This dataset contains data included in the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt.

Geographic data used to create maps in the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Geography/p6uw-qxpv

Filter by the Indicator column to select data for a particular indicator.

User notes: 10/9/25 - for the report year 2025, data for the following indicators were uploaded with changes relative to report year 2023: Crime Rate: As of January 1, 2021, the FBI replaced the Summary Reporting System (SRS) with the National Incident Based Reporting System (NIBRS), which expands how crimes were recorded and classified. This report uses California’s version of NIBRS, the California Incident Based Reporting System (CIBRS), obtained from the SANDAG Open Data Portal. Crime rates are not disaggregated by jurisdiction, as in the previous Equity Indicator Report. Internet access: The age group variable was incorporated to account for notable disparities in internet access by age. Police Stops and Searches: refined methods. Agency data was aggregated to San Diego County because data was available for all agencies; previously data was available for three agencies. Analysis of RIPA data was updated to exclude stops where the stop was made in response to a call for service, combine transgender women and transgender men into a transgender category, and limit to contraband found during search. Used term “discovery rate” instead of “hit rate.” Removed comparison to traffic collision data and instead compared to population estimates from the American Community Survey. Jail Incarceration: new data sources. The numerator data for the average daily population data in jail was obtained from the San Diego County Sheriff's Office. Population data to calculate the rates was obtained from the San Diego Association of Governments (SANDAG). The terms for conviction status were corrected to "locally sentenced" and "unsentenced" for sentencing status. For jail population data, East African was reclassified as Black and Middle Eastern as White to allow for calculation of rates using SANDAG population estimates.

8/1/25 - for the report year 2025, the following change were made: Business Ownership: the minority and nonminority labels were switched for the population estimates and some of the race/ethnicity data for nonemployer businesses were corrected. Homelessness: added asterisks to category name for unincorporated regions to allow for a footnote in the figure in the story page.

7/11/25 - for the report year 2025, the following changes were made: Beach Water Quality: the number of days with advisories was corrected for Imperial Beach municipal beach, San Diego Bay, and Ocean Beach.

5/22/25 - for the report year 2023, the following changes were made: Youth poverty/Poverty: IPUMS identified an error in the POVERTY variable for multi-year ACS samples. In July 2024, they released a revised version of all multi-year ACS samples to IPUMS USA, which included corrected POVERTY values. The corrected POVERTY values were downloaded, and the analysis was rerun for this indicator using the 2021 ACS 5-year Estimates. Youth Poverty: data source label corrected to be 2021 for all years. Employment, Homeownership, and Cost-Burdened Households - Notes were made consistent for rows where category = Race/Ethnicity.

5/9/25 - Excluding data for the crime section indicators, data were appended on May 9, 2025 and the report will be updated to reflect the new data in August 2025. The following changes in methods were made: For indicators based on American Community Survey (ACS) data, the foreign-born category name was changed to Nativity Status. Internet access: Group quarters is a category included in the survey sample, but it is not part of the universe for the analysis. For the 2025 Equity Report year, respondents in group quarters were excluded from the analysis, whereas for the 2023 Equity Report year, these respondents were included. Adverse childhood experiences - new data source.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

This dataset contains parsed, extracted, and geocoded historical business and manufacturing data from Polk's San Diego City Directory using the open source directoreadr software. Images used for data extraction can be found at https://www.sandiego.gov/digitalarchives/collections/specialcollections/citydirectories.

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

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We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

This dataset is no longer updated as of April 2023.

Basic Metadata Note: The Sudden Infant Death Syndrome (SIDS) Rate is infant deaths (under one year of age) due to SIDS per 1,000 live births, by geography. Data set includes registered deaths only. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System, 2016. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Codes: ICD‐10 Mortality code R95.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Interpretation: "There were 5 SIDS deaths per 1,000 live births in Geography X".

