description: The US Census Bureau's online County Look-up Tool provides the unique 3-digit code for the Identification of Counties and Equivalent Entities of the United States, its Possessions, and Insular Areas.; abstract: The US Census Bureau's online County Look-up Tool provides the unique 3-digit code for the Identification of Counties and Equivalent Entities of the United States, its Possessions, and Insular Areas.

State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.

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 is a lookup table containing various data related to cities, townships, unorganized territories (CTUs) and any divisions created by county boundaries splitting them. These are termed Minor Civil Division (MCDs) by the Census Bureau. The table encompases the Twin Cities 7-county metropolitan area. It is intended to be a Council wide master lookup table for these entites. It contains official federal and state unique identifiers for CTUs and MCDs as well as identifiers created and used by other organizations. The table also contains historical MCDs dating back to the 1990s and a few other non-MCD records that are of importance to Met. Council use of this table.

The County CTU Lookup Table relates to the Counties and Cities & Townships, Twin Cities Metropolitan Area dataset here: https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-metro-counties-and-ctus

NOTES:

- On 5/28/2014 a new field was added to reflect the new community designations defined in the Council's Thrive MSP 2040 regional plan - COMDES2040

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lake St. Croix Beach was incorrect. It was changed from 2394379 to 2395599 to match GNIS.

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lilydale was incorrect. It was changed from 2394457 to 2395708 to match GNIS.

- On 11/9/2010 it was discovered that the CTU ID used for the City of Crystal was incorrect. It was changed from 2393541 to 2393683 to match GNIS.

- Effective April 2008, a change was made in GNIS to match the FIPS place codes to the "civil" feature for each city instead of the "populated place" feature. Both cities and townships are now "civil" features within GNIS. This means that the official GNIS unique ID for every city in Minnesota has changed.

- As of January 1, 2006, the five digit FIPS 55-3 Place codes that were used as unique identifiers in this dataset (CTU_CODE and COCTU_CODE fields) were officially retired by the Federal governement. They are replaced by a set of integer codes from the Geographic Names Information System (GNIS_CODE field). Both codes will be kept in this database, but the GNIS_CODE is considered the official unique identifier from this point forward. The GNIS codes are also slated to become official ANSI codes for these geographic features. While GNIS treats these codes as 6 to 8 digit integer data types, the Census Bureau formats them as 8 digit text fields, right justified with leading zeros included.

- The Census Bureau will continue to create FIPS 55 Place codes for new cities and townships through the 2010 Census. After that, no new FIPS 55 codes will be created. Note that for townships that wholly incorporate into cities, the same FIPS 55 code will be used for the new city. (GNIS creates a new ID for the new city.)

- Cities and townships have also been referred to as ''MCDs'' (a Census term), however this term technically refers to the part of each city or township within a single county. Thus, a few cities in the metro area that are split by county boundaries are actually comprised of two different MCDs. This was part of the impetus for a proposed MN state data standard that uses the ''CTU'' terminology for clarity.

- A variety of civil divisions of the land exist within the United States. In Minnesota, only three types exist - cities, townships and unorganized territories. All three of these exist within the Twin Cities seven county area. The only unorganized territory is Fort Snelling (a large portion of which is occupied by the MSP International Airport).

- Some cities are split between two counties. Only those parts of cities within the 7-county area are included.

- Prior to the 2000 census, the FIPS Place code for the City of Greenwood in Hennepin County was changed from 25928 to 25918. This dataset reflects that change.

description: This data set contains county estimates of nitrogen and phosphorus content of animal wastes produced annually for the years 1982, 1987, and 1992. The estimates are based on animal populations for those years from the 1992 Census of Agriculture (U.S. Bureau of the Census, 1995) and methods for estimating the nutrient content of manure from the Soil Conservation Service (1992). The data set includes several components.. 1. Spatial component - generalized county boundaries in ARC/INFO format/1/, including nine INFO lookup tables containing animal counts and nutrient estimates keyed to the county polygons using county code. (The county lines were not used in the nutrient computations and are provided for displaying the data as a courtesy to the user.) The data is organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another INFO table lists the county names that correspond to the FIPS codes. 2. Tabular component - Nine tab-delimited ASCII lookup tables of animal counts and nutrient estimates organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another table lists the county names that correspond to the FIPS codes. The use of trade names is for identification purposes only and does not constitute endorsement by the U.S. Geological Survey.; abstract: This data set contains county estimates of nitrogen and phosphorus content of animal wastes produced annually for the years 1982, 1987, and 1992. The estimates are based on animal populations for those years from the 1992 Census of Agriculture (U.S. Bureau of the Census, 1995) and methods for estimating the nutrient content of manure from the Soil Conservation Service (1992). The data set includes several components.. 1. Spatial component - generalized county boundaries in ARC/INFO format/1/, including nine INFO lookup tables containing animal counts and nutrient estimates keyed to the county polygons using county code. (The county lines were not used in the nutrient computations and are provided for displaying the data as a courtesy to the user.) The data is organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another INFO table lists the county names that correspond to the FIPS codes. 2. Tabular component - Nine tab-delimited ASCII lookup tables of animal counts and nutrient estimates organized by 5-digit state/county FIPS (Federal Information Processing Standards) code. Another table lists the county names that correspond to the FIPS codes. The use of trade names is for identification purposes only and does not constitute endorsement by the U.S. Geological Survey.

