- 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 firstname.lastname@example.org
- API data.bayareametro.gov | Last Updated 2018-07-06T18:04:50.000Z
VITAL SIGNS INDICATOR Street Pavement Condition (T16) FULL MEASURE NAME Pavement condition index (PCI) LAST UPDATED May 2017 DESCRIPTION Street pavement condition, more commonly referred to as the pavement condition index (PCI), reflects the quality of pavement on local streets and roads in the region. Calculated using a three-year moving average, PCI ranges from zero (failed) to 100 (brand-new) and has been used as a regional indicator of pavement preservation for over a decade. DATA SOURCE Metropolitan Transportation Commission: StreetSaver CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) Pavement condition index (PCI) relies upon a three-year moving average for regional, county, and city PCI to improve the reliability of the PCI data on an annual basis. The index ranges from 0 to 100, with 0 representing a failed road and 100 representing a brand-new facility. Segment PCI data is collected on a rolling basis but is imputed for interim years based on facility age and treatments using the MTC StreetSaver system. Due to the lack of reported PCI data in 2006, the city of Palo Alto is not included in the Regional Distribution chart.
- API data.bayareametro.gov | Last Updated 2019-08-13T16:17:18.000Z
VITAL SIGNS INDICATOR Income (EC5) FULL MEASURE NAME Worker income by workplace (earnings) LAST UPDATED May 2019 DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis. DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org U.S. Census Bureau: American Community Survey Form B08521 (2006-2017; place of employment) http://api.census.gov Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov CONTACT INFORMATION Vitalsigns.firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income. Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.
- 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 email@example.com 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 2018-08-21T00:44:35.000Z
VITAL SIGNS INDICATOR Street Pavement Condition (T16) FULL MEASURE NAME Pavement condition index (PCI) LAST UPDATED August 2018 DESCRIPTION Street pavement condition, more commonly referred to as the pavement condition index (PCI), reflects the quality of pavement on local streets and roads in the region. Calculated using a three-year moving average, PCI ranges from zero (failed) to 100 (brand-new) and has been used as a regional indicator of pavement preservation for over a decade. DATA SOURCE Metropolitan Transportation Commission: StreetSaver CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) Pavement condition index (PCI) relies upon a three-year moving average for regional, county, and city PCI to improve the reliability of the PCI data on an annual basis. The index ranges from 0 to 100, with 0 representing a failed road and 100 representing a brand-new facility. Segment PCI data is collected on a rolling basis but is imputed for interim years based on facility age and treatments using the MTC StreetSaver system. Due to the lack of reported PCI data in 2007, the city of Palo Alto is not included in the Regional Distribution chart.
- API data.bayareametro.gov | Last Updated 2018-07-06T18:04:12.000Z
VITAL SIGNS INDICATOR Injuries From Crashes (EN4-6) FULL MEASURE NAME Fatalities from crashes (traffic collisions) LAST UPDATED October 2017 DESCRIPTION Fatalities from crashes refers to deaths as a result of injuries sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of injuries sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data. DATA SOURCE National Highway Safety Administration: Fatality Analysis Reporting System CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) The data is reported by the National Highway Safety Administration's Fatalities Analysis Reporting System. 2016 data comes from the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS), which was accessed via SafeTREC’s Transportation Injury Mapping System (TIMS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision, and location/jurisdiction of collision (for more: http://tims.berkeley.edu/help/files/switrs_codebook.doc). Fatalities were normalized over historic population data from the US Census and California Department of Finance and vehicle miles traveled (VMT) data from the Federal Highway Administration. For more regarding reporting procedures and injury classification see the California Highway Patrol Manual (http://www.nhtsa.gov/nhtsa/stateCatalog/states/ca/docs/CA_CHP555_Manual_2_2003_ch1-13.pdf).
- 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 2018-07-06T18:04:14.000Z
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 2018-07-06T18:03:19.000Z
VITAL SIGNS INDICATOR Commute Time (T3) FULL MEASURE NAME Commute time by residential location LAST UPDATED January 2018 DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence. DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm U.S. Census Bureau: American Community Survey Form B08013 (2006-2016; place of residence; overall time) Form C08136 (2006-2016; place of residence; time by mode) Form B08301 (2006-2016; place of residence) www.api.census.gov CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis. For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies. Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.
- 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 email@example.com 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