Geospatial Data Analytics: Environmentally Friendly Transportation To Work

Guidance: Prof Kristen Kurland

Role: GIS & Spatial Analyst 

The map represents the distribution of Non-Private Vehicle users in Pittsburgh using a 6000-meter kernel density analysis. Darker purple areas indicate higher concentrations, revealing regional trends of non-private vehicle use across the city. This macro-scale view highlights dominant areas where transit access and walkability are high, providing insights into urban infrastructure planning and transportation choices. By smoothing point data over a larger area, the map reduces clutter while clearly illustrating city-wide mobility patterns and the influence of pedestrian-friendly neighborhoods on transportation behavior.

The high-density areas in the kernel density 6000 map (the best in my opinion) align with transit-rich and walkable areas of the city. Walkable neighborhoods tend to have higher concentrations of non-private vehicle users, and the urban design and transportation access play an important role in transportation choices. The 6000 ft kernal density best illustrates these trends, showing that large-scale pedestrian infrastructure influences transportation choices.

When compared to point maps, again, kernel density maps are better as they reduce clutter and help interpret mobility patterns. Maybe the point symbolized map is useful for precise data analysis, but is not effective for identifying city-wide trends. 

Beyond high NonPrivate Vehicle values, the highest peaks in the kernel density map are influenced by population density, proximity to public transit, and mixed-use development, which encourage walking and transit use. University campuses and employment hubs also drive demand, while walkability and bikeability make non-private transportation more viable.