Population weighted centroids are a common tool for spatial analysis, particularly when more granular data is unavailable or researchers lack sophisticated geocoding tools. The ZIP Code Population Weighted Centroids allows researchers and analysts to estimate the center of population in a given geography rather than the geometric center. Data to estimate ZIP code centroids is extracted from administrative USPS address data. The population weighted centroids are based on the number of residential addresses in the component ZIP+4 (also sometimes referred to as 'ZIP9') locations for each ZIP code. The data is based on ZIP+4 centroids, not ZIP Code Tabulation Areas (ZCTAs).To learn more about administrative USPS address data, please visit: https://www.huduser.gov/portal/datasets/usps.htmlData Dictionary: DD_ZIP Code Population Weighted Centroids, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 12/2023

Comprehensive, annually-updated population datasets at ZIP code and administrative levels for 247 countries, spanning from 1975 to 2030, including historical, current, and projected population figures, enriched with attributes like area size, multilingual support, UNLOCODEs, IATA codes, and time zones.

This data comes from the 2010 Census Profile of General Population and Housing Characteristics. Zip codes are limited to those that fall at least partially within LA city boundaries. The dataset will be updated after the next census in 2020. To view all possible columns and access the data directly, visit http://factfinder.census.gov/faces/affhelp/jsf/pages/metadata.xhtml?lang=en&type=table&id=table.en.DEC_10_SF1_SF1DP1#main_content.

A dataset listing Virginia zip codes by population for 2024.

A dataset listing Georgia zip codes by population for 2024.

Dataset

This dataset was created by Shaswot Joshi

Released under Database: Open Database, Contents: Database Contents

Contents

Population by Age, Sex and Ethnicity by U.S. Postal ZIP Code from the 2020 Decennial Census

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.

A dataset listing South Carolina zip codes by population for 2024.

This data package has the purpose to offer data for demographic indicators, part of 5-years American Community Census, that could be needed in the analysis made along with health-related data or as stand-alone. The American Community Survey based on 5-years estimates is, according to U.S Census Bureau, the most reliable, because the samples used are the largest and the data collected cover all country areas, regardless of the population number.

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.

Population by U.S. Postal ZIP Code from the 2020 Decennial 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.

List of United States Postal Service (USPS) Zone Improvement Plan (ZIP) Codes found within or partially within the borders of the City of Detroit.

A dataset listing South Dakota zip codes by population for 2024.

Population by U.S. Postal ZIP Code from the Series 14 Regional Growth Forecast

A dataset listing Oklahoma zip codes by population for 2024.

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.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.