The presence or absence of crime in a neighborhood influences the choices people make in their community and can shape the social fabric of it as well. What I wanted to discover in this project was how transit ridership is affected by crime. If there is more crime in an area of a community, is the transit stop affected as well? I chose two different neighborhoods: a high-income and low-income neighborhood and performed several spatial statistics tests in GIS to calculate the relationship between bus ridership and crime in both neighborhoods.
Crime and ridership are clustered in both the low and high-income neighborhood, although ridership is more clustered than crime overall. However, the low-income neighborhood had no significant Hot-Spots, because high crime levels occur more dispersedly. Therefore, crime did not heavily influence ridership in the low-income neighborhood.
View the GIS presentation here.