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 are the full-resolution boundary zip code tabular areas (ZCTA), derived from the US Census Bureau's TIGER/Line Shapefiles. The dataset contains polygons that roughly approximate each of the USPS 5-digit zip codes. It is one of many geography datasets available in BigQuery through the Google Cloud Public Dataset Program to support geospatial analysis. You can find more information on the other datasets at the US Geographic Boundaries Marketplace page . Though they do not continuously cover all land and water areas in the United States, ZCTAs are a great way to visualize geospatial data in an understandable format with excellent spatial resolution. This dataset gives the area of land and water within each zip code, as well as the corresponding city and state for each zip code. This makes the dataset an excellent candidate for JOINs to support geospatial queries with BigQuery’s GIS capabilities. Note: BQ-GIS is in public beta, so your GCP project will need to be whitelisted to try out these queries. You can sign up to request access here . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

Zip Codes (5-digit). The dataset polygons represent location and attributes of zip codes, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Zip Codes were identified from public records (US Postal Service) and created selecting arcs from the street centerlines and vector property map.

This feature class contains polygons of the zip code boundaries within Manatee County.

This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

This child item describes R code used to determine public supply consumptive use estimates. Consumptive use was estimated by scaling an assumed fraction of deliveries used for outdoor irrigation by spatially explicit estimates of evaporative demand using estimated domestic and commercial, industrial, and institutional deliveries from the public supply delivery machine learning model child item. This method scales public supply water service area outdoor water use by the relationship between service area gross reference evapotranspiration provided by GridMET and annual continental U.S. (CONUS) growing season maximum evapotranspiration. This relationship to climate at the CONUS scale could result in over- or under-estimation of consumptive use at public supply service areas where local variations differ from national variations in climate. This method also assumes that 50% of deliveries for total domestic and commercial, industrial, and institutional deliveries is used for outdoor purposes. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following file: PS_ConsumptiveUse.zip - a zip file containing input datasets, scripts, and output datasets

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

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.

This dataset contains all zip codes in Montgomery County. Zip codes are the postal delivery areas defined by USPS. Zip codes with mailboxes only are not included.

As this is geographic data, SHP and KMZ formats are available for download.

DATA DICTIONARY:

Field Name
Description


AIN
Assessor ID Number


APN
Assessor Parcel Number (Mapbook, Page, Parcel)


Situs Street Number
Situs Street Number


Situs Street Number Fraction
Situs Street Number, Fraction (1/2, 3/4 etc.)


Situs Direction
Situs Direction (N, S, E, W)


Situs Unit Number
Situs Unit Number


Situs Street Name
Situs Street Name


Situs Address
Situs Address 


Situs City
Situs City


Situs Zip Code
Situs Zip Code


Situs Full Address
Situs Full Address


Tax Rate Area Code
Tax Rate Area Code


Tax Rate City
Tax Rate City 


Owner Agency Class Number
Owner Agency identifying Code (6 digit )


Owner Agency Name
Public Agency Owning the parcel


Owner Agency Type
Owner Agency Organization (City, State, etc.)


Property Use Code
Property Use Code


Transfer Recording Date
Transfer Recording Date


Transfer Document Number
Transfer Document Number


Owner Name
Owner Name


Owner Name Extension
Owner Name Extension (if any)


Second Owner (if any)
Second Owner (if any)


Owner Full Name
Owner Full Name


Mailing Address  House  Number
Mailing Address, House Number


Mailing Address Fraction
Mailing Address, Fraction


Mailing Address Direction
Mailing Address, Direction


Mailing Address Unit Number
Mailing Address, Unit Number


Mailing Address Street Name
Mailing Address, Street Name


Full Mailing Address
Full Mailing Address


Mailing Address City
Mailing Address, City


Mailing Address Zip Code
Mailing Address, Zip Code


Legal Description Line 1
Legal Description, Line 1


Legal Description Line 2
Legal Description, Line 2


Legal Description Line 3
Legal Description, Line 3


Legal Description Line 4
Legal Description, Line 4


Legal Description Line 5
Legal Description, Line 5


Legal Description Last Line
Legal Description, Last Line 


Parcel Legal Description
Combination of all Legal Description fields above


Parcel Creation Date
Parcel Creation Date


Center Latitude
Center Latitude


Center Longitude
Center Longitude


Center X-Coordinate
Center X-Coordinate


Center Y-Coordinate
Center Y-Coordinate


Latitude and Longitude
Combination of all Center-Latitude and Center Longitude, seperated with  comma


