This tool--a simple csv or Stata file for merging--gives you a fast way to assign Census county FIPS codes to variously presented county names. This is useful for dealing with county names collected from official sources, such as election returns, which inconsistently present county names and often have misspellings. It will likely take less than ten minutes the first time, and about one minute thereafter--assuming all versions of your county names are in this file. There are about 3,142 counties in the U.S., and there are 77,613 different permutations of county names in this file (ave=25 per county, max=382). Counties with more likely permutations have more versions. Misspellings were added as I came across them over time. I DON'T expect people to cite the use of this tool. DO feel free to suggest the addition of other county name permutations.

Context

Dataset created to link between County - State Name, State-County FIPS, and ZIP Code.

Acknowledgements

Data Sources

• US HUD

https://www.huduser.gov/portal/datasets/usps.html

• Census Bureau

https://www2.census.gov/geo/docs/reference/codes/files/national_county.txt https://www.census.gov/geo/reference/codes/cou.html

Data cleaned by Data4Democracy and hosted originally on Data.World: https://github.com/Data4Democracy/zip-code-to-county https://data.world/niccolley/us-zipcode-to-county-state

ZCTA data from USPS 6.2017 release.

Image from Reddit.

Unlock the power of geographical precision with this comprehensive ZIP to FIPS dataset. Seamlessly map ZIP codes to Federal Information Processing Standards (FIPS) codes, enabling accurate location-based analyses. Ideal for geospatial applications, demographic research, and data integration. Explore the seamless fusion of ZIP and FIPS codes for enhanced spatial insights.

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

Context

There is no story behind this data.

These are just supplementary datasets which I plan on using for plotting county wise data on maps.. (in particular for using with my kernel : https://www.kaggle.com/stansilas/maps-are-beautiful-unemployment-is-not/)
As that data set didn't have the info I needed for plotting an interactive map using highcharter .

Content

Since I noticed that most demographic datasets here on Kaggle, either have state code, state name, or county name + state name but not all of it i.e county name, fips code, state name + state code.

Using these two datasets one can get any combination of state county codes etc.

States.csv has State name + code
US counties.csv has county wise data.

Acknowledgements

Picture : https://unsplash.com/search/usa-states?photo=-RO2DFPl7wE
Counties : https://www.census.gov/geo/reference/codes/cou.html
State :

Inspiration

Not Applicable.

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.

A list of Connecticut municipalities with the 3-digit tax code and the 2010 10-digit FIPS code for county subdivisions, assigned by the U.S. Census Bureau

explain the relationship between DMA, FIPS and county

ChatGPT said: Here's a breakdown of the relationship between DMA, FIPS, and county:

🗺️ 1. County A county is a geographic and administrative subdivision of a U.S. state.

For example: Cook County in Illinois or Los Angeles County in California.

The U.S. has over 3,000 counties or county-equivalents.

🔢 2. FIPS Code FIPS = Federal Information Processing Standards code.

Used to uniquely identify geographic areas like states and counties.

It consists of:

State FIPS (2 digits) — e.g., California = 06

County FIPS (3 digits) — e.g., Los Angeles County = 037

Together: 06037 (California + Los Angeles County)

FIPS codes are used in datasets, GIS, and federal data standards to track data consistently across agencies.

📺 3. DMA (Designated Market Area) DMA is a geographic region defined by Nielsen where the population receives the same (or similar) TV and radio content.

Used in advertising, media, and Google Trends to segment the market by media consumption patterns.

There are 210 DMAs in the U.S., each labeled by Nielsen with a DMA code or name (e.g., "New York", "Chicago", "Palm Springs").

📌 How They Relate Concept Level Code Type Example County Local FIPS Los Angeles County (06037) FIPS Code System Numeric Encodes state + county DMA Media Region Name/Index Los Angeles DMA

One DMA contains multiple counties.

Example: The Los Angeles DMA includes Los Angeles County, Orange County, Ventura County, etc.

Each county has a unique FIPS code, but a DMA might span counties in multiple states.

When mapping or analyzing data (e.g., in GIS or Google Trends), you often merge county-level data (FIPS) into DMAs for media or market analysis.

🧭 Example State County FIPS DMA Name DMA Code California Los Angeles 06037 Los Angeles 803 California Orange 06059 Los Angeles 803 California Riverside 06065 Palm Springs 804

👉 DMA for FIPS 02013?

Alaska is unique: it’s sparsely populated and has limited Nielsen DMA coverage.

Most of rural Alaska does not belong to a defined DMA. Instead, large parts of Alaska are treated as “non-DMA” or are grouped into a single large DMA (often labeled Anchorage DMA if included).

