Subtitle: A Dashboard for Understanding Environmental Inequities
Data Source: CES 4.0 Dataset
Background: It also looks at the question of pollution burden and racial disparities across all counties in California in search of patterns of environmental inequity that may help drive policy decisions.
Results: From the interactive map, it can be observed that with an increased average pollution burden score, these counties are highly concentrated in the densely populated areas, especially in Southern California. These also are areas with a high count of observations, and thus targeted interventions may be necessary. The box plot for racial demographics has large variability across counties, with Hispanic populations having the highest median percentage in many counties, followed by White and Asian populations. The African American and Native American populations consistently had underrepresentation across counties. The cases table highlights that there are counties with large sets of records, either bias in data collection or regional hotspots of pollution. Demographic summary also underlines disparities in the pollution burden, with a striking overlap between high scores of pollution and minority-dense areas.
This map shows average pollution burden and observation counts by county.
The box plot shows racial percentage by California county:
County | Total Cases |
---|---|
Los Angeles | 2327 |
San Diego | 626 |
Orange | 582 |
Riverside | 452 |
Santa Clara | 372 |
San Bernardino | 368 |
Alameda | 360 |
Sacramento | 317 |
Contra Costa | 207 |
Fresno | 199 |
San Francisco | 195 |
Ventura | 173 |
San Mateo | 156 |
Kern | 151 |
San Joaquin | 139 |
Sonoma | 99 |
Solano | 95 |
Stanislaus | 94 |
Monterey | 92 |
Santa Barbara | 88 |
Placer | 84 |
Tulare | 78 |
Marin | 55 |
San Luis Obispo | 53 |
Santa Cruz | 52 |
Butte | 51 |
Merced | 49 |
Shasta | 48 |
El Dorado | 42 |
Yolo | 41 |
Napa | 40 |
Imperial | 31 |
Humboldt | 30 |
Kings | 27 |
Madera | 23 |
Sutter | 21 |
Mendocino | 20 |
Nevada | 20 |
Lake | 15 |
Siskiyou | 14 |
Yuba | 14 |
San Benito | 11 |
Tehama | 11 |
Tuolumne | 11 |
Calaveras | 10 |
Amador | 9 |
Lassen | 9 |
Del Norte | 7 |
Plumas | 7 |
Glenn | 6 |
Inyo | 6 |
Mariposa | 6 |
Colusa | 5 |
Trinity | 5 |
Modoc | 4 |
Mono | 3 |
Alpine | 1 |
Sierra | 1 |
This section adds an interactive table for deeper exploration of your dataset:
And that’s the end of this module – in essence, it’s just a new way to organize and use an RMD to output a different product. And there are many more customizations and additional arguments that can further enhance your dashboard product, so be sure to take a look at the assigned chapter to learn about the possibilities! The final step is to click “Knit to flex_dashboard”. This will give you an HTML document that you can share with others.