For this project we used the EPA's Smart Location Database which contains data based on the 2019 US census for individual blocks throughout the United States. More specifically, walkability is calculated based on intersection density, the proximity to transit stops, employment mix, and household employment mix. This database also contains individual data on population, land area, income, and other such factors which can be used to calculate further statistics.
This plot compares the population density of each state. States with large cities, such as New York and California, tend to have a higher population density.
This plot compares the low income percentage of each state. The states with a higher low income percentage tend to be states with lower population, such as South Dakota and Montana.
This plot compares the absolute population of each state. There is a loose correlation between absolute population and population density.
This plot compares the average walkability in each state. States with a higher walkability index tend to also have a higher population density and absolute population.
When we combine the insights from the four vizualizations above, we are able to gather context for the graphs below. This helps us to see how each state compares.
The following tabs show how walkability varies throughout 5 different major US cities. This is juxtaposed with population density and low income percentages for those same areas. This allows you to see the differences in correlations between these variables in different cities.
In summary, we can use these visualizations to see both how sates rank with respect to walkability and other major statistics, and how different major cities approach walkability. In the real world, this could be used to better inform city development and resource allocation to pedestrian projects.