Matched Continuum of Care (CoC) to FIPS codes for all principal cities from 2019-2022. FIPS data from Census designations and CoC data from HUD Point in Time and CoC designations (see sheet 3 for complete data sources and links).

This reference table contains data elements for the 58 Counties in California that can be used to join to other data sets. This data includes the following fields:DHCS County CodeCounty NameCounty Region CodeCounty Region DescriptionFIPS County Code (xxx)FIPS State Code + FIPS County Code (06xxx)North/South Indicator

PRISM data converted into FIPS, ZIP Code, and census tract summaries in the USA Introduction: Parameter-elevation Regressions on Independent Slopes Model (PRISM) by PRISM Climate group Oregon State temperature, precipitation 4km daily weather variable grids that I have converted to daily county FIPS, ZIP Code, and census tract summaries for use in several papers. Available for download (see Data below) in RDS (compact) format. CSV available on request. In Python it is easy to load RDS files and much more compact files than CSVs too. Note that ZIP Code throughout is actually ZIP Code Tabulation Area (ZCTA), which was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code. Defining the extent of an area is necessary in order to tabulate census data for that area.

Abstract (en): This data collection contains FIPS codes for state, county, county subdivision, and place, along with the 1990 Census tract number for each side of the street for the urban cores of 550 counties in the United States. Street names, including prefix and/or suffix direction (north, southeast, etc.) and street type (avenue, lane, etc.) are provided, as well as the address range for that portion of the street located within a particular Census tract and the corresponding Census tract number. The FIPS county subdivision and place codes can be used to determine the correct Census tract number when streets with identical names and ranges exist in different parts of the same county. Contiguous block segments that have consecutive address ranges along a street and that have the same geographic codes (state, county, Census tract, county subdivision, and place) have been collapsed together and are represented by a single record with a single address range. 2006-01-12 All files were removed from dataset 551 and flagged as study-level files, so that they will accompany all downloads. (1) Due to the number of files in this collection, parts have been eliminated here. For a complete list of individual part names designated by state and county, consult the ICPSR Website. (2) There are two types of records in this collection, distinguished by the first character of each record. A "0" indicates a street name/address range record that can be used to find the Census tract number and other geographic codes from a street name and address number. A "2" indicates a geographic code/name record that can be used to find the name of the state, county, county subdivision, and/or place from the FIPS code. The "0" records contain 18 variables and the "2" records contain 10 variables.

This CT Planning Regions layer consists of individual polygons representing each of the 169 municipalities that make up the state of Connecticut.

This feature layer is directly derived from the 'https://geodata.ct.gov/datasets/CTDOT::ct-municipalities/about' rel='nofollow ugc'>CTDOT Municipalities feature layer geometry, created by CT Department of Transportation. The municipalities are dissolved into their associated regional Councils of Governments.

This feature layer includes US Census Federal Information Processing Standards (FIPS) codes that are associated with each municipality. This was included based on information from 'https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes/2020.html' rel='nofollow ugc'>Connecticut County to County Subdivision Crosswalk from the US Census.

Zip archive of files (one per state and sex) of county-level life tables from 1928-2019 by 5yr age group in R-binary (.rds) format. We group low-population counties and geographically coterminous neighbors (1084 total) together into county-groups (401 total) with historically consistent boundaries that exceed a minimum population threshold of 10,000 at any time point in our series. County groupings never cross state borders. 2071 counties are left ungrouped. A comma-separated variable lookup table linking the 1084 individual counties (by FIPS code) to the 401 groups are available in the USMDBcountyGroupings.csv. The individual counties in the life table files are identified by their FIPS code.

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.. 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. 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

This is a polygon dataset for county boundaries as well as for city, township and unorganized territory (CTU) boundaries in the Twin Cities 7-county metropolitan area. The linework for this dataset comes from individual counties and is assembled by the Metropolitan Council for the MetroGIS community.

The County CTU Lookup Table here https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-counties-and-ctus-lookup
is also included in this dataset and contains various data related to cities, townships, unorganized territories (CTUs) and any divisions created by county boundaries splitting them is also included in the dataset.

This dataset is updated quarterly. This dataset is composed of three shape files and one dbf table.
- Counties.shp = county boundaries
- CTUs.shp = city, township and unorganized territory boundaries
- CountiesAndCTUs.shp = combined county and CTU boundaries
- CountyCTULookupTable.dbf = various data related to CTUs and any divisions created by county boundaries splitting them is also included in the dataset, described here: https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-counties-and-ctus-lookup

NOTES:

- 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.

