- What is the Population Rate of Change?
- What is the Percent who did not finish the 9th grade?
- What is the Median Earnings?
- What is the Number of Employees?
- What is the High School Graduation Rate?
- What is the Median Female Earnings?
- What is the Percent Employed?
- What is the Percent with an associate's degree?
- What is the Median Male Earnings?
- What is the College Graduation Rate?
The population count of Contra Costa Centre, CA was 6,497 in 2018.
Demographics and Population Datasets Involving Contra Costa Centre, CA
- API healthstat.dph.sbcounty.gov | Last Updated 2019-03-13T19:07:43.000Z
Percent of People who Cannot Afford to Feed Themselves Sufficiently. U.S. Census Bureau, Current Population Survey, December Supplement (AKA USDA Food Security Supplement). Dissected by Year, Geographic Area, Age Category, and Race/Ethnicity.
- API data.bayareametro.gov | Last Updated 2020-04-13T23:25:38.000Z
VITAL SIGNS INDICATOR Housing Permits (LU3) FULL MEASURE NAME Permitted housing units LAST UPDATED October 2019 DESCRIPTION Housing growth is measured in terms of the number of units that local jurisdictions permit throughout a given year. A permitted unit is a unit that a city or county has authorized for construction. DATA SOURCE Construction Industry Research Board Table 3: Residential Units and Valuation (1967-2010) No link available California Housing Foundation/Construction Industry Research Board California Construction Trends (2011-2013) http://www.mychf.org/cirb/ Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database (2014-2017) http://opendata.mtc.ca.gov CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) Bay Area housing permits data prior to 2014 comes from the California Housing Foundation/Construction Industry Research Board. Data from 2014 to 2017 comes from the Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database. Single-family housing units include detached, semi-detached, row house and town house units. Row houses and town houses are included as single-family units when each unit is separated from the adjacent unit by an unbroken ground-to-roof party or fire wall. Condominiums are included as single-family units when they are of zero-lot-line or zero-property-line construction; when units are separated by an air space; or, when units are separated by an unbroken ground-to-roof party or fire wall. Multi-family housing includes duplexes, three-to-four-unit structures and apartment-type structures with five units or more. Multi-family also includes condominium units in structures of more than one living unit that do not meet the single-family housing definition. In the permits data from 2014 to 2017, single-family units include all units not strictly classified as multi-family. This may include secondary units. Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family and multi-family units. County data is available from 1967 whereas city data is available from 1990. City data is only available for incorporated cities and towns. All permits in unincorporated cities and towns are included under their respective county’s unincorporated total. Permit data is not available for years when the city or town was not incorporated. Affordable housing is the total number of permitted units affordable to low and very low income households. Housing affordable to very low income households are households making below 50% of the area median income. Housing affordable to low income households are households making between 50% and 80% of the area median income. Housing affordable to moderate income households are households making below 80% and 120% of the area median income. Housing affordable to above moderate income households are households making above 120% of the area median income. Permit data is missing for the following cities and years: Clayton, 1990-2007 Lafayette, 1990-2007 Moraga, 1990-2007 Orinda, 1990-2007 San Ramon, 1990 Building permit data for metropolitan areas for each year is the sum of non-seasonally adjusted monthly estimates from the Building Permit Survey. The Bay Area values are the sum of the San Francisco-Oakland-Hayward MSA and the San Jose-Sunnyvale-Santa Clara MSA. The counties included in these areas are: San Francisco, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, and San Benito. Permit values reflect the number of units permitted in each respective year.
- API data.bayareametro.gov | Last Updated 2019-10-25T20:40:04.000Z
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 firstname.lastname@example.org 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.
- API data.bayareametro.gov | Last Updated 2023-07-27T19:10:55.000Z
Displacement Typologies, developed by the University of California, Berkeley's Urban Displacement Project (UDP), use housing and demographic data from the United States Census, as well as real estate market data from Zillow to classify a metropolitan area's census tracts into eight distinct categories. Each category represents a stage of neighborhood change, although should not be taken to represent a linear trajectory or to predetermine neighborhood outcomes. Instead, typologies allow practitioners and researchers to see patterns in their regions over a specified time period, and are meant to start conversations about how policy interventions and investment could respond and support more equitable development. It is important to note that in considering the entire metropolitan region, UDP's typologies classify both low- and middle-income neighborhoods at risk of or experiencing displacement or gentrification, as well as high-income neighborhoods where housing markets are becoming increasingly 'exclusive' to low income residents. UDP believes that classifying tracts in such a way allows practitioners to get a broader picture of neighborhood dynamics, specifically the concentration of poverty and wealth within a region. UDP's Typologies have evolved over time in response to community and partner feedback and the availability of new data sources. This code represents the code's most recent iteration. It makes use of data from the 2014-2018 American Community Survey; 1990, 2010 and 2000 Decennial Census; and 2012-2017 Zillow Home Value and Rent Indices. For more information: ● Displacement typology data (includes graphic with category criteria) - https://github.com/urban-displacement/displacement-typologies ● Urban Displacement Project - https://www.urbandisplacement.org/
- API data.bayareametro.gov | Last Updated 2023-03-19T07:18:29.000Z
- API data.sfgov.org | Last Updated 2020-01-31T22:45:39.000Z
**Please refer to the downloadable XLSX attachment (http://bit.ly/SFMTATravelSurvey2019) for the complete dataset, metadata, and instructions for use.** This workbook provides data and data dictionaries for the SFMTA 2019 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 2.2: Mode Share target of 80% sustainable travel by 2030. • 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 801 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: 2019: May - August 2019 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.3% 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 = 801(Total Sample). Margin of error = +/- 3.3% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%