Abstract

A crosswalk dataset matching US ZIP codes to corresponding county codes

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.)

County definition

In the United States, a county is an administrative or political subdivision of a state that consists of a geographic region with specific boundaries and usually some level of governmental authority. The term "county" is used in 48 U.S. states, while Louisiana and Alaska have functionally equivalent subdivisions called parishes and boroughs, respectively.

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

(SEE SUPPLEMENTAL INFORMATION SECTION FOR FILE-SPECIFIC INFORMATION.)Digital orthophoto quarter-quads are now available for most of the United States and its Territories. Quarter-quad DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed on CD-ROM, DVD, and File Transfer Protocol (FTP) as uncompressed files.A downloadable software is available (DOQQ-to-GeoTIFF conversion) which will convert a DOQ image from Native to GeoTIFF format in either NAD27 or NAD83. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

RateWatch provides 3 datasets containing interest rates for deposits, loans and fees at branch level for U.S. financial institutions covering over 96,000 locations. Data is gathered from institutions of all types and sizes. The largest depth of data is around consumer products such as CDs, Savings, Checking, Money Markets, Auto Loans, Home Equity Loans and Mortgages. Within each category, details are available for multiple terms and/or dollar tiers. Data includes several identifying fields including institution name, address, routing number, asset size, institution type, MSA codes, latitude, longitude, state and city FIPS code. Consult relevant data dictionary for more information. File structure: text delimited (delimiter: pipe). "Join" file provides cross reference between an institution's rate setting location and its other branches. A given Institution may have multiple rate setting locations that may vary by product type. DATA AVAILABLE FOR YEARS: 2001-2024 (2025-02-05)

Abstract (en): The CFFR covers federal expenditures or obligations for the following categories: grants, salaries and wages, procurement contracts, direct payments for individuals, other direct payments, direct loans, guaranteed or insured loans, and insurance. Information available in the CFFR data file includes the government identification code, program identification code, object/assistance type code, amount in whole dollars, and FIPS code. For each unique government unit code all programs are listed, and for each program all records with different object categories are listed. The Geographic Reference File contains the names and governmental unit codes for all state, county, and subcounty areas in the country. In addition, the file contains associated geographic codes (FIPS, GSA, MSA, and Census Bureau place codes), the 1986 population, and the congressional districts serving each government unit. The Program Identification File contains program identification codes and their respective program titles. Federal government expenditures or obligations in state, county, and subcounty areas of the United States. United States Territories and the District of Columbia are included. 2006-01-18 File CB9364.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.

This data collection relates ZIP codes to counties, to standard metropolitan statistical areas (SMSAs), and, in New England, to minor civil divisions (MCDs). The relationships between ZIP codes and other geographical units are based on 1979 boundaries, and changes since that time are not reflected. The Census Bureau used various sources to determine ZIP code-county or ZIP code-MCD relationships. In the cases where the sources were confusing or contradictory as to the geographical boundaries of a ZIP code, multiple ZIP-code records (each representing the territory contained in that ZIP-code area) were included in the data file. As a result, the file tends to overstate the ZIP code-county or ZIP code-MCD crossovers. The file is organized by ZIP code and is a byproduct of data used to administer the 1980 Census. Variables include ZIP codes, post office names, FIPS state and county codes, county or MCD names, and SMSA codes.