- The five digit CTU codes in this dataset are identical to the Federal Information Processing Standard (FIPS) ''Place'' codes. They are also used by the Census Bureau and many other organizations and are proposed as a MN state data coding standard.

- 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.

- The boundary line data for this dataset comes from each county.

- 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.

This dataset contains a listing of incorporated places (cities and towns) and counties within the United States including the GNIS code, FIPS code, name, entity type and primary point (location) for the entity. The types of entities listed in this dataset are based on codes provided by the U.S. Census Bureau, and include the following: C1 - An active incorporated place that does not serve as a county subdivision equivalent; C2 - An active incorporated place legally coextensive with a county subdivision but treated as independent of any county subdivision; C3 - A consolidated city; C4 - An active incorporated place with an alternate official common name; C5 - An active incorporated place that is independent of any county subdivision and serves as a county subdivision equivalent; C6 - An active incorporated place that partially is independent of any county subdivision and serves as a county subdivision equivalent or partially coextensive with a county subdivision but treated as independent of any county subdivision; C7 - An incorporated place that is independent of any county; C8 - The balance of a consolidated city excluding the separately incorporated place(s) within that consolidated government; C9 - An inactive or nonfunctioning incorporated place; H1 - An active county or statistically equivalent entity; H4 - A legally defined inactive or nonfunctioning county or statistically equivalent entity; H5 - A census areas in Alaska, a statistical county equivalent entity; and H6 - A county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.

Montgomery County is an Exception Grantee, meaning that the highest quartile of block groups for low- and moderate-income percent constitute the areas where Area Benefit may be applied, even though all areas contain less than 51% low- and moderate-income persons. Montgomery County’s exception threshold for 2023 is 42.88%. 160 block groups qualify based on this threshold.You can search for and download the latest Low to Moderate Income Population by Block Group data for the entire United States from HUD here.Data Dictionary:GEOIDThis is the concatenation of State, County, Tract, and Block Group FIPS codes.SOURCE GEONAMEThe name of the block group, place, county, or county subdivision.STUSABThe state abbreviation.COUNTYNAMEThe Name of the County.STATEThe numeric Federal Information Process Standards (FIPS) state code.COUNTYThe numeric Federal Information Processing Standards (FIPS) county code.TRACTThe numeric code for the census tract. In other publications or reports, the code sometimes appears as a 2 digit decimal XXXX.XX.BLKGRPThe block group code.LOWThe count of Low-income persons.LOWMODThe count of Low- and Moderate-income persons.LMMIThe count of Low-, Moderate-, and Medium-income persons for the NSP programs..LOWMODUNIVPersons with the potential for being deemed Low-, Moderate- and Middle-income. Use as the denominator for LOW, LOWMOD, and LMMI %'s.LOWMOD_PCTThe percentage of Low- and Moderate-income persons. Calculated from LOWMOD divided by LOWMODUNIV.UCLOWMODThe uncapped count of Low- and Moderate-income persons.UCLOWMOD_PThe percentage of uncapped Low- and Moderate-income persons. Calculated from UCLOWMOD divided by LOWMODUNIV.MOE LOWMOD PCTThe margin of error (MOE) for the LOWMOD_PCT.MOE UCLOWMOD PCTThe margin of error (MOE) for the UCLOWMOD_PCT.

US Northeast Census Tracts contains the US Census tract geometries used as the unit of analysis for network metrics. The file "northeast_tracts.shp" includes a merged dataset with the borders of all census tracts in Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. All other files in this repository are the original state-by-state sources used to create the final merged dataset. Census Tracts The 2020 census tract file is based on the 2020 Census. The following fields are included: USPS: United States Postal Service state abbreviation. GEOID: Geographic identifier — fully concatenated geographic code (State FIPS, County FIPS, Census Tract number). GEOIDFQ: Fully qualified geographic identifier — used to join with data.census.gov data tables. ALAND: Land area (square meters) — created for statistical purposes only. AWATER: Water area (square meters) — created for statistical purposes only. ALAND_SQMI: Land area (square miles) — created for statistical purposes only. AWATER_SQMI: Water area (square miles) — created for statistical purposes only. INTPTLAT: Latitude (decimal degrees). The first character is blank or “–” denoting North or South latitude respectively. INTPTLONG: Longitude (decimal degrees). The first character is blank or “–” denoting East or West longitude respectively. The .shp file in this repository includes its required companion files for correct GIS operation: .shx (spatial index), .dbf (attribute table), .prj (projection information), and .cpg (character encoding).