This dataset is part of the Geographical repository maintained by Opendatasoft.This dataset contains data for zip codes 5 digits in United States of America.ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery.Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.Add administrative hierarchy.

Abstract

A crosswalk dataset matching US ZIP codes to corresponding census tracts

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

Census tract definition

A census tract, census area, census district or meshblock is a geographic region defined for the purpose of taking a census. Sometimes these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county. In unincorporated areas of the United States these are often arbitrary, except for coinciding with political lines.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact information

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

GeoJunxion‘s ZIP+4 is a complete dataset based on US postal data consisting of plus 35 millions of polygons​. The dataset is NOT JUST a table of spot data, which can be downloaded as csv or other text file as it happens with other suppliers​. The data can be delivered as shapefile through a single RAW data delivery or through an API​.

The January 2021 USPS data source has significantly changed since the previous delivery. Some States have sizably lower ZIP+4 totals across all counties when compared with previous deliveries due to USPS parcelpoint cleanup, while other States have a significant increase in ZIP+4 totals across all counties due to cleanup and other rezoning. California and North Carolina in particular have several new ZIP5s, contributing to the increase in distinct ZIPs and ZIP+4s​.

GeoJunxion‘s ZIP+4 data can be used as an additional layer on an existing map to run customer or other analysis, e.g. who is my customer who not, what is the density of my customer base in a certain ZIP+4 etc.

Information can be put into visual context, due to the polygons, which is good for complex overviews or management decisions. ​CRM data can be enriched with the ZIP+4 to have more detailed customer information​.

Key specifications:

Topologized ZIP polygons​

GeoJunxion ZIP+4 polygons follow USPS postal codes ​

ZIP+4 code polygons: ​

ZIP5 attributes ​

State codes. ​

Overlapping ZIP+4 ​

boundaries for multiple ZIP+4 addresses on one area​

Updated USPS source (January 2021) ​

Distinct ZIP5 codes: 34 731​

Distinct ZIP+4 codes: 35 146 957 ​

The ZIP + 4 polygons are delivered in Esri shapefile format. This format allows the storage of geometry and attribute information for each of the features. ​

The four components for the shapefile data are: ​

.shp – This file stores the geometry of the feature​

.shx –This file stores an index that stores the feature geometry​

.dbf –This file stores attribute information relating to individual features​

.prj –This file stores projection information associated with features​

​Current release version 2021. Earlier versions from previous years available on request.

​

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

These datasets are associated with the manuscript "Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas." The datasets include (1) ZIP code-level daily average temperature for 2000-2017, (2) ZIP code-level daily counts of Medicare hospitalizations for cardiovascular disease for 2000-2017, and (3) ZIP code-level population-weighted urban heat island intensity (UHII). There are 9,917 ZIP codes included in the datasets, which are located in the urban cores of 120 metropolitan statistical areas across the contiguous United States. (1) The ZIP code-level daily temperature data is publicly available at: https://doi.org/10.15139/S3/ZL4UF9. A data dictionary is also available at this link. (2) The ZIP code-level daily counts of Medicare hospitalizations cannot be uploaded to ScienceHub because of privacy requirements in the data use agreement with Medicare. (3) The ZIP code-level UHII data is attached, along with a data dictionary describing the dataset. Portions of this dataset are inaccessible because: The ZIP code-level daily counts of Medicare cardiovascular disease hospitalizations cannot be uploaded to ScienceHub due to privacy requirements in data use agreements with Medicare. They can be accessed through the following means: The Medicare data can only be accessed internally at EPA with the correct permissions. Format: The Medicare data includes counts of the number of cardiovascular disease hospitalizations in each ZIP code on each day between 2000-2017. This dataset is associated with the following publication: Cleland, S., W. Steinhardt, L. Neas, J. West, and A. Rappold. Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 178(108005): 1, (2023).

This Zipcode GIS Layer is a spatial dataset that outlines the boundaries of ZIP code areas across York County, Pennsylvania. This layer is used in Geographic Information Systems (GIS) to support mapping, analysis, and decision-making based on location. Each ZIP code area is represented as a shape on the map and includes basic information such as the ZIP code, city, and state. This data is useful for a wide range of applications including business planning, public services, marketing, transportation, and emergency response. The Zipcode GIS Layer allows users to visualize and analyze geographic patterns, such as population distribution, service coverage, and regional trends. It can be used on its own or combined with other spatial data for more detailed studies.

A listing of NYS counties with accompanying Federal Information Processing System (FIPS) and US Postal Service ZIP codes sourced from the NYS GIS Clearinghouse.

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

Census data plays a pivotal role in academic data research, particularly when exploring relationships between different demographic characteristics. The significance of this particular dataset lies in its ability to facilitate the merging of various datasets with basic census information, thereby streamlining the research process and eliminating the need for separate API calls.

The American Community Survey is an ongoing survey conducted by the U.S. Census Bureau, which provides detailed social, economic, and demographic data about the United States population. The ACS collects data continuously throughout the decade, gathering information from a sample of households across the country, covering a wide range of topics

Methodology

The Census Data Application Programming Interface (API) is an API that gives the public access to raw statistical data from various Census Bureau data programs.

