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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: Housing Affordability - County Overall
data.bayareametro.gov | Last Updated 2019-10-25T20:43:28.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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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: 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 – 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: Housing Affordability - County by Income
data.bayareametro.gov | Last Updated 2019-10-25T20:43:10.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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Vital Signs: Jobs – by county
data.bayareametro.gov | Last Updated 2020-04-13T23:20:49.000ZVITAL 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: Jobs – by metro
data.bayareametro.gov | Last Updated 2020-04-13T23:20:28.000ZVITAL 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: Greenhouse Gas Emissions - by county
data.bayareametro.gov | Last Updated 2018-07-10T01:36:51.000ZVITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3) FULL MEASURE NAME Greenhouse gas emissions from primary sources LAST UPDATED August 2017 DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels. DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11 California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/ California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overe
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Vital Signs: Life Expectancy – by county
data.bayareametro.gov | Last Updated 2018-07-06T18:05:04.000ZVITAL 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