectively begin until mid 2016. Observing trends along this spatial time period, Marlyand stays in the top interval constantly. We can probably attribute this to Washington D.C. where the median income is greatly effected by the number of overpaid politicians calling this region home. Outside of that, we can see an increase of median income across the board, even in Arkansas who is routinely the bottom of the median income ladder.
Overall, I learned a ton from doing this project. I can safely say that my experience with d3 javascript and html skyrocketed during this process. I even had to bust rust off my python skills when doing some data collecting. My technical proficiency collecting, implementing, and subsequently fixing geospatial data also increased. Being the sole person on this project, I was responsible for every task and I think this challenge forced me to become a better GI analyst in the short term. My vision for this project was stifled by my lack of experience; I wanted to visualize and compare and investigate several different aspects but I still need to learn. I feel confident with the visualization side, I know what classification schemes to use and when, and with tools at hand like colorbrewer, finding an appropriate colorscheme is a trivial matter.
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