Overview

Title: Insights from CalEnviroScreen 4.0: Environmental and Socioeconomic Disparities in California’s Most Populous Counties

Subtitle: Dashboard for Final PHW 251B Assignment

Data Source: CalEnviroScreen (CES) 4.0

Dashboard Author: Diana Valdivia

Background CalEnviroScreen (CES) 4.0 is a comprehensive dataset developed and maintained by the Office of Environmental Health Hazard Assessment (OEHHA) within California’s EPA. It includes data on environmental, public health, and socioeconomic conditions and plays a key role in guiding health equity program funding and allocation.

Data Description: This dashboard highlights CES 4.0 scores and key environmental and socioeconomic factors across California’s 10 most populous counties. It provides insights into pollution burden and population characteristics to assess regional disparities.

Study Population: The study focuses on the populations residing in the 10 most populous counties in California, namely Fresno, Contra Costa, Sacramento, Alameda, Santa Clara, San Bernardino, Riverside, Orange, San Diego, and Los Angeles. The study population provides geographic and industrial variability across these counties, from agricultural hubs like Fresno to densely urbanized areas like Los Angeles, exposes residents to a wide range of environmental risk factors. Additionally, focusing on densely populated areas ensures a robust sample size, enabling statistically significant findings that can inform targeted interventions and policy recommendations.

Research Purpose

Research Question: The primary research question is: How do (1) environmental exposures, such as pollution, toxins, and air quality, and (2) CES 4.0 scores vary across the 10 most populous counties in California? This question aims to explore regional disparities in environmental risks within these areas.

Additionally, the analysis examines socioeconomic factors—such as education, poverty, unemployment, and housing burden—to investigate their potential relationship with environmental risks and their cumulative impact on public health.

Importance: The findings from this analysis can help policymakers, health professionals, and environmental advocates identify initial areas of concern for public health and environmental quality. By understanding the weighted averages of environmental factors such as air quality and exposure to toxins, this information can guide intial steps for interventions, resource allocation, and public health strategies aimed at improving the well-being of residents in these counties. The interactive plots and summary tables provide a user-friendly way for viewers to explore these factors and their potential implications for community health.

Table I: Summary of Descriptions

Data Context

This plot illustrates the CES 4.0 scores for the 10 most populous counties in California, ordered from least populous (Fresno) to most populous (Los Angeles). The CES 4.0 score is derived from two key components: the pollution burden (averaging environmental exposures and effects) and population characteristics (averaging sensitive populations and socioeconomic factors).

The data is filtered to include only these counties, with scores rounded to two decimal places for clarity. This provides a clear and detailed view of disparities that shape public health outcomes across the state.

Results

The interactive box plot displays the distribution of CES 4.0 scores, highlighting variations in environmental and socioeconomic conditions among the counties. Users can explore the plot by hovering over data points for exact values or focusing on individual counties to gain valuable insights into public health disparities. For instance, you can compare Los Angeles, the most populous county, with Fresno, the least populous, to examine how their CES 4.0 scores differ.

The next step involves a deeper analysis of the environmental factors contributing to these scores across the 10 counties.

CES 4.0 Scores

The interactive box plot visualizes the range, median, and variability of CES 4.0 scores for each of the 10 counties:

Data Context

The dataset includes variables such as CES 4.0 Score, Ozone, PM2.5, Diesel PM, Drinking Water Quality, Lead, Pesticides, Toxic Releases, and Traffic, all weighted by population size to provide representative averages. A summary table presents these averages for each county, rounded to two decimal places, offering a clear view of key environmental indicators. Users can interact with the table to search, sort, and compare environmental conditions across counties, providing insights into how these factors contribute to public health disparities.

Results

This analysis addresses the research question by calculating and presenting population-weighted averages for environmental exposures and CES 4.0 scores across California’s 10 most populous counties. The results reveal regional variations in environmental risks and cumulative burdens. The interactive table allows users to explore these disparities, highlighting how different counties are affected by pollution and socioeconomic challenges, and offering insights into the unequal distribution of environmental risks.

Next, we will examine a broad example comparing two counties by analyzing their populations’ socioeconomic factors.

Table II: Summary table

A summary table of population-weighted averages for the selected environmental and health variables (e.g., Ozone, PM2.5, Traffic, etc.).

Data Context

Lastly, let’s compare Fresno, the county with one of the highest CES 4.0 Score, and Santa Clara, the county with one of the lowest CES 4.0 Score, among California’s most populated counties. This comparison focuses on socioeconomic factors, such as education, housing burden, poverty, and unemployment, differ between these two counties. The data is weighted to provide representative averages.

Results

The analysis compares socioeconomic factors (Education, Poverty, Unemployment, Housing Burden) between Fresno and Santa Clara counties. The results show that Fresno has a higher mean value across all socioeconomic indicators than Santa Clara, reflecting more significant challenges in education, poverty, unemployment, and housing. As a reminder the education factor looks at percentage of population over 25 with less than a high school education.

The results are crucial because the CES 4.0 integrates socioeconomic factors to assess overall community vulnerability. Understanding how these factors vary across counties like Fresno and Santa Clara helps identify regional disparities that may influence environmental health outcomes.

Recommendations for further research include a deeper investigation into how resources and interventions can be targeted to counties with higher socioeconomic challenges, especially where these factors amplify exposure to environmental risks. This data can serve as a starting point for further research to develop community-specific strategies aimed at improving education, reducing poverty, and addressing housing burdens, thereby mitigating the compounded effects of environmental hazards.

Graph I