OBJECTID
Row ID

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses. Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables. Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021. This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data. This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score. This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4. The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting. These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons. For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

This dataset combines annual files from 2005 to 2017 published by the IRS. ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS. The data include items, such as:

Number of returns, which approximates the number of householdsNumber of personal exemptions, which approximates the populationAdjusted gross income (AGI)Wages and salariesDividends before exclusionInterest received Enrichment and notes:- the original data sheets (a column per variable, a line per year, zipcode and AGI group) have been transposed to get a record per year, zipcode, AGI group and variable- the data for Wyoming in 2006 was removed because AGI classes were not correctly defined, making the resulting data unfit for analysis.- the AGI groups have seen their definitions change: the variable "AGI Class" was used until 2008, with various intervals of AGI; "AGI Stub" replaced it in 2009. We provided the literal intervals (eg. "$50,000 under $75,000") as "AGI Group" in each case to help the analysis.- the codes for each tax item have been joined with a dataset of variables to provide full names.- some tax items are available since 2005, others since more recent years, depending on their introduction date (available in the dataset of variables); as a consequence, the time range of the plots or graphs may vary.- the unit for amounts and AGIs is a thousand dollars.

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: March 17, 2022National Figures: data.census.gov

During the COVID-19 pandemic, certain occupations and industries were deemed "essential", and typically included individuals who worked in healthcare, food service, public transportation, etc. However, early on in the pandemic, while these workers faced disproportionately higher risks, they often did not receive adequate personal protective equipment (PPE), were unable to work from home, and were limited in their ability to take other precautions to safeguard their health (Chen et al., 2021). As a result, previous studies have documented higher rates of infection, hospitalization, and death among essential workers compared to their non-essential worker counterparts (Selden & Berdahl, 2021; Wei et al., 2022). This dataset provides users with information on the number and proportion of essential workers in census tracts or ZIP Code tabulation areas (ZCTAs) in the United States over the 2016-2020 period.

This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release 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. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

This layer shows the population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2018-2022ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table was downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: December 15, 2023National Figures: data.census.gov

This child item describes a machine learning model that was developed to estimate public-supply water use by water service area (WSA) boundary and 12-digit hydrologic unit code (HUC12) for the conterminous United States. This model was used to develop an annual and monthly reanalysis of public supply water use for the period 2000-2020. This data release contains model input feature datasets, python codes used to develop and train the water use machine learning model, and output water use predictions by HUC12 and WSA. Public supply water use estimates and statistics files for HUC12s are available on this child item landing page. Public supply water use estimates and statistics for WSAs are available in public_water_use_model.zip. This page includes the following files: PS_HUC12_Tot_2000_2020.csv - a csv file with estimated monthly public supply total water use from 2000-2020 by HUC12, in million gallons per day PS_HUC12_GW_2000_2020.csv - a csv file with estimated monthly public supply groundwater use for 2000-2020 by HUC12, in million gallons per day PS_HUC12_SW_2000_2020.csv - a csv file with estimated monthly public supply surface water use for 2000-2020 by HUC12, in million gallons per day Note: 1) Groundwater and surface water fractions were determined using source counts as described in the 'R code that determines groundwater and surface water source fractions for public-supply water service areas, counties, and 12-digit hydrologic units' child item. 2) Some HUC12s have estimated water use of zero because no public-supply water service areas were modeled within the HUC. STAT_PS_HUC12_Tot_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply total water use from 2000-2020 STAT_PS_HUC12_GW_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply groundwater use for 2000-2020 STAT_PS_HUC12_SW_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply surface water use for 2000-2020 public_water_use_model.zip - a zip file containing input datasets, scripts, and output datasets for the public supply water use machine learning model version_history_MLmodel.txt - a txt file describing changes in this version

This layer shows housing units broken down by owner occupied and renter occupied in Tempe Zip Codes.Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online).A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): S2502 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: March 17, 2022National Figures: data.census.gov

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf