SCAG has developed its regional geospatial dataset of land use information at the parcel-level (approximately five million parcels) for 197 local jurisdictions in its region. The regional land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. This is SCAG's 2016 regional land use dataset developed for the Final Connect SoCal, the 2020-2045 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), including general plan land use, specific plan land use, zoning code and existing land use. Please note this data was reviewed by local jurisdictions and reflects each jurisdiction's input received during the Connect SoCal Local Input and Envisioning Process.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.Data DictionaryField NameData TypeField DescriptionOBJECTIDObject IDInternal feature numberShapeGeometryType of geometrySCAGUID16Text2016 SCAG unique identification numberSCAGUID12Text2012 SCAG unique identification numberAPNTextAssessor’s parcel numberCOUNTYTextCounty nameCOUNTY_IDDoubleCounty FIPS codeCITYTextCity nameCITY_IDDoubleCity FIPS codeACRESDoubleAcreage informationYEARDoubleDataset yearCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeDENSITYDoubleAverage density of residential/housing development (dwelling unit per acre) permitted based on jurisdiction’s general planLOWDoubleMinimum density of residential/housing development permitted (dwelling unit per acre) based on jurisdiction’s general planHIGHDoubleMaximum density of residential/housing development permitted (dwelling unit per acre) based on jurisdiction’s general planYEAR_ADOPTDateYear when jurisdiction adopted/last updated current general plan land use elementGP12_CITYText2012 jurisdiction’s general plan land use designationGP12_SCAGText2012 SCAG general plan land use codeSP_NAMETextSpecific plan nameCITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeDENSITY_SPDoubleAverage density of residential/housing development (dwelling unit per acre) permitted based on jurisdiction’s specific planLOW_SPDoubleMinimum density of residential/housing development permitted (dwelling unit per acre) based on jurisdiction’s specific planHIGH_SPDoubleMaximum density of residential/housing development permitted (dwelling unit per acre) based on jurisdiction’s specific planYR_AD_SPDateYear when jurisdiction adopted/last updated current specific planSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeZN12_CITYText2012 jurisdiction’s zoning codeLU16Text2016 SCAG existing land use codeLU12Text2012 SCAG existing land use codeNOTESTextAdditional informationShape_LengthDoubleLength of feature in internal unitsShape_AreaDoubleArea of feature in internal units squared2016 SCAG Land Use CodesLegendLand Use DescriptionSingle Family Residential1110 Single Family Residential1111 High Density Single Family Residential (9 or more DUs/ac)1112 Medium Density Single Family Residential (3-8 DUs/ac)1113 Low Density Single Family Residential (2 or less DUs/ac)Multi-Family Residential1120 Multi-Family Residential1121 Mixed Multi-Family Residential1122 Duplexes, Triplexes and 2- or 3-Unit Condominiums and Townhouses1123 Low-Rise Apartments, Condominiums, and Townhouses1124 Medium-Rise Apartments and Condominiums1125 High-Rise Apartments and CondominiumsMobile Homes and Trailer Parks1130 Mobile Homes and Trailer Parks1131 Trailer Parks and Mobile Home Courts, High-Density1132 Mobile Home Courts and Subdivisions, Low-DensityMixed Residential1140 Mixed Residential1100 ResidentialRural Residential1150 Rural ResidentialGeneral Office1210 General Office Use1211 Low- and Medium-Rise Major Office Use1212 High-Rise Major Office Use1213 SkyscrapersCommercial and Services1200 Commercial and Services1220 Retail Stores and Commercial Services1221 Regional Shopping Center1222 Retail Centers (Non-Strip With Contiguous Interconnected Off-Street Parking)1223 Retail Strip Development1230 Other Commercial1231 Commercial Storage1232 Commercial Recreation1233 Hotels and MotelsFacilities1240 Public Facilities1241 Government Offices1242 Police and Sheriff Stations1243 Fire Stations1244 Major Medical Health Care Facilities1245 Religious Facilities1246 Other Public Facilities1247 Public Parking Facilities1250 Special Use Facilities1251 Correctional Facilities1252 Special Care Facilities1253 Other Special Use FacilitiesEducation1260 Educational Institutions1261 Pre-Schools/Day Care Centers1262 Elementary Schools1263 Junior or Intermediate High Schools1264 Senior High Schools1265 Colleges and Universities1266 Trade Schools and Professional Training FacilitiesMilitary Installations1270 Military Installations1271 Base (Built-up Area)1272 Vacant Area1273 Air Field1274 Former Base (Built-up Area)1275 Former Base Vacant Area1276 Former Base Air FieldIndustrial1300 Industrial1310 Light Industrial1311 Manufacturing, Assembly, and Industrial Services1312 Motion Picture and Television Studio Lots1313 Packing Houses and Grain Elevators1314 Research and Development1320 Heavy Industrial1321 Manufacturing1322 Petroleum Refining and Processing1323 Open Storage1324 Major Metal Processing1325 Chemical Processing1330 Extraction1331 Mineral Extraction - Other Than Oil and Gas1332 Mineral Extraction - Oil and Gas1340 Wholesaling and WarehousingTransportation, Communications, and Utilities1400 Transportation, Communications, and Utilities1410 Transportation1411 Airports1412 Railroads1413 Freeways and Major Roads1414 Park-and-Ride Lots1415 Bus Terminals and Yards1416 Truck Terminals1417 Harbor Facilities1418 Navigation Aids1420 Communication Facilities1430 Utility Facilities1431 Electrical Power Facilities1432 Solid Waste Disposal Facilities1433 Liquid Waste Disposal Facilities1434 Water Storage Facilities1435 Natural Gas and Petroleum Facilities1436 Water Transfer Facilities1437 Improved Flood Waterways and Structures1438 Mixed Utilities1440 Maintenance Yards1441 Bus Yards1442 Rail Yards1450 Mixed Transportation1460 Mixed Transportation and UtilityMixed Commercial and Industrial1500 Mixed Commercial and IndustrialMixed Residential and Commercial1600 Mixed Residential and Commercial1610 Residential-Oriented Residential/Commercial Mixed Use1620 Commercial-Oriented Residential/Commercial Mixed UseOpen Space and Recreation1800 Open Space and Recreation1810 Golf Courses1820 Local Parks and Recreation1830 Regional Parks and Recreation1840 Cemeteries1850 Wildlife Preserves and Sanctuaries1860 Specimen Gardens and Arboreta1870 Beach Parks1880 Other Open Space and Recreation1890 Off-Street TrailsAgriculture2000 Agriculture2100 Cropland and Improved Pasture Land2110 Irrigated Cropland and Improved Pasture Land2120 Non-Irrigated Cropland and Improved Pasture Land2200 Orchards and Vineyards2300 Nurseries2400 Dairy, Intensive Livestock, and Associated Facilities2500 Poultry Operations2600 Other Agriculture2700 Horse RanchesVacant3000 Vacant3100 Vacant Undifferentiated3200 Abandoned Orchards and Vineyards3300 Vacant With Limited Improvements3400 Beaches (Vacant)1900 Urban VacantWater4000 Water4100 Water, Undifferentiated4200 Harbor Water Facilities4300 Marina Water Facilities4400 Water Within a Military Installation4500 Area of Inundation (High Water)Specific Plan7777 Specific PlanUnder Construction1700 Under ConstructionUndevelopable or Protected Land8888 Undevelopable or Protected LandUnknown9999 Unknown

About this dataset

Context

The dataset presents the median household income across different racial categories in Orange County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Orange County population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 53.65% of the total residents in Orange County. Notably, the median household income for White households is $116,018. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $116,018.

https://i.neilsberg.com/ch/orange-county-ca-median-household-income-by-race.jpeg" alt="Orange County median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Orange County.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Orange County median household income by race. You can refer the same here

Comprehensive demographic dataset for Orange, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Orange County by race. It includes the distribution of the Non-Hispanic population of Orange County across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Orange County across relevant racial categories.

Key observations

Of the Non-Hispanic population in Orange County, the largest racial group is White alone with a population of 1.22 million (58.21% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Orange County
• Population: The population of the racial category (for Non-Hispanic) in the Orange County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Orange County total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Orange County Population by Race & Ethnicity. You can refer the same here

This is SCAG 2019 Regional Land Use dataset developed for the final 2024 Connect SoCal, the 2024-2050 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), including general plan land use, specific plan land use, zoning code, and existing land use at parcel-level (approximately five million parcels) for 197 local jurisdictions in the SCAG region.The regional land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. It is the most frequently and widely utilized SCAG geospatial data. From late 2019 to early 2020, SCAG staff obtained the 2019 parcel boundary GIS file and tax roll property information from county assessor’s offices. After months of data standardization and clean-up process, SCAG staff released the 2019 parcel boundary GIS files along with the 2019 Annual Land Use dataset in February 2021. In December 2021, SCAG staff successfully developed the preliminary dataset of the 2019 regional land use data and released the draft SCAG Data/Map Book in May 2022. The preliminary land use data was reviewed by local jurisdictions during the Local Data Exchange (LDX) process for Connect SoCal 2024. As a part of the final 2019 regional land use data development process, SCAG staff made every effort to review the local jurisdictions’ inputs and comments and incorporated any updates to the regional land use datasets. The products of this project has been used as one of the key elements for Connect SoCal 2024 plan development, growth forecasting, scenario planning, and SCAG’s policy discussion on various planning issues, as well as Connect SoCal key growth strategy analysis.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.2019 SCAG Land Use Codes: LegendLand Use Description Single Family Residential1110 Single Family Residential 1111 High Density Single Family Residential (9 or more DUs/ac) 1112 Medium Density Single Family Residential (3-8 DUs/ac) 1113 Low Density Single Family Residential (2 or less DUs/ac)Multi-Family Residential1120 Multi-Family Residential 1121 Mixed Multi-Family Residential1122 Duplexes, Triplexes and 2- or 3-Unit Condominiums and Townhouses1123 Low-Rise Apartments, Condominiums, and Townhouses1124 Medium-Rise Apartments and Condominiums1125 High-Rise Apartments and CondominiumsMobile Homes and Trailer Parks1130 Mobile Homes and Trailer Parks1131 Trailer Parks and Mobile Home Courts, High-Density1132 Mobile Home Courts and Subdivisions, Low-DensityMixed Residential1140 Mixed Residential1100 ResidentialRural Residential 1150 Rural ResidentialGeneral Office1210 General Office Use 1211 Low- and Medium-Rise Major Office Use 1212 High-Rise Major Office Use 1213 SkyscrapersCommercial and Services1200 Commercial and Services1220 Retail Stores and Commercial Services 1221 Regional Shopping Center 1222 Retail Centers (Non-Strip With Contiguous Interconnected Off-Street Parking) 1223 Retail Strip Development1230 Other Commercial 1231 Commercial Storage 1232 Commercial Recreation 1233 Hotels and MotelsFacilities1240 Public Facilities1241 Government Offices1242 Police and Sheriff Stations1243 Fire Stations1244 Major Medical Health Care Facilities1245 Religious Facilities1246 Other Public Facilities1247 Public Parking Facilities1250 Special Use Facilities1251 Correctional Facilities1252 Special Care Facilities1253 Other Special Use FacilitiesEducation1260 Educational Institutions1261 Pre-Schools/Day Care Centers1262 Elementary Schools1263 Junior or Intermediate High Schools1264 Senior High Schools1265 Colleges and Universities1266 Trade Schools and Professional Training FacilitiesMilitary Installations1270 Military Installations1271 Base (Built-up Area)1272 Vacant Area1273 Air Field1274 Former Base (Built-up Area)1275 Former Base Vacant Area1276 Former Base Air FieldIndustrial1300 Industrial 1310 Light Industrial1311 Manufacturing, Assembly, and Industrial Services1312 Motion Picture and Television Studio Lots1313 Packing Houses and Grain Elevators1314 Research and Development1320 Heavy Industrial1321 Manufacturing1322 Petroleum Refining and Processing1323 Open Storage1324 Major Metal Processing1325 Chemical Processing1330 Extraction1331 Mineral Extraction - Other Than Oil and Gas1332 Mineral Extraction - Oil and Gas1340 Wholesaling and WarehousingTransportation, Communications, and Utilities1400 Transportation, Communications, and Utilities 1410 Transportation1411 Airports1412 Railroads1413 Freeways and Major Roads1414 Park-and-Ride Lots1415 Bus Terminals and Yards1416 Truck Terminals1417 Harbor Facilities1418 Navigation Aids1420 Communication Facilities1430 Utility Facilities1431 Electrical Power Facilities1432 Solid Waste Disposal Facilities1433 Liquid Waste Disposal Facilities1434 Water Storage Facilities1435 Natural Gas and Petroleum Facilities1436 Water Transfer Facilities 1437 Improved Flood Waterways and Structures1438 Mixed Utilities1440 Maintenance Yards1441 Bus Yards1442 Rail Yards1450 Mixed Transportation1460 Mixed Transportation and UtilityMixed Commercial and Industrial1500 Mixed Commercial and IndustrialMixed Residential and Commercial1600 Mixed Residential and Commercial 1610 Residential-Oriented Residential/Commercial Mixed Use 1620 Commercial-Oriented Residential/Commercial Mixed UseOpen Space and Recreation1800 Open Space and Recreation 1810 Golf Courses 1820 Local Parks and Recreation 1830 Regional Parks and Recreation 1840 Cemeteries 1850 Wildlife Preserves and Sanctuaries 1860 Specimen Gardens and Arboreta 1870 Beach Parks 1880 Other Open Space and Recreation 1890 Off-Street TrailsAgriculture2000 Agriculture2100 Cropland and Improved Pasture Land2110 Irrigated Cropland and Improved Pasture Land2120 Non-Irrigated Cropland and Improved Pasture Land2200 Orchards and Vineyards2300 Nurseries2400 Dairy, Intensive Livestock, and Associated Facilities2500 Poultry Operations2600 Other Agriculture2700 Horse RanchesVacant3000 Vacant3100 Vacant Undifferentiated3200 Abandoned Orchards and Vineyards3300 Vacant With Limited Improvements3400 Beaches (Vacant)1900 Urban VacantWater4000 Water4100 Water, Undifferentiated4200 Harbor Water Facilities4300 Marina Water Facilities4400 Water Within a Military Installation4500 Area of Inundation (High Water)Specific Plan7777 Specific PlanUnder Construction1700 Under ConstructionUndevelopable or Protected Land8888 Undevelopable or Protected LandUnknown9999 Unknown