This data layer is an element of the Oregon GIS Framework. 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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Harvard CGA Geotweet Census Archive is a subset of Harvard CGA Geotweet Archive v2.0 enriched with nationwide census data. It contains the tweet and user identification records along with census variables for more than 2 billion geo-tagged tweets from January 2012 to July 2023. This dataset is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet Census Archive Field name_TYPE_Description message_id----TEXT----Tweet ID user_id ----TEXT----User ID number fips ----FLOAT----County fips code county ----TEXT----County name state ----TEXT----State abbreviation GEOID20 ----FLOAT----Census block geoid

The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This dataset contains records for each public elementary and secondary education agency in the 50 states, the District of Columbia, United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands), and Department of Defense schools outside of the United States for 1994-1995. Data were reported to the Bureau of the Census for the National Center for Education Statistics by the state coordinators. Each record provides state and federal identification numbers, agency name, address, and telephone number, county name and FIPS code, agency type code, student counts, graduates and other completers counts, and other codes for selected characteristics of the agency. In addition, grade span, number of schools operated by the agency, and number of classroom teachers were aggregated.

Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Removed data from Chattahoochee Hills (ORI = "GA06059") from 2016 data. In 2016, that agency reported about 28 times as many vehicle arsons as their population (Total mobile arsons = , population = 2754.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Arson data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about arsons reported in the United States. The data sets here combine all data from the years 2001-2015 into a single file. The year 2006 is not available. Please note that the files are quite large and may take some time to open.The raw data that I downloaded from NACJD has monthly data. The data here is yearly and was created by adding all the monthly columns together for each variable. The format is similar to the UCR's Offenses Known data where each row is an agency-year and columns are crime counts for various crimes. Instead of various crimes, here it is the type of arson such as arson of a single occupancy building, a storage building, or a motor vehicle. Like the Offenses Known data it has the number of reports found to have actually occurred ("actual"), be unfounded, cleared, and cleared with an arrestee under the age of 18. There are also columns for the total number of arsons reported to police, total number of arsons of uninhabited buildings, and estimated damage from the arson.About 30% of the rows were from agencies that did not report any months of data. I removed these rows to reduce file size. I did not make any changes to the data other than the following: Change some column names, reorder columns, and spell out the month in the months reported variable (originally some months were abbreviated). Years 2001 and 2002 had "1" and "2" as their reported years which I changed to "2001" and "2002". I deleted the agency of Oneida, New York (ORI = NY03200), since they had multiple years that reported single arsons costing over $700 million. I also added state, county, and place FIPS code from the LEAIC (crosswalk).When an arson is determined to be unfounded the estimated damage from that arson is added as negative to zero out the previously reported estimated damages. This occasionally leads to some agencies have negative values for arson damages. You should be cautious when using the estimated damage columns as some values are quite large. Negative values in other columns are also due to adjustments (zeroing out the error) from month to month. Negative values are not meant to be NA in this data set. All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

The database includes ZIP code, city name, alias city name, state code, phone area code, city type, county name, country FIPS, time zone, day light saving flag, latitude, longitude, county elevation, Metropolitan Statistical Area (MSA), Primary Metropolitan Statistical Area (PMSA), Core Based Statistical Area (CBSA) and census 2000 data on population by race, average household income, and average house value.

This dataset contains FIPS (Federal Information Processing Standard), GNIS (Geographic Name Information System common) codes for identifying Washington state counties cities and towns. This is an official list from OFM (Office of Financial Management).

The Geographic Names Information System (GNIS) was developed by the United States Geological Survey (USGS) to meet major national needs regarding geographic names and their standardization and dissemination. This dataset consists of standard report files written from the National Geographic Names Data Base, one of five data bases maintained in the GNIS. A standard format data file containing Michigan place names and geographic features such as towns, schools, reservoirs, parks, streams, valleys, springs and ridges is accompanied by a file that provides a Cross-Reference to USGS 7.5 x 7.5 minute quadrangle maps for each feature. The records in the data files are organized alphabetically by place or feature name. The other variables available in the dataset include: Federal Information Processing Standard (FIPS) state/county codes, Geographic Coordinates -- latitude and longitude to degrees, minutes, and seconds followed by a single digit alpha directional character, and a GNIS Map Code that can be used with the Cross-Reference file to provide the name of the 7.5 x 7.5 minute quadrangle map that contains that geographic feature.

This data set consists of digital county boundaries for Oklahoma. The boundaries were derived from U.S. Geological Survey 1:100,000-scale Digital Line Graph data sets. Two attributes are associated with each county polygon. The first is the FIPS code and the second is the county name.

This dataset contains model-based census tract level estimates for the PLACES 2021 release in GIS-friendly format. PLACES 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 Area (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. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 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. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract 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=024cf3f6f59e49fe8c70e0e5410fe3cf