Parcels represent taxable pieces of property. A parcel is created by the San Diego County Assessor/Recorder/County Clerk (ARCC) to identify a specific portion of real property that is taxed at a certain rate for a certain owner. Tax parcels are typically the same as a legally subdivided lot but are not necessessarily so. For example, a single owner may own a legally subdivided piece of property but there may be two or more tax parcels covering that property. Legal subdivisions are shown in the LOTS layer.Parcels are keyed to the Assessor Parcel Number (APN) and the parcel polygon identifier (PARCELID).The SanGIS parcel layers are “stacked” parcels. That means that for any piece of ground there may be multiple parcels. For example, a condominium building in downtown San Diego may have 200 individual condos. Each condo is a separate taxable parcel. All 200 parcels will be associated with the same physical lot on the ground. When the SanGIS parcel layer is created each individual condo has a polygon representing the physical location of the parent parcel. In this example there will be 200 polygons all stacked on top of each other that represent the taxable parcels and each polygon will have the same physical characteristics (shape, size, area, location) – they are, essentially, copies of each other. However, other associated information (owner, document numbers, etc) will be different for each. In this case, each condo unit will have its own parcel number and there will be no single parcel representing the lot on the ground. Besides condominiums there are two other cases where you will see stacked parcels – possessory interest and mobile homes. Possessory interests have Assessor Parcel Numbers (APNs) that start with 76x. A possessory interest (or PI) parcel represents a taxable interest in the underlying, or parent, parcel but not necessarily ownership. For instance, a private company may have an arrangement with a University to operate a business on campus – a coffee shop or gift shop for example. The private business is taxable and is assinged a 76x APN and that APN is associated with the parent parcel which is owned by the University. Possessory intestests do not represent ownership on the parcel, only a taxable interest in the underlying parent parcel.Mobile home parcel APNs start with 77x. In a manner similar to the possessory interests, mobile home owners own their home (coach) but not the underlying property on which the house sits. The actual mobile home is a separate taxable parcel associated with the mobile home park parent parcel. These taxable parcels all have the same polygon as the underlying parent parcel and will show as stacked parcels as well.This dataset contains parcels as shown on the Assessor Parcel Maps (APM). However, parcels shown in this layer may lag that of the official APM by a number of weeks due to how SanGIS is notified of the newly created parcel and the timing of publication of the parcel layer.This dataset contains the parcel polygon and associated parcel information provided by the County ARCC in thier Master Property Record (MPR file) and Parcel Assessment Record (PAR file). In addition to the MPR and PAR data assigned by ARCC, SanGIS may add situs address information if it has been provided by the addressing authority in which the parcel is situated. The situs address information provided by SanGIS may not be the same as the SITUS address data in the MPR.This dataset contains site address information along with owner names and addresses, and other property information. Key fields in this dataset include:Land use information provided in the NUCLEUS_USE_CD field (225 types with a 3-digit domain). The ASR_LANDUSE field is an older version of this field but comprises more generalized land uses (91 types). Generalized land use zoning information is provided in the NUCLEUS_ZONE_CD field. The ASR_ZONE field is an older version of this field. Land use zoning is generalized comprising 9 zone types. This can provide a useful approximation for parcels that are outside of the San Diego City and County zoning jurisdictions.Please note that land use and zoning fields are not regularly maintained by the Assessor's Office and should only be used as an approximate guide. Updates are only made when there is new construction, or a change in ownership. They are not updated when the County and Local Cities update their zoning data or when permit changes to properties are completed. Please refer to city and County official zoning datasets for official zoning information, and to SANDAG for more current land use data. NOTE: If the name of this layer includes "_NORTH", "_SOUTH", or "_EAST" it represents a subset of the entire San Diego County Parcel Base. That is, the "_NORTH" layer includes only parcels generally in the Northwestern portion of the County. The "_SOUTH" layer includes parcels in the Southwestern portion. And the "_EAST" layer includes parcels in the approximate Eastern half of the County.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/community_health_statistics/CHSU_Mortality.html#leading

Leading Causes of Death in San Diego County, by Gender, Race/Ethnicity, HHSA Region and Supervisorial District. Gender and race/ethnicity are at the county geographic level.

