DOI: 10.5061/dryad.3xsj3txnw
title: Surface water supply allocation, crop, and disadvantaged community data for the San Joaquin Valley, CA, 2016

description: Societies globally are struggling to meet freshwater demands while
 agencies attempt to address water access inequities under a rapidly
 changing climate and growing population. An understanding of dynamic
 interactions between people and water, known as sociohydrology, regionally
 could provide approaches to addressing local water mismanagement and water
 access inequity. In semi-arid California, local water agencies, primarily
 agricultural irrigation districts, are at the intersection of rethinking
 approaches to balance freshwater demands. More than 150 years of complex
 water governance and management have defined San Joaquin Valley irrigation
 districts and the region's water access inequities and
 sociohydrologic instability. Data in this dataset supported analysis of
 water governance, specifically including surface water and groundwater
 dependence within and outside of irrigation districts. Additional data
 includes disadvantaged community designation, allowing for assessment of
 inequities between water users and the relationship to broader societal
 inequities.
description: Data Availability & Software This
 study brings together detailed data from different local and state sources
 about irrigation districts. It uses this data to figure out the factors
 (e.g., the district's history, politics, environment, and cultural
 characteristics) that influence the shortage of surface water and the
 dependence on groundwater in this agricultural area. Table 1 lists
 datasets and sources. The data sources table in the README describes
 variables calculated from the datasets highlighted in Table 1. The major
 variables used in this analysis show an irrigation district’s history
 (i.e., age, dedicated water amount), surface water allocation and
 delivery, and crop composition within the district’s boundaries (e.g.,
 total crop fraction, perennial crop fraction, annual crop fraction, and
 revenue). This dataset has the following tables as separate CSV
 files: Table 7 includes the variable values
 per irrigation district (freshwater variable normalized values
 reported) Table 8 includes the surface water
 allocation amounts for irrigation districts in this study and the source
 of information Table 9 specifies the Land IQ crop
 types that make up the annual, perennial, and irrigated forage
 categories Table 10 lists the crop revenue values and
 the associated crop type used in the analysis for irrigation districts
 within the eight San Joaquin Valley counties. 
 The 2016 county crop report for each county is used to derive
 crop revenue values. The primary software used to facilitate this analysis
 is Esri ArcGIS Pro 2.7.1 and R software (R Core Team, 2021).
 <strong>Irrigation District
 Boundaries </strong> The most up-to-date
 irrigation district boundaries were obtained directly from the Local
 Agency Formation Commission (LAFCO) for seven counties in the San Joaquin
 Valley—San Joaquin, Stanislaus, Merced, Fresno, Madera, Tulare, and Kern.
 Kings County LAFCO could not provide updated boundaries, and the
 Department of Water Resources 2015 water agency boundaries were used for
 irrigation districts in this county. This study focuses solely on water
 agencies in the San Joaquin Valley floor that distribute water for
 irrigation and exclude water conservation, domestic, and municipal water
 agencies. The irrigation district boundaries from these various sources
 were combined to create a single geospatial data file of irrigation
 districts in the San Joaquin Valley using ArcGIS software.