Comprehensive demographic dataset for University Park, Irvine, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for Woodbury, Irvine, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This data set represents the total number of Californians age 60 and over who were provided a home delivered meal from the Older Americans Act Title IIIC-2 Nutrition Services Program. Key sociodemographic variables include: age, high risk nutrition status, low income, lives alone and minority/non-minority.

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

Comprehensive demographic dataset for Woodbridge, Irvine, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

US Census American Community Survey (ACS) 2020, 5-year estimates of the key housing characteristics for Orange County, California. The data contains 406 fields for the variable groups H01: Housing occupancy (universe: total housing units, table X25, 3 fields); H02: Units in structure (universe: total housing units, table X25, 11 fields); H03: Population in occupied housing units by tenure by units in structure (universe: total population in occupied housing units, table X25, 13 fields); H04: Year structure built (universe: total housing units, table X25, 15 fields); H05: Rooms (universe: total housing units, table X25, 18 fields); H06: Bedrooms (universe: total housing units, table X25, 21 fields); H07: Housing tenure by race of householder (universe: occupied housing units, table X25, 51 fields); H08: Total population in occupied housing units by tenure (universe: total population in occupied housing units, table X25, 3 fields); H09: Vacancy status (universe: vacant housing units, table X25, 8 fields); H10: Occupied housing units by race of householder (universe: occupied housing units, table X25, 8 fields); H11: Year householder moved into unit (universe: occupied housing units, table X25, 18 fields); H12: Vehicles available (universe: occupied housing units, table X25, 18 fields); H13: Housing heating fuel (universe: occupied housing units, table X25, 10 fields); H14: Selected housing characteristics (universe: occupied housing units, table X25, 9 fields); H15: Occupants per room (universe: occupied housing units, table X25, 13 fields); H16: Housing value (universe: owner-occupied units, table X25, 32 fields); H17: Price asked for vacant for sale only, and sold not occupied housing units (universe: vacant for sale only, and sold not occupied housing units, table X25, 28 fields); H18: Mortgage status (universe: owner-occupied units, table X25, 10 fields); H19: Selected monthly owner costs, SMOC (universe: owner-occupied housing units with or without a mortgage, table X25, 45 fields); H20: Selected monthly owner costs as a percentage of household income, SMOCAPI (universe: owner-occupied housing units with or without a mortgage, table X25, 26 fields); H21: Contract rent distribution and rent asked distribution in dollars (universe: renter-occupied housing units paying cash rent and vacant, for rent, and rented not occupied housing units, table X25, 7 fields); H22: Gross rent (universe: occupied units paying rent, table X25, 28 fields), and; X23: Gross rent as percentage of household income (universe: occupied units paying rent, table X25, 11 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

