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Vital Signs: Vulnerability To Sea Level Rise - Inundation Areas Shapefile (lower resolution)
data.bayareametro.gov | Last Updated 2018-07-06T18:04:14.000ZVITAL SIGNS INDICATOR Vulnerability to Sea Level Rise (EN11) FULL MEASURE NAME Share of population living in zones at risk from various sea level rise forecast scenarios LAST UPDATED July 2017 DESCRIPTION Vulnerability to sea level rise refers to the share of the historical and current Bay Area population located in areas at risk from forecasted sea level rise over the coming decades. Given that there are varying forecasts for the heightened high tides (i.e., mean highest high water mark), projected sea level impacts are presented for six scenarios ranging from a one foot rise to six feet. A neighborhood is considered vulnerable to sea level rise when at least 10 percent of its land area is forecasted to be inundated by peak high tides in the coming years. The dataset includes at-risk population and population share data for the region, counties, and neighborhoods. DATA SOURCE San Francisco Bay Conservation and Development Commission/Metropolitan Transportation Commission ART (Adaption to Rising Tides) Bay Area Sea Level Rise Analysis and Mapping Project (2017) 2017 Sea Level Rise Maps http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Projected areas of inundation were developed by BCDC and NOAA at one-foot intervals ranging from one foot to four feet of sea level rise. Regional and local sea level rise analysis is based on data from BCDC’s ART (Adapting to Rising Tides) Bay Area Sea Level Rise and Mapping Project. This data reflects the most up-to-date and detailed sea level rise mapping for the Bay Area. Sea level rise analysis for metro areas is based on national sea level rise mapping from NOAA, which is best for metro-to-metro comparison. To determine the impacts on historical and current populations, inundation areas were overlaid on a U.S. Census shapefile of 2010 Census tracts using Census Bureau population data. Because census tracts can extend beyond the coastline, the baseline scenario of zero feet was used to determine existing sea level coverage of census tracts. Sea level rise refers to the change from this level. The area of the tract was determined by measuring the component of the tract area not currently under water. This area, rather than the total tract area, was used as the denominator to determine the percentage of the census tract that is inundated under future sea level rise projection scenarios. When at least 10 percent of tract land area is inundated with a given sea level, its residents are considered to be affected by sea level rise. For the purpose of this analysis, SLR scenarios were assumed not to reflect periodic inundation due to extreme weather events, which may lead to an even greater share of residents affected on a less frequent basis. Prior to the impacts from sea level rise, neighborhoods will experience temporary flooding from extreme weather events which can create significant damage to homes and neighborhoods. It should be noted that by directly reviewing maps and tools through the ART (Adapting to Rising Tides) program, regular inundation sea level rise and temporary flooding from extreme weather events are both available. More information on this approach is available here: http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/ Sea level rise analysis for metro areas reflects local, as opposed to global, sea level rise. Recent data has shown sea level is rising faster in the southeast region of the United States. Regional differences in the rate of sea level rise. More information and data related to the rate of sea level rise for different coastal regions is available here: https://oceanservice.noaa.gov/facts/sealevel-global-local.html
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Vital Signs: Poverty - by city
data.bayareametro.gov | Last Updated 2019-10-25T20:36:08.000ZVITAL SIGNS INDICATOR Poverty (EQ5) FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit LAST UPDATED December 2018 DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels. DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000) U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented. For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract. To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.
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Vital Signs: Life Expectancy – by ZIP Code
data.bayareametro.gov | Last Updated 2018-07-06T18:05:06.000ZVITAL SIGNS INDICATOR Life Expectancy (EQ6) FULL MEASURE NAME Life Expectancy LAST UPDATED April 2017 DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time. DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population. Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly im
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Vital Signs: Poverty - Bay Area
data.bayareametro.gov | Last Updated 2019-10-25T20:36:36.000ZVITAL SIGNS INDICATOR Poverty (EQ5) FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit LAST UPDATED December 2018 DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels. DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000) U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented. For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract. To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.