 Era Analysis Statistical analysis of the
 variables is done for irrigation districts within four major historical
 eras to shed light on how key water management events may have shaped
 irrigation districts over their formation. Irrigation district formation
 dates were categorized into major water management historical eras for
 infrastructure investments and economic development as outlined by Hanak et al. (2011). The four major eras considered are the Era of Local Organization (1887-1913), Hydraulic Era (1914-1968), Era of Conflict (1969-2000), and Era of Reconciliation (2001-2020), mainly following Hanak et al. (2011). <strong>Groundwater Reliance Calculation</strong> Groundwater use in the San Joaquin Valley has been measured by irrigation districts and estimates have relied mostly on crop use estimated modeling studies for decades (Famiglietti, 2014). Hence, resolving disaggregated data and understanding of groundwater use across the region will be improved through the implementation of SGMA. Key datasets used to quantify the estimates of groundwater reliance per irrigation district in this study were: The 2016 crop land use data for California (often called Land IQ 2016) electronic Water Rights Information Management System (eWRIMS) (State Water Resources Control Board, 2020) - archive link U.S. Bureau of Reclamation agricultural contractors list A gridded-based water balance model called Water Footprint Analysis in R (WAFR)  (Booth, 2018) that uses crop coefficients to estimate crop water requirements. The Land IQ 2016 dataset includes primary agricultural land use, wetlands, and urban boundaries for 58 counties in California derived for 2016. This study uses only agricultural land use classifications from the Land IQ 2016 dataset to calculate crop composition within irrigation district boundaries. Crop composition within irrigation districts also served as an input to the WAFR model to calculate crop water requirements for each district. Surface water allocation amounts were obtained from various sources— including eWRIMS, USBR agricultural contract amount lists and reports, Groundwater Sustainability Plans (GSP), Agricultural Water Management Plans (AWMP), and irrigation district web pages. Surface water delivery averages from 2001-2015 were obtained from (Jezdimirovic, Hanak, & Escriva-bou, 2020 - archive link) except for Banta Carbona Irrigation Districts, Byron-Bethany Irrigation District, and South San Joaquin Irrigation District. Average 2008-2019 surface water deliveries 2008-2019 for Banta-Carbona and Byron-Bethany irrigation districts were obtained from Tracy Subbasin GSP and South San Joaquin Irrigation District 2005-2019 average surface water deliveries were obtained from their 2020 AWMP. The water budget equation (Eqn. 1) is used to derive estimates of groundwater reliance per irrigation district, meaning the amount of groundwater needed to make up for irrigation demand unmet by surface water, defined as: ∆S+ P+Q<sub>GW</sub>+ Q<sub>SW</sub>-ET=0 (Eqn.1) Where ΔS is the change in water storage, P is precipitation, Q<sub>GW</sub> is groundwater outflow, Q<sub>SW</sub> is surface water runoff, and ET is evapotranspiration. For this project, a series of assumptions were made to quantify the reliance on groundwater for    each irrigation district in the San Joaquin Valley using the water budget equation, these are: Precipitation, P, varies by irrigation district. Precipitation observations from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were used in the WAFR model to obtain the proportion of crop water requirements for irrigation districts. For more information on the data processing and WAFR model, refer to Booth (2018). Q<em><sub>SW</sub></em> varies across irrigation districts, and values are based on surface water allocations determined by each irrigation district’s surface water right amount. This study assumes that irrigation districts have 100% allocation of their claimed water rights to meet irrigation demands (i.e., crop water requirements) to simulate a districts groundwater dependence and crop water demand during drought with full surface water capacity. Refer to SI Table 1 for more details on surface water allocation sources. Given that most groundwater basins in the San Joaquin Valley have been designated as critically overdrafted by the Department of Water Resources, the likelihood is that the volume of groundwater outflow, Q<sub>GW</sub> is sufficiently substantial and included in the water budget is speculative. Although shallow groundwater drainage and water quality is a management concern, there has been a decline over time and limited discharge and water quality in delta. Therefore, the