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.

This dataset was created to help users to go between County - State Name, State-County FIPS, City, or to ZIP Code. Most importantly, this dataset was created because we shouldn't have to pay for free & public data.

Assumptions - HUD uses the most up to date Zip Code boundaries from the USPS when they post their new Quarterly data. *ZIP Codes are updated on a regular basis. Here is an example announcement from the USPS. - City data only available from 2018 onward.

Data Sources

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

Census Bureau The table data, direct link. This data is only updated once every census, 10 years. The details of the National County text file can be found here

USPS Zip to City Lookup More information can be found here. It's a free API from the USPS. Need to create a username to pull the data.

Data Dictionary

Files 2018 -> Newer - ZIP ZIP Code - STCOUNTFP US State & County FIPS ID - CITY City for that Zip/Fips Code - STATE US State - COUNTYNAME US County Name - CLASSFP FIPS Class Code, as defined by the Census

Files 2010-2017 - ZIP ZIP Code - COUNTYNAME US County Name - STATE US State - STCOUNTFP US State & County FIPS ID - CLASSFP FIPS Class Code, as defined by the Census

FIPS Class Code Details Source Copied 7/29/17

• H1 identifies an active county or statistically equivalent entity that does not qualify under subclass C7 or H6.
• H4 identifies a legally defined inactive or nonfunctioning county or statistically equivalent entity that does not qualify under subclass H6.
• H5 identifies census areas in Alaska, a statistical county equivalent entity.
• H6 identifies a county or statistically equivalent entity that is a really coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
• C7 identifies an incorporated place that is an independent city; that is, it also serves as a county equivalent because it is not part of any county, and a minor civil division (MCD) equivalent because it is not part of any MCD.

Acknowlegement

Foto von Annie Spratt auf Unsplash

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.

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.

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.

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.

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

The dataset contains a hierarchal listing of New York State counties, cities, towns, and villages, as well as official locality websites

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. This is a MetroGIS Regionally Endorsed dataset https://metrogis.org/.

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.

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.

"This file provides digital data for all 1990 precensus map features, the associated 1990 census initial tabulation geographic area codes, such as 1990 census block numbers, and the codes for the Jan. 1, 1990 political areas on both sides of each line segment of every mapped feature. The data contain basic information on 1990 census geographic area codes, feature names, and address ranges in the form of ten ""Record Types."" The Census Bureau added four new record types in response to some us er and vendor requests to provide point and area information contained in the Census Bureau's Precensus Map sheets that is not contained in the Precensus TIGER/Line files. The record types include: Basic data records (individual Feature Segment Records), shape coordinate points (feature shape records), additional decennial census geographic area codes, index to alternate feature names, feature name list, additional address range and zip code data, landmark features, area landmarks, area boundaries, and polygon location. Each segment record contains appropriate decennial census and FIPS geographic area codes, latitude/longitude coordinates, the name of the feature (including the relevant census feature class code identifying the segment by category), and, for areas formerly covered by the GBF/DIME-Files, the address ranges and ZIP code associated with those address ranges for each side of street segments. For other areas, the TIGER?Line files do not contain address ranges or ZIP Codes. The shape records provide coordinate values that describe the shape of those feature segments that are not straight."

From https://www.census.gov/hhes/commuting/data/jtw_workerflow.html as of March 29, 2017:These files were compiled from STF-S-5, Census of Population 1990: Number of Workers by County of Residence by County of Work [http://doi.org/10.3886/ICPSR06123.v1]. For the six New England States (CT, ME, MA, NH, RI, VT), data are provided for Minor Civil Divisions (MCDs) instead of for counties.For any State, or for the entire nation, there are four files to choose from, depending on the sort order and format you may find most useful.The sort order refers to whether the county of residence or the county of work is the main focus. If you are most interested in the number of people who live in a county, and want to know where they go to work, you should download one of the files sorted by county of residence. These files will show you all the work destinations for people who live in each county.On the other hand, if you are most interested in the people who work in a county, and want to know where they come from, you should download one of the files sorted by county of work. These files will show you all the origins for people who work in each county.The files have also been created in two formats: DBF and ASCII. The DBF files are directly accessible by a number of database, spreadsheet, and geographic information system programs. The ASCII files are more general purpose and may be imported into many software applications.Record Layouts Record Layout for ASCII (Plain Text) Files [TXT - 2K] coxcoasc.txtRecord Layout for DBF Files [TXT - 2K]coxcodbf.txtThe link to the FIPS Lookup File [ed.: absent when archived] can be used to access a list of FIPS State codes and the corresponding State names. In the county-to-county worker flow files, only the State codes are used. The files do not contain State names.United States county-to-county worker flow files: 1990 Residence County USresco.txt USresco.zip USresco.dbf USresco.dbf.zipWork County USwrkco.txt USwrkco.zip USwrkco.dbf USwrkco.dbf.zip [Ed.: the original site also had state files. These were not downloaded, as they simply split the United States file into smaller chunks.]

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.

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 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.

This CT Planning Regions layer consists of individual polygons representing each of the 9 planning regions that make up the state of Connecticut.

This feature layer is directly derived from the 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 Connecticut County to County Subdivision Crosswalk from the US Census.

Connecticut’s 9 planning regions provide a geographic framework within which municipalities can jointly address common interests and coordinate such interests with state plans and programs. CGS Section 16a-4a authorizes the Secretary of the Office of Policy and Management (OPM) to designate or redesignate the boundaries of logical planning regions. CGS Section 4-124j authorizes the member municipalities of each planning region to establish a formal regional governance structure known as a council of governments (COG).

