The population count of Long Beach, CA was 468,883 in 2018. The population count of San Diego, CA was 1,401,932 in 2018. The population count of San Francisco, CA was 870,044 in 2018.
Population
Population Change
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Demographics and Population Datasets Involving Long Beach, CA or San Diego, CA or San Francisco, CA
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San Mateo County And California Crime Rates 2000-2014
performance.smcgov.org | Last Updated 2016-08-31T20:40:07.000ZViolent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.
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Vital Signs: Migration - Bay Area
data.bayareametro.gov | Last Updated 2019-10-25T20:40:04.000ZVITAL SIGNS INDICATOR Migration (EQ4) FULL MEASURE NAME Migration flows LAST UPDATED December 2018 DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables. DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration. Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23) One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.
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Census Places SF1 in Colorado 2000
data.colorado.gov | Last Updated 2024-05-28T11:03:58.000ZData includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).
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Census Counties in Colorado 2010
data.colorado.gov | Last Updated 2024-05-28T11:04:29.000ZCensus Data is collected every 10 years by mail surveys to every household with primary data collection fields of population, gender, race and number of occupants. Data includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).
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Census Counties SF1 in Colorado 2000
data.colorado.gov | Last Updated 2024-05-28T11:04:19.000ZCensus Data is collected every 10 years by mail surveys to every household with primary data collection fields of population, gender, race and number of occupants. Data includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).
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Census County Subdivisions SF1 in Colorado 2000
data.colorado.gov | Last Updated 2024-05-28T11:04:15.000ZCensus Data is collected every 10 years by mail surveys to every household with primary data collection fields of population, gender, race and number of occupants. Data includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).
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Census Counties SF3 in Colorado 2000
data.colorado.gov | Last Updated 2024-05-28T11:04:19.000ZCensus Data is collected every 10 years by mail surveys to every household with primary data collection fields of population, gender, race and number of occupants. Data includes demographics, education level, commute information, and more subset to Colorado by the Department of Local Affairs (DOLA).
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equity_priority_communities_2020_acs2018
data.bayareametro.gov | Last Updated 2024-01-15T03:20:02.000Zgis.plan.equity_priority_communities_2020_acs2018
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Public Housing
data.bayareametro.gov | Last Updated 2021-12-10T20:13:08.000ZThe feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States. According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. HCD downloaded the HUD data