Open Data Network

Projections 2040 by Jurisdiction

Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for jurisdictions in the nine county San Francisco Bay Area region.

Vital Signs: Life Expectancy – by ZIP Code

VITAL 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

Basis Master Data Management

This list identifies data that has been collected for the Bay Area Spatial Information System platform. These datasets support MTC's Analytical Services and Long range Planning efforts and drive decision making and policy development across the agency. The data is managed by the Data & Visualization Team, with assistance from key staff that have been identified as Data Stewards in various units across the agency. Data is stored and managed within MTC's Enterprise Data Lake, and disseminated through the Socrata Connected Government Cloud as a Service (DaaS).

Plan Bay Area 2050 Transportation Project List

Final Transportation Project List for Plan Bay Area 2050, the long-range plan for the nine-county San Francisco Bay Area. For more information, see planbayarea.org

Vital Signs: Commute Patterns - Bay Area

VITAL SIGNS INDICATOR Commute Patterns (T5) FULL MEASURE NAME Commute flows between Bay Area counties LAST UPDATED April 2020 DESCRIPTION Commute patterns, more commonly referred to as county-to-county commute flows, reflect the number of individuals traveling within and between various counties for commuting purposes. DATA SOURCE U.S. Census Transportation Planning Package Table A302103 5-Year Average (2012-2016) https://ctpp.transportation.org/2012-2016-5-year-ctpp/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The Census Transportation Planning Package is produced only every five years and relies upon 5-year rolling average data for all data tables. In order to analyze trends related to the Bay Area, commute patterns were evaluated for all interactions between the nine Bay Area counties and for all interactions between other California counties and any Bay Area county. Commute flows between non-California counties and the San Francisco Bay Area were assumed to be negligible.

Vital Signs: Daily Miles Traveled - by county (per-capita)

VITAL SIGNS INDICATOR Daily Miles Traveled (T15) FULL MEASURE NAME Per-capita vehicle miles traveled LAST UPDATED July 2017 DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled. DATA SOURCE California Department of Transportation: California Public Road Data/Highway Performance Monitoring System 2001-2015 http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php California Department of Finance: Population and Housing Estimates Forms E-8 and E-5 2001-2015 http://www.dof.ca.gov/research/demographic/reports/estimates/e-8/ http://www.dof.ca.gov/research/demographic/reports/estimates/e-5/2011-20/view.php U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common. The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years.

CEQA Roadway Screening Tool - Cancer Risk

This dataset (raster file (.tif), Cancer Risk, per million) provides the basis for the CEQA Roadway Screening Tool and is designed to assist project sponsors in conducting cumulative cancer risk and hazard analyses as part of their California Environmental Quality Act (CEQA) environmental review for proposed land use projects. Please see attached README for additional details and disclaimer. Please review this document in detail before using the dataset.

BASIS Datasets Tracker

General Plan and Zoning 2018

Previous Name: BASE PARCEL 2018

Vital Signs: Street Pavement Condition - by segment (Updated October 2019)

VITAL SIGNS INDICATOR Street Pavement Condition (T16) FULL MEASURE NAME Pavement condition index (PCI) LAST UPDATED October 2019 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 vitalsigns.info@bayareametro.gov 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. Pavement condition data was not collected in 2008. As such, 2007 and 2009 PCI scores were averaged to compute a 2008 estimate for the region, county and city datasets. Due to a lack of reported PCI data for the city of Palo Alto in 2007, an average could not be calculated, so Palo Alto is not included in the Regional Distribution chart.