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Vital Signs: Life Expectancy – Bay Area
data.bayareametro.gov | Last Updated 2018-07-06T18:05:05.000ZVITAL SIGNS INDICATOR Life Expectancy (EQ6) FULL MEASURE NAME Life Expectancy LAST UPDATED April 2017 DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time. DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population. Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area. Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip
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Vital Signs: Jobs – by subcounty
data.bayareametro.gov | Last Updated 2020-04-13T23:19:44.000ZVITAL SIGNS INDICATOR Jobs (LU2) FULL MEASURE NAME Employment estimates by place of work LAST UPDATED March 2020 DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees. DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/ U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/ U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/ Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/ METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed. For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017. The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma
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Vital Signs: Greenfield Development – by metro area
data.bayareametro.gov | Last Updated 2020-07-03T16:36:42.000ZVITAL SIGNS INDICATOR Greenfield Development (LU5) FULL MEASURE NAME The acres of construction on previously undeveloped land LAST UPDATED November 2019 DESCRIPTION Greenfield development refers to construction on previously undeveloped land and the corresponding expansion of our region’s developed footprint, which includes the extent of urban and built-up lands. The footprint is defined as land occupied by structures, with a building density of at least 1 unit to 1.5 acres. DATA SOURCE Department of Conservation: Farmland Mapping and Monitoring Program GIS Data Tables/Layers (1990-2016) https://www.conservation.ca.gov/dlrp/fmmp U.S. Census Bureau: Decennial Census Population by Census Block Group (2000-2010) http://factfinder.census.gov U.S. Census Bureau: American Community Survey (5-year) Population by Census Block Group (2000-2017) http://factfinder.census.gov METHODOLOGY NOTES (across all datasets for this indicator) For regional and local data, FMMP maps the extent of “urban and built-up” lands, which generally reflect the developed urban footprint of the region. The footprint is defined as land occupied by structures with building density of at least 1 unit to 1.5 acres. Uses include residential, industrial, commercial, construction, institutional, public administration, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes. To determine the amount of greenfield development (in acres) occurring in a given two-year period, the differences in urban footprint are computed on a county-level. FMMP makes slight refinements to urban boundaries over time, so changes in urban footprint +/- 100 acres are not regionally significant. The GIS shapefile represents the 2016 urban footprint and thus does not show previously urbanized land outside of the footprint (i.e. Hamilton Air Force Base). For metro comparisons, a different methodology had to be used to avoid the geospatial limitations associated with FMMP. U.S. Census population by census block group was gathered for each metro area for 2000, 2010, and 2017. Population data for years 2000 and 2010 come from the Decennial Census while data for 2018 comes from the 2017 5-year American Community Survey. The block group was considered urbanized if its average/gross density was greater than 1 housing unit per acre (a slightly higher threshold than FMMP uses for its definition). Because a block group cannot be flagged as partially urbanized, and non-residential uses are not fully captured, the urban footprint of the region calculated with this methodology is smaller than in FMMP. The metro data should be primarily used for looking at comparative growth rate in greenfield development rather than the acreage totals themselves.
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Vital Signs: Jobs – Bay Area
data.bayareametro.gov | Last Updated 2020-04-13T23:21:14.000ZVITAL SIGNS INDICATOR Jobs (LU2) FULL MEASURE NAME Employment estimates by place of work LAST UPDATED October 2019 DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees. DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/ U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/ U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/ Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/ METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed. For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017. The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma
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Vital Signs: Daily Miles Traveled - by metro area (per-capita)
data.bayareametro.gov | Last Updated 2018-07-06T18:04:45.000ZVITAL SIGNS INDICATOR Daily Miles Traveled (T15) FULL MEASURE NAME Per-capita vehicle miles traveled LAST UPDATED July 2017 DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled. DATA SOURCE Federal Highway Administration: Highway Statistics Series 2015 Table HM-71; limited to urbanized areas https://www.fhwa.dot.gov/policyinformation/statistics.cfm U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) "Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common. The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years."
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Vital Signs: Daily Miles Traveled - Bay Area (per-capita)
data.bayareametro.gov | Last Updated 2018-07-06T18:04:21.000ZVITAL SIGNS INDICATOR Daily Miles Traveled (T15) FULL MEASURE NAME Per-capita vehicle miles traveled LAST UPDATED July 2017 DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled. DATA SOURCE California Department of Transportation: California Public Road Data/Highway Performance Monitoring System 2001-2015 http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php California Department of Finance: Population and Housing Estimates Forms E-8 and E-5 2001-2015 http://www.dof.ca.gov/research/demographic/reports/estimates/e-8/ http://www.dof.ca.gov/research/demographic/reports/estimates/e-5/2011-20/view.php U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common. The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years.