These regions have been recognized as county-equivalents and supersede the eight legacy counties in the state.

For more information see:

CT Office of Policy and Management, Regional Councils of Governments in Connecticut Final Change to County Equivalents in Connecticut [PDF] Final Federal Register Notice CT Office of the Secretary of the State, Regional Councils of Governments

Field name

Field description

PlanningRegion

Name of the Councils of Governments planning region.

PlanningRegion FIPS

US Census FIPS code associated with the Councils of Governments planning region.

StateFIPS

US Census FIPS code associated with the state.

PlanningRegionFIPS_GEOID

Full US Census FIPS for the COG.

ObjectID

Unique Object ID.

CT Municipalities

CT Counties

CT Councils of Governments

CT Planning Regions

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.

The dataset, provided both in comma-separated values (.csv) and the more informative Stata (.dta) format, contains place/year demographic data on more than 300 rural Alaska communities annually for 1990 to 2022 -- about 10,000 place/years. For each of the available place/years, the data include population estimates from the Alaska Department of Labor and Workforce Development or (in Census years) from the US Census. For a subset consisting of 104 northern or western Alaska (Arctic/subarctic) towns and villages, the dataset also contains yearly estimates of natural increase (births minus deaths) and net migration (population minus last year's population plus natural increase). Natural increase was calculated from birth and death counts provided confidentially to researchers by the Alaska Health Analytics and Vital Records Section (HAVRS). By agreement with HAVRS, the community-level birth and death counts are not available for publication. Population, natural increase, and net migration estimates reflect mid-year values, or change over the past fiscal rather than calendar year. For example, the natural increase value for a community in 2020 is based on births and deaths of residents from July 1, 2019 to June 31, 2020. We emphasize that all values here are best estimates, based on records of the Alaska government organizations. The dataset contains 19 variables: placename Place name (string) placenum Place name (numeric) placefips Place FIPS code year Year borough Borough name boroughfips Borough FIPS code latitude Latitude (decimal, - denotes S) longitude Longitude (decimal, - denotes W) town Village {0:pop2020<2,000} or town {1:pop2020>2,000} village104 104 selected Arctic/rural communities {0,1} arctic43 43 Arctic communities {0,1}, Hamilton et al. 2016 north37 37 Northern Alaska communities {0,1), Hamilton et al. 2016 pop Population (2022 data) cpopP Change in population, percent natinc Natural increase: births-deaths natincP Natural increase, percent netmig Net migration estimate netmigP Net migration, percent nipop Population without migration Three of these variables flag particular subsets of communities. The first two subsets (43 or 37 places) were analyzed in earlier publications, so the flags might be useful for replications or comparisons. The third subset (104 places) is a newer, expanded group of Arctic/subarctic towns and villages for which natural increase and net migration estimates are now available. The flag variables are: If arctic43 = 1 Subset consisting of 43 Arctic towns and villages, previously studied in three published articles: 1. Hamilton, L.C. & A.M. Mitiguy. 2009. “Visualizing population dynamics of Alaska’s Arctic communities.” Arctic 62(4):393–398. https://doi.org/10.14430/arctic170 2. Hamilton, L.C., D.M. White, R.B. Lammers & G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. https://doi.org/10.1007/s11111-011-0145-1 3. Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers & H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. https://doi.org/10.1007/s11111-016-0259-6 If north37 = 1 Subset consisting of 37 northern Alaska towns and villages, previously analyzed for comparison with Nunavut and Greenland in a paper on demographics of the Inuit Arctic: 4. Hamilton, L.C., J. Wirsing & K. Saito. 2018. “Demographic variation and change in the Inuit Arctic.” Environmental Research Letters 13:11507. https://doi.org/10.1088/1748-9326/aae7ef If village104 = 1 Expanded group consisting of 104 communities, including all those in the arctic43 and north37 subsets. This group includes most rural Arctic/subarctic communities that had reasonably complete, continuous data, and 2018 populations of at least 100 people. These data were developed by updating older work and drawing in 61 additional towns or villages, as part of the NSF-supported Arctic Village Dynamics project (OPP-1822424).

Researchers have long been able to analyze crime and law enforcement data at the individual agency level and at the county level using data from the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data series. However, analyzing crime data at the intermediate level, the city or place, has been difficult, as has merging disparate data sources that have no common match keys. To facilitate the creation and analysis of place-level data and linking reported crime data with data from other sources, the Bureau of Justice Statistics (BJS) and the National Archive of Criminal Justice Data (NACJD) created the Law Enforcement Agency Identifiers Crosswalk (LEAIC). The crosswalk file was designed to provide geographic and other identification information for each record included in the FBI's UCR files and Bureau of Justice Statistics' Census of State and Local Law Enforcement Agencies (CSLLEA). The LEAIC records contain common match keys for merging reported crime data and Census Bureau data. These linkage variables include the Originating Agency Identifier (ORI) code, Federal Information Processing Standards (FIPS) state, county and place codes, and Governments Integrated Directory government identifier codes. These variables make it possible for researchers to take police agency-level data, combine them with Bureau of the Census and BJS data, and perform place-level, jurisdiction-level, and government-level analyses.

A comma-separated variable lookup table linking the 1084 individual counties (by FIPS code) to the 401 groups.