Notes: 1. Rank is based on total number of deaths in each of the National Center for Health Statistics (NCHS) "rankable" categories. The top 15 leading causes of death presented here are based on the San Diego County residents for each year.
2. Cause of death is based on the underlying cause of death reported on death certificates as classified by ICD-10 codes.
3. Deaths for specific demographics or geographic area may not equal the total deaths for San Diego County due to missing data.
§ Not shown for fewer than 5 deaths.

Source: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System.
Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2018.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Infant Mortality - VRBIS Dataset Note: This dataset is created from the mortality database. Cases are registered deaths only. The Infant Mortality Rate is infant deaths (under one year of age) per 1,000 live births, by geography. Rates not calculated in cases where infant's zip code of residence is unknown. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native. Blank Cells: Rates not calculated for fewer than 5 events.

Sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System, 2016.
Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Interpretation: "There were 5 infant deaths per 1,000 live births in Geography X".

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

This data was scraped from 22 county websites in the 2020 tax year. It includes the total annual property tax for each parcel listed. The included counties cover 86% of California's population:

Los Angeles County San Diego County Orange County Riverside County San Bernardino County Santa Clara County Alameda County Sacramento County Contra Costa County Fresno County Kern County San Francisco County Ventura County San Mateo County San Joaquin County Stanislaus County Sonoma County Tulare County Solano County Santa Barbara County Monterey County Placer County San Luis Obispo County Merced County Santa Cruz County Marin County Yolo County Butte County Napa County

Open source here: https://github.com/typpo/ca-property-tax

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data.

The Edwards aquifer in south-central Texas is one of the most permeable and productive aquifers in the United States and is the major source of public water supply for Bexar, Comal, Hays, Medina, and Uvalde Counties. The Edwards aquifer also supplies large quantities of water for use in agriculture, business, and industry in the region. The major artesian springs of the Edwards aquifer provide water for recreational activities, businesses, and downstream users, and provide habitat for several threatened or endangered species. The areas pertinent to the recharge process are the catchment area and the recharge zone. The catchment area consists of the parts of river basins upstream from the recharge zone. Streams in the catchment area generally gain water as they flow toward the recharge zone. As the streams flow across the recharge zone, which is essentially the outcrop of the Edwards aquifer, they lose substantial amounts of water through infiltration into the highly permeable, faulted and fractured rocks of the outcrop. Direct infiltration of rainfall also occurs in the interstream areas of the recharge zone. Annual recharge estimates are based on data collected from a network of U.S. Geological Survey (USGS) streamflow-gaging stations at sites upstream and downstream from the recharge zone and on assumptions related to applying runoff characteristics from gaged areas to ungaged areas, Puente (1978). Runoff is affected by the distribution of rainfall, which historically has been estimated from a network of rain gages, some operated by the USGS, some by the Edwards Aquifer Authority (EAA), and some by the National Weather Service. Since 2004, the distribution of rainfall has been estimated from calibrated NEXRAD (NEXt-generation weather RADar) data provided by the EAA. The annual estimation of recharge to the Edwards aquifer is part of a continuing program of the USGS in cooperation with the Edwards Aquifer Authority. This data release is formatted as a tab-delimited file (.txt) that contains the estimated annual recharge to the Edwards aquifer for 1934 through 2022. The estimated annual recharge is divided into eight major river basins within the catchment area and for some of those basins, will also include estimates of recharge from adjacent ungaged areas.

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data. The following utility companies are included:Pacific Gas and Electric (PG&E)Southern California Edison (SCE)San Diego Gas and Electric (SDG&E)Sacramento Municipal Utility District (SMUD)Layers included in this dataset:Power Outage Incidents - Point layer that shows data from all of the utilities and is best for showing a general location of the outage and driving any numbers in dashboards.Power Outage Areas - Polygon layer that shows rough power outage areas from PG&E only (They are the only company that feeds this out publicly). With in the PG&E territory this layer is useful to show the general area out of power. The accuracy is limited by how the areas are drawn, but is it good for a visual of the impacted area.Power Outages by County - This layer summaries the total impacted customers by county. This layer is good for showing where outages are on a statewide scale. If you have any questions about this dataset please email GIS@caloes.ca.gov