This dataset identifies demographic and housing estimates including sex and age, race and housing units by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2011 through 2015. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.

The dataset here provides the total number of Qualified Health Plan selections by ZIP Code for 37 states for the second Health Insurance Marketplace open enrollment period (November 15, 2014 – February 15, 2015, including additional special enrollment period activity reported through February 22, 2015).

US Census American Community Survey (ACS) 2015, 5-year estimates of the key demographic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2015 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Feature layer generated from running the Enrich layer solution. USA ZIP Codes (2015) were enriched

US Census American Community Survey (ACS) 2015, 5-year estimates of the key demographic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2015 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

.Table NameZIP Code Business Statistics: Total for Zip Code: 2015 .Release ScheduleThe data in this file were released on May 12, 2017..Key Table InformationBeginning with reference year 2007, ZBP data are released using the Noise disclosure methodology to protect confidentiality. See Survey Methodologyfor complete information on the coverage and methodology of the ZIP Code Business Patterns data series. .UniverseThe universe of this file is all operating establishments with one or more paid employees. This universe includes most establishments classified in the North American Industry Classification System (NAICS) Codes 11 through 813990. For specific exclusions and inclusions, see Industry Classification of Establishments. .Geography CoverageThe data are shown at the 5-digit ZIP Code level only. .Industry CoverageThe data are shown for NAICS code 00 (Total for all sectors) only. .Data Items and Other Identifying RecordsThis file contains data on the number of establishments, total employment, first quarter payroll and annual payroll. .Sort OrderData are presented in ascending ZIP Code sequence. .FTP DownloadDownload the entire table at http://www2.census.gov/econ2015/CB/sector00/CB1500CZ11.zip. .Contact InformationU.S. Census Bureau Economy-Wide Statistics Division Business Statistics Branch Tel: (301)763-2580 Email: ewdd.county.business.patterns@census.gov ..NOTE: Data based on the 2013 Zip Business Patterns. For information on confidentiality protection, nonsampling error, and definitions, see Survey Methodology..Source: U.S. Census Bureau, 2015 ZIP Code Business Patterns.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the 2010 Census and American Community Survey 5-year estimates for 2011-2015, to show total population by zip code in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

last_edited_date = Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2010; 2011-2015

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Credits

U.S. Census Bureau, Atlanta Regional Commission

Census data for zip codes (geospatial extent) from the American Community Survey, gathered from 2011 through 2015 from the Department of Local Affairs (DOLA).

[Metadata] - 2015 Zip Code Tabulation Areas (ZCTA) with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016.

The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.

This study contains measures of neighborhood-school gap for 2009-2010 and 2015-2016. Neighborhood-school gap (NS gap) refers to the discrepancy between the demographics of a public school and its surrounding community. For example, if 60 percent of a school's student body is Black, but 30 percent of the neighborhood population is Black, the school has a positive Black neighborhood-school gap. These datasets measure gaps in race and poverty between elementary school student populations and the census tracts and ZIP code tabulation areas (ZCTAs) that those elementary schools serve. Data is at the census tract and ZCTA level. Supplemental data containing component variables used to calculate NS gap at the school and block group level is also available.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data American Community Survey 5-year estimates for 2011-2015 to show population with less than full English proficiency, by zip code in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here. Attributes: ZIP = Zip code (text) ZIP_dbl = Zip code (numeric) Total_Population_2010 = Total Population, 2010 Census Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)- - - - - -Pop_5Years_andOlder = #, Population 5 years and Over SpeakEngl_lessThan_vWell = #, Speak English Less than "very well" Pct_SpeakEng_lessThan_vWell = %, Speak English Less than "very well"- - - - - -last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES project by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. It represents a first-of-its kind effort to release information uniformly on this large scale.

Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS.

This data only covers the health of adults (people 18 and over) in East Baton Rouge Parish. All estimates lie within a 95% confidence interval.

PurposeWe examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.MethodsA total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.ResultsQuality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.ConclusionsThe patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from using data from American Community Survey 5-year estimates for 2011-2015 to show school enrollment by level of school, by zip code in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

Pop_3yrsOlder_Enrolld_in_School = #, Population 3 years and over enrolled in school

Nursery_school_preschool = #, Enrolled in nursery school, preschool

Pct_Nursery_Preschool = %, Enrolled in nursery school, preschool

Kindergarten = #, Enrolled in kindergarten

Percent_Kindergarten = %, Enrolled in kindergarten

Elementary_school_grades_1_8 = #, Enrolled in elementary school (grades 1-8)

Percent_ElemSchool_grades_1_8 = %, Enrolled in elementary school (grades 1-8)

High_school_grades_9_12 = #, Enrolled in high school (grades 9-12)

Percent_HS_grades_9_12 = %, Enrolled in high school (grades 9-12)

College_or_graduate_school = #, Enrolled in college or graduate school

Percent_College_or_grad_school = %, Enrolled in college or graduate school

last_edited_date = Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from U.S. Census: County Business Patterns to show number and density of business establishments and payroll data, for 2005-2015, by zip code in the Atlanta region.