total volume of water from lateral exchange is not substantial in volume, but it is recognized that it could affect water quality (Schoups et al., 2005). Crop water requirements (CWR) were calculated by accumulating daily crop evapotranspiration demand during the growing season within a given location and using a crop coefficient of evapotranspiration, ETc. For more information on the data processing and WAFR model, refer to Booth (2018). This analysis quantifies irrigation district groundwater runoff, QGW, based on surface water allocation amounts (Eqn. 2) and average surface water delivery for irrigation districts (Eqn. 3). SW<sub><em>allocation</em> </sub>- CWR = ± SW (Eqn. 2) and SW<sub><em>delivery</em> </sub>- CWR = ± SW (Eqn. 3), SW<em><sub>allocation</sub></em> is an irrigation district’s surface water allocation, SW<em><sub>delivery</sub></em> is an irrigation district’s surface water delivery, and CWR is an irrigation district’s crop water requirement. If Equation 2 or 3 results in surface water surplus, SWS, then it is assumed that an irrigation district is not reliant on groundwater to meet irrigation demands or CWR. Whereas, if Equation 2 or 3 results in surface water deficit, SWD, it is assumed that an irrigation district does not have enough surface water allocations or average surface water deliveries to meet irrigation demands and relies on groundwater to meet CWR amounts. Irrigation districts with surface water delivery of “no record” are assumed to receive no surface water delivery to facilitate calculating the surface water delivery surplus/deficit.  <strong>Irrigation District and GDC Disadvantaged Community Comparison</strong> The CalEnviroScreen 4.0 dataset (archive link) is obtained for the most recent environmental health hazard assessment (2018) from the California Office of Environmental Health Hazard Assessment (OEHHA) (California Office of Environmental Health Hazard Assessment, 2018), and the most up to date (2018) DAC census places boundaries were obtained from the Department of Water Resources (DWR) DAC Mapping Tool California Department of Water Resources, 2018). The CalEnviroScreen 4.0 dataset provides several indicators that reflect environmental conditions or poverty vulnerability for populations at the census tract level. The DAC census place boundaries provide the area, name, and location of DACs in California, reduced to the San Joaquin Valley floor (See shaded grey area in map, below) for this analysis. To assess environmental and poverty conditions in San Joaquin Valley's DACs, we combined the CalEnviroScreen 4.0 dataset with DAC census place centroids using Esri ArcGIS Pro software. We also used irrigation district boundaries to identify Groundwater Dependent Communities (GDCs) on the valley floor, which are areas not served by irrigation districts and are highly dependent on groundwater to meet domestic water needs. Descriptive statistics (e.g., mean, median) were used to compare the traits between DACS with GDCs and irrigation districts, and an unpaired two-sample Wilcoxon test comparing the mean of the variables between the two groups is used to derive the p-value (α=0.05).
description: # San Joaquin Valley Surface Water Supply and Crop Data
 [https://doi.org/10.5061/dryad.3xsj3txnw](https://doi.org/10.5061/dryad.3xsj3txnw) This data package includes documentation and tables associated with the "The paradox of production: surface water supply ensures productivity but not prosperity in California’s San Joaquin Valley" by Espinoza and Viers. The dataset includes four tables and field definitions. Full methodology and interpretation of the data is supplied in the associated paper. The tables support exploration of: * access to surface water based on irrigation districts' water rights priority dates * groundwater dependence of irrigation districts * water access for disadvantaged communities within and outside of irrigation districts ## Description of the data and file structure This dataset includes both a single Microsoft Excel file and CSV files with the same data as well as a PDF listing data sources. The Excel file has five tables, labeled for their numbers in the supplementary material for the related paper: * **Table 3**: Irrigation District DAC (ID-DAC) and groundwater dependent communities (GDC)  list and statistics * **Table 7**: Irrigation districts and their associated variables including group/cluster assignment (Columns= variables and rows = Irrigation Districts) * **Table 8**: Surface water allocation amounts for irrigation districts included in this analysis and value sources * **Table 9**: Land IQ crop types for the year 2016 categorized into annual, perennial, and irrigated