- API
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
- API
Vital Signs: Greenhouse Gas Emissions - Bay Area
data.bayareametro.gov | Last Updated 2018-07-10T01:38:19.000ZVITAL 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 ov
- API
ART Bay Area Inundation Scenario - 36" Sea Level Rise
data.bayareametro.gov | Last Updated 2023-06-09T00:07:18.000ZInundation feature set representing areas vulnerable to a 36 inch rise in sea level for the San Francisco Bay Region. This is a derivative feature set, assembled by the Metropolitan Transportation Commission (MTC), created by merging county-specific, land-only inundation feature sets. The source, county-level feature sets were produced for Adapting to Rising Tides (ART), a program led by the San Francisco Bay Conservation and Development Commission (BCDC), in September 2017. The sea level rise (SLR) scenario used to produce this data represents 36 inches (three feet) of water level above the current mean higher high water (MHHW) tidal datum. This is considered the most likely level of sea level rise expected by 2100; or an existing 50-year extreme tide. The polygons contain the extent and depth of land-only inundation (in feet) flooding of the bayside shoreline. Depth of flooding were created by subtracting a land surface Digital Elevation Model (DEM) from the water surface DEM representing the SLR scenario (MHHW + SLR). Extent of flooding were created by employing a two rule assessment to determine if an area is inundated. It must be below the assigned water surface DEM elevation value, and it must be connected to an adjacent area that was either flooded or open water. This method applies an "eight-side rule" for connectedness, where the area is considered "connected" if any of its cardinal or diagonal directions is connected to a flooded area or open water. Hydraulic connectivity assessment removes areas from the inundation zone if they are protected by levees or other topographic features that prevent inland inundation. This assessment also removed areas that are low lying but inland and not directly connected to an adjacent inundated area. The 36 inch SLR scenario can be used to approximate all extreme tide/sea level rise combinations that produce a water level in the range of MHHW + 33 inches to MHHW + 39 inches, including: - 36 inches of SLR; - 1-year extreme tide event coupled with 24 inches of SLR; - 2-year extreme tide event coupled with 18 inches of SLR; - 5-year extreme tide event coupled with 12 inches of SLR; - 25-year extreme tide event coupled with 6 inches of SLR, and - 50-year extreme tide event under existing conditions (no SLR). Publication Date: June 2019 Creation Date: March 2019 Status: Progress: Complete Maintenance and Update Frequency: None planned Contact Information: Contact Organization: Metropolitan Transportation Commission Contact Person: Data & Visualization Contact Address: Address Type: mailing and physical Address: 375 Beale Street, Suite 800 City: San Francisco State or Province: California Postal Code: 94105 Country: United States of America Contact Voice Telephone: (415) 778-6700 Contact Electronic Mail Address: dataviz@bayareametro.gov Hours: 9:00 AM - 5:00 PM (PST) Monday through Friday
- API
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
- API
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
- API
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
- API
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
- API
Vital Signs: Vulnerability To Sea Level Rise - Tracts Shapefile
data.bayareametro.gov | Last Updated 2019-09-13T20:27:59.000ZVITAL 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@mtc.ca.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
- API
Vital Signs: Vulnerability to Sea Level Rise - Bay Area
data.bayareametro.gov | Last Updated 2018-07-06T18:04:27.000ZVITAL 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 Sea Level Rise Maps http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/ U.S. Census Bureau 1990-2010 Decennial Census http://factfinder2.census.gov U.S. Census Bureau 2015 American Community Survey http://factfinder2.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.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://oc
- API
Vital Signs Targets - Measure Performance (updated December 2020)
data.bayareametro.gov | Last Updated 2020-12-14T23:47:00.000ZThis dataset is used for the Targets page on the Vital Signs website at www.vitalsigns.mtc.ca.gov/targets. CONTACT INFORMATION vitalsigns.info@bayareametro.gov