Analyzing California Transporation Infrastructure with Python GIS

This personal project applies concepts learned in IS 445: Data Visualization at UIUC and is a continued exploration of Python GIS visualization libraries


Languages, Libraries, and Tools Used

python geopandas folium matplotlib

Census-Designated Counties of the San Francisco Bay Area
It is important to highlight the geographic layout of the region prior to analyzing factors like access to transportation ('NAMELSAD' refers to the Census legal/statistical area description code for county)


Dataset 1: Low-Income or Disadvantaged Communities Designated by California

This GeoJSON dataset from the California State Geoportal, posted by the California Energy Commission, explores 2020 State Census data for designated Low-Income Communities (LIC) and Disadvantaged Communities (DAC).

  • Low-Income applies to Census tracts (geographic areas) with median household incomes that are at or below either 80% of the statewide median income or the threshold designated by the Department of Housing and Community Development.
  • Disadvantaged designation is determined by the following categories:
    • have the highest (meaning higher pollution burden) 25% of overall scores in the CalEnviroScreen 4.0
    • have no overall scores but the highest 5% of CalEnviroScreen 4.0 cumulative pollution burden scores
    • were identified in the 2017 DAC designation as disadvantaged
    • are lands under the control of federally recognized tribes
Peninsula Subregion (San Francisco, San Mateo)
East Bay Subregion (Alameda, Contra Costa)


Dataset 2: California High Quality Transit Areas

This is a GeoJSON dataset from the California State Geoportal, posted by the California Department of Transportation (CalTrans). It provides information about estimated High Quality Transit Areas in the state. This data includes attributes such as type of transportation and primary transportation agency.

  • ‘hqta_type’ refers to the half-mile surrounding high quality transit corridors and major transit stops
Peninsula Subregion
East Bay Subregion


Focusing on Santa Clara County

Having grown up in San Jose, the county seat of Santa Clara and largest city in northern California, I wanted to analyze and visualize the relationship between LIC/DAC areas and major public transportation.

Side by Side Iframes


Transporation in Relation to LIC/DAC Communities


Overlaying the high quality bus and rail routes over underpriviledged or low-income areas of Santa Clara County gives us various insights for access to transportation in the subregion. While this dataset doesn’t provide information for every single form of transportation, it is still apparent that there are areas (LIC/DAC or not) of the cities that are not connected by major bus or rail. This is particularly evident for parts of Milpitas in the North as well as Sunnyvale, Mountain View, and Palo Alto in the Southwest. Transportation infrastructure is a highly debated issue in the state, especially given the history of California’s “car-centric culture”. Going forward, it would also be valuable to cross-reference this information with data on actual usage of the high quality transportation stops.

Jupyter Notebook Analysis Original Assignment