A dataset listing California counties by population for 2024.

This data release is comprised of geospatial and tabular data developed for the HayWired communities at risk analysis. The HayWired earthquake scenario is a magnitude 7.0 earthquake hypothesized to occur on the Hayward Fault on April 18, 2018, with an epicenter in the city of Oakland, CA. The following 17 counties are included in this analysis unless otherwise specified: Alameda, Contra Costa, Marin, Merced, Monterey, Napa, Sacramento, San Benito, San Francisco, San Joaquin, San Mateo, Santa Clara, Santa Cruz, Solano, Sonoma, Stanislaus, and Yolo. The vector data are a geospatial representation of building damage based on square footage damage estimates by Hazus occupancy class for developed areas covering all census tracts in 17 counties in and around the San Francisco Bay region in California, for (1) earthquake hazards (ground shaking, landslide, and liquefaction) and (2) all hazards (ground shaking, landslide, liquefaction, and fire) resulting from the HayWired earthquake scenario mainshock. The tabular data cover: (1) damage estimates, by Hazus occupancy class, of square footage, building counts, and households affected by the HayWired earthquake scenario mainshock for all census tracts in 17 counties in and around the San Francisco Bay region in California; (2) potential total population residing in block groups in nine counties in the San Francisco Bay region in California (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma); (3) a subset of select tables for 17 counties in and around the San Francisco Bay region in California from the U.S. Census Bureau American Community Survey 5-year (2012-2016) estimates at the block group level selected to represent potentially vulnerable populations that may, in the event of a major disaster, leave an area rather than stay; and (4) building and contents damage estimates (in thousands of dollars, 2005 vintage), by Hazus occupancy class, for the HayWired earthquake scenario mainshock for 17 counties in and around the San Francisco Bay region in California. The vector .SHP datasets were developed and intended for use in GIS applications such as ESRI's ArcGIS software suite. The tab-delimited .TXT datasets were developed and intended for use in standalone spreadsheet or database applications (such as Microsoft Excel or Access). Please note that some of these data are not optimized for use in GIS applications (such as ESRI's ArcGIS software suite) as-is--census tracts or counties are repeated (the data are not "one-to-one"), so not all information belonging to a tract or county would necessarily be associated with a single record. Separate preparation is needed in a standalone spreadsheet or database application like Microsoft Excel or Microsoft Access before using these data in a GIS. These data support the following publications: Johnson, L.A., Jones, J.L., Wein, A.M., and Peters, J., 2020, Communities at risk analysis of the HayWired scenario, chaps. U1-U5 of Detweiler, S.T., and Wein, A.M., eds., The HayWired earthquake scenario--Societal consequences: U.S. Geological Survey Scientific Investigations Report 2017-5013, https://doi.org/10.3133/sir20175013.

VITAL 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.

description: This workbook provides data and data dictionaries for the SFMTA 2017 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. Provide additional trip details, including trip purpose for each trip in the mode share question series. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 804 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the weight field when appropriate in your analysis. The survey period for this survey is as follows: 2017: February - April 2017 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.4% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: n = 804(Total Sample). Margin of error = +/- 3.4% n = 400. Margin of error = +/- 4.85% n = 100. Margin of error = +/- 9.80%; abstract: This workbook provides data and data dictionaries for the SFMTA 2017 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY2018 with a 95% confidence level and MOE +/- 5% or less. Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. Provide additional trip details, including trip purpose for each trip in the mode share question series. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 804 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the weight field when appropriate in your analysis. The survey period for this survey is as follows: 2017: February - April 2017 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.4% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: n = 804(Total Sample). Margin of error = +/- 3.4% n = 400. Margin of error = +/- 4.85% n = 100. Margin of error = +/- 9.80%