forage categories * **Table 10**: Table of crop revenue per crop type used for each county; revenue values obtained from each county crop report (2016) Where the value `NA` appears in the data tables, it means `Not Available`. ## Data Sources A full list of data sources is included in the files in this repository as *EspinozaViers_Table1_DataSources.pdf* ## Abbreviations used in field definitions * ACS: American Communities Survey * CVP: Central Valley Project * DAC: Disadvantaged Community * DWR: Department of Water Resources * eWRIMS: Electronic Water Rights Information Management System * GSP: Groundwater Sustainability Plan * ID: Irrigation Districts * LAFCO: Local Agency Formation Commission * SSJID: South San Joaquin Irrigation District * SWP: State Water Project * SWRCB: State Water Resources Control Board * USBR: United States Bureau of Reclamation ## Field Definitions | **Variable** | **Acronym** | **Unit** | **Description** | | ------------------------------------------------------------ | ---------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Irrigation District Traits** | | | | | Irrigation District Unique Identification | UID | NA | Unique identification created for each irrigation district (ID) | | Irrigation District Short Name | IDShortName | NA | Shortened version of the ID name | | Formation | Formation | Year | Sourced from ID websites, Agricultural Water Management Plans (AWMPs), Groundwater Sustainability Plans (GSPs) | | Age\* | Age | Years (Yrs.) | Deduced from 'Formation' year (2021 – Year Formed) | | Formation Era | Era | Year Range | There are four formation eras reflective of major water management events in California: 1887-1913, 1914-1968, 1969-2000, 2001-2020 based on [Hanak et al, 2011](https://www.ppic.org/publication/managing-californias-water-from-conflict-to-reconciliation/) | | Era Name | EraLabel | Name | Era names are as following in chronological order based on Formation Era description: Era of Local Organization, Hydraulic Era, Era of Conflict, Era of Reconciliation based on [Hanak et al, 2011](https://www.ppic.org/publication/managing-californias-water-from-conflict-to-reconciliation/) | | Service Area | ServArea\_Ha | Hectares (Ha) | Based off the LAFCO irrigation district boundaries; calculate the area in ArcGIS in Acres and Hectares; validated using ID websites and other resources | | Latitude | LAT | Decimal degrees | y-coordinate of the centroid of ID boundaries; for ID with multiple polygons the polygon with the larger area was selected | | Longitude | LON | Decimal degrees | x-coordinate of the centroid of ID boundaries; for ID with multiple polygons the polygon with the larger areas was selected | | Group Name | GroupName | NA | The group named reflects the irrigation district’s cluster group | | **Surface Water Allocation Variables** | | | | | Surface Water Allocation | SWAlloc | Megaliter (ML) | The amount of 100% surface water allocation in a year (i.e., total surface water rights); Amounts obtained from SWRCB eWRIMS database, USBR contract lists, Agricultural Water Management Plans, and Groundwater Sustainability Plans; sum of all surface water allocation sources (e.g., CVP, SWP, other) | | Normalized Surface Water Allocation\* | SWAlloc\_MLHa | Megaliter per Hectare (ML/Ha) | Derived by dividing the surface water allocation by the ID crop area (Ha) | | Pending Surface Water Allocations | PendingSW | ML | The amount of surface water allocation pending approval by the State Water Resource Control Board (SWRCB); data from SWRCB electronic Water Rights Information Management System (eWRIMS) | | Normalized Pending Surface Water Allocations\* | PendingSW\_MLHa | ML/HA | Derived by dividing the pending surface water allocation amounts by irrigation district crop areas (Ha) | | Surface Water Delivery (Average) | SWDelivery | ML | Based on average surface water deliveries from 2001-2015 as reported by (Jezdimirovic *et al.*, 2020b) for available irrigation districts. Banta-Carbona ID and Byron-Bethany ID surface water delivery average from 2008-2019; data from Tracy Subbasin GSP. South San Joaquin ID surface water delivery average from 2005-2019; Data from SSJID 2020 Agricultural Water Management Plan | | Normalized Surface Water Delivery\* | SWDelivery\_MLHa | ML/Ha | Derived from dividing the Surface Water Delivery by the ID crop area | | Central Valley Project Water (CVP) Allocation | TheoCVP | ML | Values obtained from USBR contract water allocation reports, Agricultural Water Management Plans, and Groundwater Sustainability Plans | | Normalized Central Valley Project Water Allocation\* | TheoCVP\_MLHa | ML/Ha | Derived from dividing the CVP allocation by the ID crop area | | State Water Project (SWP) Allocation | TheoSWP | ML | Values obtained from DWR contract water allocation reports, Agricultural Water Management Plans, and Groundwater Sustainability Plans | | Normalized State Water Project Allocation\* | TheoSWP\_MLHa | ML/Ha | Derived from dividing the SWP by the ID crop area | | Difference in Surface Water Allocation vs Delivery\* | DiffThRel\_MLHa | AF/Ha | The difference between surface water allocation and actual average amount of surface water delivered | | Crop Water Requirement | CWR | ML | Deduced from WAFR model ([Booth et al., 2018](https://escholarship.org/uc/item/7w19h3j3)) CWR output on Land IQ 2016 data for San Joaquin Valley applied to Land IQ 2016 land uses; Sum of the CWU of all crops within the IDs; CWR is derived by the evapotranspiration of blue water (surface water/groundwater) divided by the harvested acres within an irrigation district, which results in the depth of water multiplied by the ID crop area | | Normalized Crop Water Requirement\* | NCWR16\_AFHa | ML/Ha | Derived by dividing the CWR for Land IQ 2016 by the ID crop area | | Surface Water Allocation Surplus/Deficit | SWAllocSurDef | ML | Difference between Surface Water Allocation and CWR | | Normalized Surface Water Allocation Surplus/Deficit \* | SWAllocSurDef\_MLHa | ML/Ha | Derived by dividing the surplus/deficit amounts resulting after meeting CWR based on surface water allocation by ID crop area | | Surface Water Delivery Surplus/Deficit | SWDelSurDef | ML | Difference between Surface Water Delivery and CWR | | Normalized Surface Water Delivery Surplus/Deficit \* | SWDelSurDef\_MLHa | ML/Ha | Derived by dividing the surplus/deficit amounts resulting after meeting CWR based on surface water delivery by ID crop area | | **Crop Variables** | | | | | Total Irrigated Crop Area | CropFct | Ha | Obtained from Land IQ 2016 data for crops within IDs; includes Mixed Pasture & Miscellaneous Grasses | | Fraction of Total Irrigated Crop Area\* | CropFct | Fraction | Divided the Total Irrigated Crop Area by the ID Service Area | | Perennial Crop Area | PerennialCropArea | Ha | Obtained from Land IQ 2016 data for crops within IDs; Categorized Land IQ perennial crops as perennial to create this variable | | Fraction of Perennial Crops \* | PerenFct | Fraction | Deduced dividing by ID perennial area by the ID total crop area | | Annual Crop Area | AnnCrpArea | Ha | Obtained from Land IQ 2016 data for crops within IDs; Categorized Land IQ annual crops as annual to create this variable | | Fraction Annual Crop Area\* | AnnualFct | Fraction | Deduced by dividing each ID annual crop area by ID crop area | | Irrigated Forage Crop Area | IrrigPastCropArea | Ha | Obtained from Land IQ 2016 data for crops within IDs; Categorized pasture, Miscellaneous Grain and Hay, Miscellaneous Grasses, and Alfalfa as irrigated forage | | Fraction Irrigated Forage Area\* | IrrigPastFct | Fraction | Deduced by dividing each ID irrigated forage area by ID crop area | | Fraction for Top Crops in the San Joaquin Valley\* | Fct\_\[Crop Name] | Fraction | Obtained the areas for the top crops in the San Joaquin Valley from Land IQ 2016 data for crops within IDs; The top crops in the San Joaquin Valley are Almond, Walnuts, Grapes, Cotton, and Citrus. | | **Crop Economic Variables** | | | | | Total Crop Revenue | TotCropRev | USD | Land IQ 2016 data was used to calculate the sum of acres across all crop types within each ID. County Crop Reports 2016 were used to assign crop revenues to associated crops (for more details on Crop Report values used per crop type see Table 7). The final total crop revenue is the sum of revenue for all crop types per ID | | Normalized Total Crop Revenue \* | TotCropRev\_USDHA | USD/Ha | Derived by dividing the total crop revenue by ID crop area | | Annual Crop Revenue | AnnualRev | USD | Land IQ 2016 data was used to calculate the sum of acres across all crop types within each ID. County Crop Reports 2016 were used to assign crop revenues to associated crops (for more details on Crop Report values used per crop type see Table 7). The final annual crop revenue is the sum revenue for all annual crop types per ID | | Normalized Annual Crop Revenue \* | AnnualRev\_USDHA | USD/Ha | Derived by dividing the annual crop revenue by ID annual crop area | | Perennial Crop Revenue | PerennCropRev | USD | Land IQ 2016 data was used to calculate the sum of acres across all crop types within each ID. County Crop Reports 2016 were used to assign crop revenues to associated crops (for more details on Crop Report values used per crop type see Table 7). The final perennial crop revenue is the sum revenue for all perennial crop types per ID | | Normalized Perennial Crop Revenue \* | PerennRev\_USDHa | USD/Ha | Derived by dividing the perennial crop revenue by ID perennial crop area | | **Disadvantaged Community Variables** | | | | | DAC Name | DACName | NA | Identifier of disadvantaged communities | | Associated Irrigation District or Groundwater Dependent Area | ID\_WA | NA | Derived by joining DAC centroids to irrigation district and groundwater dependent region  vector shapefile | | DAC Population (2018) | Pop18 | NA | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset | | Median Household Income (2018) | MHI18 | USD | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset | | DAC Severity Status | Status | NA | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset | | DAC Longitude | Longitude | Decimal degrees | Derived by finding the coordinate of each DAC polygon centroid in ESRI ArcPro software | | DAC Latitude | Latitude | Decimal degrees | Derived by finding the coordinate of each DAC polygon centroid in ESRI ArcPro software | | CalEnviroScreen4.0 Score Percentile | CIScoreP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen4.0 states that derived from CalEnviroScreen Score which is the Pollution Score multiplied by Population Characteristics Score; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | PM2.5 Percentile | pmP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen4.0 states that derived from PM2.5 Score which is the annual mean PM2.5 concentrations; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Drinking Water Score Percentile | drinkP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 states that it is the drinking water contaminant index for selected contaminants; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Groundwater Threats Score Percentile | gwthreatsP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 states that it is the percentile of the sum of weighted GeoTracker leaking underground storage tank sites within buffered distances to populated blocks of census tracts; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Pollution Score Percentile | PollutionP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen states that the Pollution Burden is the average of percentiles from the Pollution Burden indicators (with a half weighting for the Environmental Effects indicators) and the Pollution Burden Score is the Pollution Burden variable scaled with a range of 0-10. (Used to calculate CES 4.0 score) which is used for the pollution burden percentile; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Asthma Percentile | asthmaP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; is the percentile of the age-adjusted rate of emergency department visits for asthma; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Poverty Percentile | povP | Percentile | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen states that it is the percentile of the percent of the population living below twice the federal poverty level; See CalEnviroScreen Data Dictionary for more details [https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40](https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40) | | Hispanic Population Percentage | Hispanic\_pct | Percent | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 derives this from 2019 ACS population estimates of the percent per census tract of those who identify as Hispanic or Latino | | White Population Percentage | White\_pct | Percent | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 derives this from 2019 ACS population estimates of the percent per census tract of those who identify as non-Hispanic white | | African American Population Percentage | African\_American\_pct | Percent | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 derives this from 2019 ACS population estimates of the percent per census tract of those who identify as non-Hispanic African American or black | | Native American Population Percentage | Native\_American\_pct | Percent | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 derives this from 2019 ACS population estimates of the percent per census tract of those who identify as non-Hispanic Native American | | Asian American Population Percentage | Asian\_American\_pct | Percent | Derived by joining DAC census place centroids to the CalEnviroScreen vector dataset; CalEnviroScreen 4.0 derives this from 2019 ACS population estimates of the percent per census tract of those who identify as non-Hispanic Asian or Pacific Islander | ## Questions and More Information For questions contact Vicky Espinoza ([espinoza.vicky42@gmail.com](mailto:espinoza.vicky42@gmail.com)) or Joshua Viers (PI; [jviers@ucmerced.edu](mailto:jviers@ucmerced.edu))