A dataset listing Illinois zip codes by population for 2024.

A dataset listing New York zip codes by population for 2024.

A dataset listing Tennessee zip codes by population for 2024.

A dataset listing Washington zip codes by population for 2024.

Our zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

A dataset listing Connecticut 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.

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

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.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 73729 (Cleo Springs, OK). Interactive charts load automatically as you scroll for improved performance.

Effects of initial lockdown and stimulus payments on zip code spending.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 73747 (Isabella, OK). Interactive charts load automatically as you scroll for improved performance.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 73718 (Ames, OK). Interactive charts load automatically as you scroll for improved performance.

A dataset listing Missouri zip codes by population for 2024.

Effects of initial lockdown and stimulus payments on percentage change across geographic regions.

Effects of initial lockdown and stimulus payments on per card spending.

Effects of initial lockdown and stimulus payments on dollar change across geographic regions.

A dataset listing Florida cities by population for 2024.

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as epidemiology, public health, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, and U.S. Census Bureau American Community Survey (ACS). The data are particularly useful for studies on rural populations who may lack air monitoring sites. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by making it easier to use these data in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

A dataset listing the richest zip codes in North Carolina per the most current US Census data, including information on rank and average income.