For Assignment 4, I’ve created 2 Figures using the Cal Environmental Screening Results. One is a map of the Response Rates in California Counties. The other is a Bubble Chart depicting the average Pollution Burden Score in Bay Area counties.
Subtitle: Response Rates in CA Counties and Average CES 4.0 Burden Score in the Bay Area
Data source: calenvironscreen40resultsdatadictionary.xlsx
Background: In Figure 1, the map visualizes response rates from California counties, using data sourced from the CalEnviroScreen dataset. The dataset provides information on pollution burden and population characteristics across census tracts in California. This figure focuses on aggregating data at the county level, highlighting the number of responses recorded in each county. The goal is to identify geographic trends in data collection and assess how well different counties are represented in the dataset. By understanding and recording response patterns, this can help policymakers and researchers aim to have equitable survey participation and meet gaps in coverage.
Figure 2 is a bubble chart that visualizes the average CalEnviroScreen Score across counties in the San Francisco Bay Area, derived from the CalEnviroScreen dataset. The CalEnviroScreen Score is a calculated value that represents the combined level of pollution burden and population vulnerability in a given census tract in California, determined by multiplying the “Pollution Burden Score” by the “Population Characteristics Score”. It us used to demonstrate areas with high pollution exposure alongside a high population susceptible to its effects. A higher score indicates a greater environmental burden on a community. The interactive chart also includes the number of observations per county to provide context for the data representation when a user moves their mouse over each datapoint. By focusing on Bay Area counties, this analysis highlights regional differences in pollution burden and population distribution. These insights are essential for identifying priority areas for environmental interventions and for ensuring equitable policy development.
Results: The map reveals notable variation in response rates across California counties. Urban counties such as Los Angeles, Orange County, and San Diego display higher response counts, likely due to larger populations and greater engagement in environmental assessments. In contrast, rural counties, such as Inyo and Modoc, show much lower response counts, reflecting challenges in data collection in sparsely populated areas. These findings highlight the need for targeted outreach to underrepresented counties to improve dataset comprehensiveness and equity in environmental health research.
The bubble chart shows variation in Pollution Burden Scores across Bay Area counties. Counties such as Alameda and Solano exhibit higher average scores, suggesting greater exposure to environmental pollutants compared to other counties like Marin or Sonoma, which have lower scores. The size of each bubble reflects the number of observations, with more populous counties, such as Santa Clara, showing larger bubbles.