This is a listing of Federal Information Processing Standard (FIPS) codes for each of the 67 counties in Pennsylvania. Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html For more technical details :

Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235.

Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.).

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.

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

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.

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.

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.

This is a polygon dataset for county boundaries as well as for cities, township and unorganized territory (CTU) boundaries in the Twin Cities 7-county metropolitan area as of April 2000. This dataset will be kept static as of this date to be used with 2000 census data and other year 2000 data.

This dataset is composed of three shape files and a lookup table.

Census2000Counties.shp = county boundaries

Census2000CTUs.shp = city, township and unorganized territory boundaries

Census2000CountiesAndCTUs.shp = combined county and CTU boundaries

Census2000CountyCTULookupTable.dbf = a lookup table containing various data related to cities, townships, unorganized territories and any divisions created by county boundaries splitting them.


NOTES:

- 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 primarily from each county, with MN/DOT data being used in Anoka 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 to match 2000 U.S. Census data.

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.

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

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.]

"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."

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 (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.

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.

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.

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).

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.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.