Comprehensive demographic dataset for Mission Viejo, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

US Census American Community Survey (ACS) 2019, 5-year estimates of the key housing characteristics for Orange County, California. The data contains 406 fields for the variable groups H01: Housing occupancy (universe: total housing units, table X25, 3 fields); H02: Units in structure (universe: total housing units, table X25, 11 fields); H03: Population in occupied housing units by tenure by units in structure (universe: total population in occupied housing units, table X25, 13 fields); H04: Year structure built (universe: total housing units, table X25, 15 fields); H05: Rooms (universe: total housing units, table X25, 18 fields); H06: Bedrooms (universe: total housing units, table X25, 21 fields); H07: Housing tenure by race of householder (universe: occupied housing units, table X25, 51 fields); H08: Total population in occupied housing units by tenure (universe: total population in occupied housing units, table X25, 3 fields); H09: Vacancy status (universe: vacant housing units, table X25, 8 fields); H10: Occupied housing units by race of householder (universe: occupied housing units, table X25, 8 fields); H11: Year householder moved into unit (universe: occupied housing units, table X25, 18 fields); H12: Vehicles available (universe: occupied housing units, table X25, 18 fields); H13: Housing heating fuel (universe: occupied housing units, table X25, 10 fields); H14: Selected housing characteristics (universe: occupied housing units, table X25, 9 fields); H15: Occupants per room (universe: occupied housing units, table X25, 13 fields); H16: Housing value (universe: owner-occupied units, table X25, 32 fields); H17: Price asked for vacant for sale only, and sold not occupied housing units (universe: vacant for sale only, and sold not occupied housing units, table X25, 28 fields); H18: Mortgage status (universe: owner-occupied units, table X25, 10 fields); H19: Selected monthly owner costs, SMOC (universe: owner-occupied housing units with or without a mortgage, table X25, 45 fields); H20: Selected monthly owner costs as a percentage of household income, SMOCAPI (universe: owner-occupied housing units with or without a mortgage, table X25, 26 fields); H21: Contract rent distribution and rent asked distribution in dollars (universe: renter-occupied housing units paying cash rent and vacant, for rent, and rented not occupied housing units, table X25, 7 fields); H22: Gross rent (universe: occupied units paying rent, table X25, 28 fields), and; X23: Gross rent as percentage of household income (universe: occupied units paying rent, table X25, 11 fields). The US Census geodemographic data are based on the 2019 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census 2010 (SF1) housing characteristics for Orange County, California. The layer contains housing-level data for tables H1-H16 of the US Census 2010 SF1 dataset for Elementary School districts in Orange County. The US Census geodemographic data are based on the 2010 TigerLines across multiple census geographies. The spatial geographies were merged with SF1 and SF2 demographic data tables for both Housing and Population characteristics.

Comprehensive demographic dataset for Balboa Peninsula, Newport Beach, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

US Census 2010 (SF1) housing characteristics for Orange County, California. The layer contains housing-level data for tables H1-H16 of the US Census 2010 SF1 dataset for Census ZIP Code Tabulation Areas in Orange County. The US Census geodemographic data are based on the 2010 TigerLines across multiple census geographies. The spatial geographies were merged with SF1 and SF2 demographic data tables for both Housing and Population characteristics.

This dataset includes the boundaries for California Assembly Districts in Orange County. The data is based upon information from the State of California Citizens Redistricting Commission Final Report on 2011 Redistricting (August 15, 2011).

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people’s everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data. All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Methods Survey data was collected via the Qualtrics platform. The survey contains 52 questions. It was distributed to the general population in zip codes within the counties of Los Angeles and Orange. It was also distributed directly to members of a large credit union headquartered in Orange County (“large” according to NCUA asset classes). Participants were eligible to complete the survey if they live in Orange County or Los Angeles County, are older than 18, and have a combined household income of less than $100,000. Incomplete responses have been removed. The survey yielded 1,370 complete responses (1,213 from the general population participants and 157 from members of the large credit union).

US Census American Community Survey (ACS) 2019, 5-year estimates of the key demographic characteristics for Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2019 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).