- API data.bayareametro.gov | Last Updated 2018-07-06T18:04:14.000Z
VITAL 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 email@example.com 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 data.bayareametro.gov | Last Updated 2018-07-06T18:06:30.000Z
VITAL SIGNS INDICATOR List Rents (EC9) FULL MEASURE NAME List Rents LAST UPDATED October 2016 DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region. DATA SOURCE real Answers (1994 – 2015) no link Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/ CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section. Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries. Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville. Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.
- API data.bayareametro.gov | Last Updated 2018-07-06T18:04:02.000Z
VITAL SIGNS This dataset is used for the Targets page on the Vital Signs website at www.vitalsigns.mtc.ca.gov/targets. CONTACT INFORMATION email@example.com
- API data.bayareametro.gov | Last Updated 2018-08-15T17:02:20.000Z
VITAL SIGNS INDICATOR Housing Production (LU4) FULL MEASURE NAME Produced housing units by unit type LAST UPDATED July 2018 DESCRIPTION Housing production is measured in terms of the change in the number of housing units in local jurisdictions in a given year. The total number of units produced captures housing units added by new construction and annexations and subtracts demolitions, adjusting for units lost or gained by conversions or natural disasters. DATA SOURCES California Department of Finance Population and Housing Estimates Form E-8 1990-2010 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ California Department of Finance Population and Housing Estimates Form E-5 2011-2017 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) Single-family housing units include single detached units and single attached units. Multi-family housing units include buildings with two or more units and apartment units. Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family, multi-family units, and mobile home units. City data is only available for incorporated cities and towns. All units produced in unincorporated cities and towns are included under their respective county’s unincorporated total. Housing production data is not available for years when the city or town was not incorporated. Housing production data for metropolitan areas for each year is the difference of annual housing unit estimates from the Census Bureau’s Population Estimates Program. Housing production data for the region, counties, and cities for each year is the difference of annual housing unit estimates from the California Department of Finance. In the years new cities were incorporated, units that were annexed were removed from the unincorporated county total and are not counted as “produced” units for the new city. Housing units produced reflect the net change in the number of units present in each respective year.
- API data.bayareametro.gov | Last Updated 2018-07-06T18:06:55.000Z
VITAL SIGNS INDICATOR Population (LU1) FULL MEASURE NAME Population estimates LAST UPDATED September 2016 DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region. DATA SOURCES U.S. Census Bureau 1960-1990 Decennial Census http://factfinder2.census.gov California Department of Finance 1961-2016 Population and Housing Estimates http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, tract) are as of January 1, 2010, released beginning November 30, 2010 by the U.S. Census Bureau. A priority development area (PDA) is a locally-designated infill area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are as current as July 2016. Population estimates for PDAs were derived from Census population counts at the block group level for 2000-2014 and at the tract level for 1970-1990. Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average). Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average. Estimates of density for tracts and PDAs use gross acres as the denominator. Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark. The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside InlandCoastalDelta: American Canyon, Benicia, Clayton, Concord, Cotati, Danville, Dublin, Lafayette, Martinez, Moraga, Napa, Novato, Orinda, Petaluma, Pleasant Hill, Pleasanton, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Walnut Creek, Antioch, Brentwood, Calistoga, Cloverdale, Dixon, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Livermore, Morgan Hill, Oakley, Pittsburg, Rio Vista, Sonoma, St. Helena, Suisun City, Vacaville, Windsor, Yountville Unincorporated: all unincorporated towns
- API data.bayareametro.gov | Last Updated 2018-10-24T00:30:32.000Z
VITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
- API data.bayareametro.gov | Last Updated 2019-07-09T19:13:38.000Z
This dataset is used for the Targets page on the Vital Signs website at www.vitalsigns.mtc.ca.gov/targets. CONTACT INFORMATION email@example.com
- API data.bayareametro.gov | Last Updated 2018-07-10T01:44:17.000Z
VITAL SIGNS INDICATOR Jobs (LU2) FULL MEASURE NAME Employment estimates by place of work LAST UPDATED June 2016 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 Bureau of Labor Statistics: Current Employment Statistics 1990-2015 http://www.bls.gov/data/ Note: Nonfarm employment only CONTACT INFORMATION firstname.lastname@example.org 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 southern San Mateo County. The number of sub-county collections of cities varies from one (San Francisco, Marin, and Napa counties) to four (Santa Clara County). Estimates for sub-county area are the sums of city-level estimates from Longitudinal Employer-Household Dynamics (LEHD) data. To correct for limitations in LEHD estimates, the 2014 estimates were increased to 2015 values preserving the spatial distribution of jobs calculated from LEHD. County employment data from EDD was used to adjust the LEHD estimates. 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
- API data.bayareametro.gov | Last Updated 2018-07-06T18:05:48.000Z
VITAL SIGNS INDICATOR Economic Output (EC14) FULL MEASURE NAME Per-capita gross regional product LAST UPDATED December 2016 DESCRIPTION Economic output is measured by the total and per-capita gross regional product and refers to the value of goods and services generated by workers and companies in a region. DATA SOURCE Bureau of Economic Analysis: Regional Economic Accounts 2001-2015 http://www.bea.gov/regional/ California Department of Finance: Population and Housing Estimates 2001-2009 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ Note: Table E-8 California Department of Finance: Population and Housing Estimates 2010-2015 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ Note: Table E-5 CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) As gross regional product data is only available on the MSA level, Bay Area data includes 10 counties (the nine core counties + San Benito County); this results in a slightly higher regional GRP as a result of additional population and business activity. Per-capita data reflects the additional population included as a result of San Benito County’s participation in the San Jose MSA. Data is inflation-adjusted by using both nominal and real data developed by BEA and appropriately escalating real GRP data in 2009 dollars to today’s dollars (2015). This inflation adjustment approach is specific to each MSA and is different from the CPI inflation approach used for other datasets on the Vital Signs website.
- API data.bayareametro.gov | Last Updated 2019-07-30T01:22:26.000Z
VITAL SIGNS INDICATOR Home Prices (EC7) FULL MEASURE NAME Home Prices LAST UPDATED October 2016 DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city. DATA SOURCE Zillow Median Sale Price (1997-2016) http://www.zillow.com/research/data/ Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2016; specific to each metro area) http://data.bls.gov CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves. For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/ Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.