We used this API to collect various demographic and socioeconomic variables from both the ACS and the Deccenial survey on different geographical levels:

ZCTAs:

ZIP Code Tabulation Areas (ZCTAs) are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

Census Tract:

Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity that can be updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program (PSAP).

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census.

Block Groups:

Block groups (BGs) are the next level above census blocks in the geographic hierarchy (see Figure 2-1 in Chapter 2). A BG is a combination of census blocks that is a subdivision of a census tract or block numbering area (BNA). (A county or its statistically equivalent entity contains either census tracts or BNAs; it can not contain both.) A BG consists of all census blocks whose numbers begin with the same digit in a given census tract or BNA; for example, BG 3 includes all census blocks numbered in the 300s. The BG is the smallest geographic entity for which the decennial census tabulates and publishes sample data.

Census Blocks:

Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps.

Abstract

A crosswalk table from US postal ZIP codes to geo-points (latitude, longitude)

Documentation

Data source: public.opendatasoft.

The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not).

This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not). This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".

The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.

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.

Prior research has demonstrated that access to parks and greenspace can have a positive impact on many aspects of and contributors to health, including physical activity levels (Kaczynski et al., 2007), healthy aging (Finlay, 2015), and sense of well-being (Larson et al., 2016). Neighborhood parks can also contribute to sense of community (Gómez, 2015). These datasets describe the number and area of parks in each census tract or each ZIP code tabulation area (ZCTA) in the United States. Measures include the total number of parks, park area, and proportion of park area within each census tract or ZCTA.

This web map contains ZIP Code points, ZIP Code boundaries, and 2020 U.S. Census Tract boundaries for the United States, including all 50 states and the District of Columbia. The data is maintained by Esri Demographics (Esri DM) and sourced from ArcGIS Data and Maps, providing a comprehensive view of geographic and demographic distributions across the country. Data Layers Included:

Census Tracts (2020) – Defined polygon boundaries containing 2020 Census population data, Census codes, and Esri Updated Demographics. These tracts serve as small, stable geographic areas used for demographic analysis. ZIP Code Points – Point-based locations representing U.S. ZIP Codes, including postal names, ZIP Code types, population estimates, and area size. The points are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics. This layer is updated annually. ZIP Code Boundaries – Polygon representations of ZIP Code areas, useful for mapping service areas, jurisdictional boundaries, and demographic studies. This layer is updated annually. The boundaries are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics.

Data Source and Usage Rights: The data used in this hosted feature layer is provided via ArcGIS Data and Maps. The datasets within ArcGIS Data and Maps are subject to varying redistribution rights granted by Esri’s third-party data suppliers. Users must consult the Redistribution Rights document to determine permitted uses, applicable disclaimers, attribution requirements, and other conditions of use. This dataset may be used and redistributed with proper metadata and attribution in accordance with the Esri Master License Agreement. A downloadable layer package for 2020 Census Tracts is available here: 🔗 2020 Census Tract Layer Package Public Information Access: The Texas Public Information Act grants the public the right to access government records, except where certain exceptions apply. The public information officer may not ask why the records are requested. 🔗 Request public records online: Austin Public Information Requests This work is licensed under the Esri Master License Agreement.

Abstract

A crosswalk matching US ZIP codes to corresponding CBSA (core-based statistical area)

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

CBSA definition

A core-based statistical area (CBSA) is a U.S. geographic area defined by the Office of Management and Budget (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. Areas defined on the basis of these standards applied to Census 2000 data were announced by OMB in June 2003. These standards are used to replace the definitions of metropolitan areas that were defined in 1990. The OMB released new standards based on the 2010 Census on July 15, 2015.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact authors

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

This data set contains all zip codes within Delaware County, Ohio. In 2003, Delaware County zip codes were carefully evaluated and cleaned-up based on cross referencing between the Census Bureau's zip code file from the 2000 census, the United States Postal Service website, and tax mailing addresses from the treasurer's office. The zip code layer was then created in 2005 by dissolving all Delaware County parcels by their property addresses. Tax exempt parcels and dedicated roads with no zip codes were manually populated based on their location within the zip code layer. This layer was also used to populate the zip_right and zip_left attributes for Delaware County's road centerline.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

This dataset contains measures of traffic volume per census tract and ZIP code tabulation area (ZCTA) in the United States from 1963 to 2019 (primarily 1997 to 2019). High traffic volume may be used as a proxy for heavy traffic, high traffic speeds, and impediments to walking or biking. The dataset contains measures of the average, maximum, and minimum traffic volume per year or per ZCTA per year. These figures are available for all streets, highways, and non-highways. In the ZCTA dataset, data is collected intermittently across locations over time, therefore traffic volume has been interpolated for years in which no measures are available. Data Source: Traffic volume measurements are derived from Kalibrate's TrafficMetrix database accessed via Esri Demographics. Census tract boundaries come from the 2010 TIGER/Line shapefiles. ZCTA boundaries come from the 2019 TIGER/Line shapefiles.

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.