Overview

Column 1

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.

County Pollution Burden

This map shows average pollution burden and observation counts by county.

Column 2

Demographic Distribution by Race

The box plot shows racial percentage by California county:

Cases by County Section

This section summarizes the number of cases (or records) per county, providing insights into where the dataset is focused geographically.
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

Data Explorer

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.