Month: February 2024

Aside from mortality data during an epidemic, can you think of any other situations where a heat map or Voronoi polygons would be useful ways to analyze spatial data? How might they be useful in this context of your final project?

One situation in which a Voronoi polygon may be useful would be for a business to pinpoint it’s locations of operation along with the relative proximities of its customers. This may offer a business insight as to whether or not it is worth opening new locations or closing existing ones. Voronoi polygons may also be helpful for public transportation purposes, where someone could view the map and easily find out where the nearest bus stop, subway station, etc. is located relative to their location. Heat maps could prove to be useful for many statistical purposes. For instance, any economically based data (such as interest rates as in the previous practicum) could be visualized in a heat map to better understand spatial relationships. Additionally, I usually think of heat maps used to visualize the density or frequency of made shots by location on a basketball court, where the heat map serves some importance in sports analytics.

In terms of my project, I think that Voronoi polygons may be useful, as I am planning to observe the spatial locations of pharmacies in the Omaha area and put that in context with COVID vaccination rates and community health over the last 4 years. I could create Voronoi polygons around pharmacies as the center point, and this would allow me to better analyze the distances that certain communities lie from health-related resources.

1. Aside from mortality data during an epidemic, can you think of any other situations where a heat map or Voronoi polygons would be useful ways to analyze spatial data? How might they be useful in this context of your final project (if at all)? 

What came to mind first for me is response/action for troops, paramedics, firefighters, police, and other response teams where time is crucial. Using Voronoi polygons, it could help mark what station is closest given a certain address or location within the city, state… whatever the area may be. This would allow for the quickest time to arrival–and for many of these occupations, an increased rate of survival or deescalation of a situation. In terms of my final project, I could use heat maps to mark reported deer kills within the state of Iowa, which would allow an easy visual of where most deer are killed; I could further change this to two different heat maps of doe vs. buck reported harvests and see the differences in this. In fact, I believe this would be a great addition to my final project and would help visually/spatially support an argument I am trying to make with my map.

1. What patterns do you see between mortgage companies and locations that supplied lendees in Philadelphia?  

There is a lot more loans within the red areas. It is also important to note that these same areas also had lower white populations and higher Black and foreign born populations.

2. Which regions had the highest interest rates? 

Also within the “undesirable” areas are the  highest interest loans. I also included in this map where the Berean company offered loans, which was clustered over where the highest loans are also offered. 

3. What indication do you see (if any) that HOLC maps caused redlining (as opposed to mapping preexisting discrimination). If none, what additional historical evidence do you think you might need to establish this relationship? 

I think that offering high interest loans in very strict clusters sort of had an effect of trapping people. The high interest loans made it more difficult for people to save large amounts, as well as building generational wealth in the form of owning property. The mapping of the ‘undesirable” or “hazardous” essentially trapped people in those areas and kept the interest rates high, which further deterred others from seeking homes there.

4. What additional data layers do you think might supply evidence of discriminatory housing policy/segregated urban development that you don’t have access to in this exercise?

Another layer to add would be something along the lines of employment rates or average incomes of neighborhoods, or even graduation rates. Discrimination was rampant throughout all forms, ranging from access to different services, goods, and opportunities . I think also having that intersection of access and discrimination would also show us how discrimination could come in different forms, and impact other forms of discrimination. 

5. Create one clear, legible map that you think best demonstrates the most compelling visualization of redlining in Philadelphia.  

This map is still very confusing, but I though it was important to include mortgage rates and the HOLC map, as well as where loans were offered. I also had to lessen the opacity for the heat map.