Attributes:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

Number_of_establishments_2015 = Number of establishments, 2015

Establishments_perSqMi_2015 = Establishments per Square Mile, 2015

Establishments_per1000_Pop_2015 = Establishments per 1,000 population, 2015 (Population is 2010)

Paid_employees_March_2015 = Paid employees for pay period including March 12 (number), 2015

First_quarter_payroll_000s_2015 = First-quarter payroll (000s), 2015

Annual_payroll_000s_2015 = Annual payroll (000s), 2015

Number_of_establishments_2013 = Number of establishments, 2013

Establishments_perSqMi_2013 = Establishments per Square Mile, 2013

Establishments_per1000_Pop_2013 = Establishments per 1,000 population, 2013 (Population is 2010)

Annual_payroll_000s_2013 = Annual payroll (000s), 2013

First_quarter_payroll_000s_2013 = First-quarter payroll (000s), 2013

Paid_employees_March_2013 = Paid employees for pay period including March 12 (number), 2013

Number_of_establishments_2010 = Number of establishments, 2010

Paid_employees_March_2010 = Paid employees for pay period including March 12 (number), 2010

First_quarter_payroll_000s_2010 = First-quarter payroll (000s), 2010

Annual_payroll_000s_2010 = Annual payroll (000s), 2010

Number_of_establishments_2005 = Number of establishments, 2005

Paid_employees_March_2005 = Paid employees for pay period including March 12 (number), 2005

First_quarter_payroll_000s_2005 = First-quarter payroll (000s), 2005

Annual_payroll_000s_2005 = Annual payroll (000s), 2005

Chng_establishments_2005_2010 = Change in the number of establishments between 2005-2010

Chng_establishments_2005_2013 = Change in the number of establishments between 2005-2013

Chng_establishments_2005_2015 = Change in the number of establishments between 2005-2015

Chng_estabmts_PerSqMi_2005_2010 = Change in the number of establishments, Per Sq Mile, between 2005-2010

Chng_estabmts_PerSqMi_2005_2013 = Change in the number of establishments, Per Sq Mile, between 2005-2013

Chng_estabmts_PerSqMi_2005_2015 = Change in the number of establishments, Per Sq Mile, between 2005-2015

Chng_establishments_2010_2015 = Change in the number of establishments between 2010-2015

Chng_estabmts_PerSqMi_2010_2015 = Change in the number of establishments, Per Sq Mile, between 2010-2015All_establishments_2015 = All establishments, 2015 Very_Small_Businesses_2015 = Very Small businesses (1-4) employees, 2015 Small_Businesses_2015 = Small Businesses (5-19 employees), 2015 Medium_Businesses_2015 = Medium-sized businesses (20-99 employees), 2015 Large_Businesses_2015 = Large Businesses (100+ employees), 2015 Pct_Very_Small_Businesses_2015 = %, Very Small businesses (1-4) employees, 2015 Pct_Small_Businesses_2015 = %, Small Businesses (5-19 employees), 2015 Pct_Medium_Businesses_2015 = %, Medium-sized businesses (20-99 employees), 2015 Pct_Large_Businesses_2015 = %, Large Businesses (100+ employees), 2015 All_establishments_2010 = All establishments, 2010 Very_Small_Businesses_2010 = Very Small businesses (1-4) employees, 2010 Small_Businesses_2010 = Small Businesses (5-19 employees), 2010 Medium_Businesses_2010 = Medium-sized businesses (20-99 employees), 2010 Large_Businesses_2010 = Large Businesses (100+ employees), 2010 Pct_Very_Small_Businesses_2010 = % Very Small businesses (1-4) employees, 2010 Pct_Small_Businesses_2010 = % Small Businesses (5-19 employees), 2010 Pct_Medium_Businesses_2010 = % Medium-sized businesses (20-99 employees), 2010 Pct_Large_Businesses_2010 = % Large Businesses (100+ employees), 2010 All_establishments_2005 = All establishments, 2005 Very_Small_Businesses_2005 = Very Small businesses (1-4) employees, 2005 Small_Businesses_2005 = Small Businesses (5-19 employees), 2005 Medium_Businesses_2005 = Medium-sized businesses (20-99 employees), 2005 Large_Businesses_2005 = Large Businesses (100+ employees), 2005 Pct_Very_Small_Businesses_2005 = %, Very Small businesses (1-4) employees, 2005 Pct_Small_Businesses_2005 = %, Small Businesses (5-19 employees), 2005 Pct_Medium_Businesses_2005 = %, Medium-sized businesses (20-99 employees), 2005

Pct_Large_Businesses_2005 = %, Large Businesses (100+ employees), 2005

last_edited_date = Last date the feature was edited by ARCSource: U.S. Census Bureau, Atlanta Regional Commission

Date: 2005-2015

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 36 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=2c3deb0c05a748b391ea8c9cf9903588

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 29 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

These datasets contain measures of internet access per United States census tract and ZIP code tabulation area (ZCTA) from the 2015-2019 American Community Survey five-year estimate. Key variables include the number and percent of households per tract or ZCTA with any type of internet subscription, with broadband internet, and with a computer or smartphone.

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 important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. 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. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.