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

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.

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.

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.

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

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

The U.S. Census Bureau TIGER/Line® files in this data collection were originally distributed by the Inter-university Consortium for Political and Social Research (ICPSR) through its TIGER/Line file web site, which was decommissioned in 2018 (archived version: https://web.archive.org/web/20090924181858/http://www.icpsr.umich.edu/TIGER/index.html). There, users could download various versions of the U.S. Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) database. The TIGER/Line files do not include demographic data, but they do contain geographic information that can be linked to the Census Bureau’s demographic data. Due to file number limitations in openICPSR, the original data collections have been bundled into single zip packages. A single TIGER_directory.txt file listing the original files and the original directory structure is included with the root directory. Documentation files are also included as standalone subdirectories in each collection so users do not need to download entire zip bundles to view documentation. The TIGER/Line data are stored in compressed format in subdirectories by state name. There is one TIGER/Line file (in a compressed format) for each county or county equivalent. The file names consist of TGR + the 2-digit state FIPS (Federal Information Processing Standards) code + the 3-digit county FIPS code (i.e. TGR01031.ZIP for Coffee County, Alabama). Each state folder contains individual county files.The individual county files include one file for each record type included for that county with the following name convention: tgr01031.rt1. The convention follows the order described above with each file having a suffix which includes 'rt' (record type) followed by its designation (in this case record type 1). Each county file also contains its own metadata record.If present, documentation files for the TIGER/Line data are stored in a directory named '0docs' which is located in the 'Parent Directory'. This directory appears at the top of the index of state subdirectories for each edition of the TIGER/Line files. The documentation includes a complete list of FIPS state and county codes.

"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."

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

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

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Abstract (en): This poll, fielded December 10-13 2009, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President Barack Obama and his handling of the presidency, the federal budget deficit, health care, the situation in Afghanistan, unemployment, global warming, and the economy. Respondents were asked whether the Obama Administration or the Republicans in Congress could be trusted to do a better job handling the economy, health care reform, the situation in Afghanistan and energy policy. Several questions addressed health care including whether respondents supported the health care system being developed by Congress and the Obama Administration, whether they believed health care reform would increase the federal budget deficit, whether government should lower the age requirement for Medicare, and what the respondents' plan preference was for people who are not insured. Noneconomic questions focused on the role of the United States in Afghanistan, confidence in the Obama Administration in the handling of Afghanistan and the Taliban, and the environment. Other questions focused on the topics of health care in the United States, job availability, personal finances as well as opinions on professional golfer Tiger Woods. Demographic variables include sex, age, race, political political philosophy, party affiliation, education level, religious preference, household income, and whether respondents considered themselves to be a born-again Christian. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. The weights were derived using demographic information from the Census to adjust for sampling and nonsampling deviations from population values. Until 2008 ABC News used a cell-based weighting system in which respondents were classified into one of 48 or 32 cells (depending on sample size) based on their age, race, sex, and education; weights were assigned so the proportion in each cell matched the Census Bureau's most recent Current Population Survey. To achieve greater consistency and reduce the chance of large weights, ABC News in 2007 tested and evaluated iterative weighting, commonly known as raking or rim weighting, in which the sample is weighted sequentially to Census targets one variable at a time, continuing until the optimum distribution across variables (again, age, race, sex, and education) is achieved. ABC News adopted rim weighting in January 2008. Weights are capped at lows of 0.2 and highs of 6. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Households were selected by random-digit dialing. Within households, the respondent selected was the youngest adult living in the household who was home at the time of the interview. Please refer to the codebook documentation for more information on sampling. computer-assisted telephone interview (CATI)The data available for download are not weighted and users will need to weight the data prior to analysis.The variables PCTBLACK, PCTASIAN, PCTHISP, MSAFLAG, CSA, CBSA, METRODIV, NIELSMKT, BLOCKCNT, STATE, CONGDIST, and ZIP were converted from character variables to numeric.To preserve respondent confidentiality, codes for the variables FIPS (FIPS County) and ZIP (ZIP Code) have been replaced with blank codes.System-missing values were recoded to -1.The CASEID variable was created for use with online analysis.Several codes in the variable CBSA contain diacritical marks.Value labels for unknown codes were added in variables MSA, CSA, CBSA, COLLEDUC, and METRODIV. The data collection was produced by Taylor Nelson Sofres of Horsham, PA. Original reports using these data may be found via the ABC News Polling Unit Web site and via the Washington Post Opinion Surveys and Polls Web site.

Abstract (en): This dataset contains records for each public secondary and elementary education agency in the United States and its outlying areas (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) for 1985-1986. Reporting agencies serve instructional levels from pre-kindergarten through grade 12, or the equivalent span of instruction in ungraded or special education districts. Regional Educational Service Agencies, supervisory unions, and county superintendents are also represented. Variables include state and federal ID numbers, agency name, address, city, and ZIP code, FIPS county and out-of-state indicators, instructional operating status, agency type, grade span, metropolitan statistical area (MSA) ID and status, and board of control selection code. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in operation during 1985-1986 in the 50 states, the District of Columbia, and United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands). The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission and represents any location where fire fighters are stationed at or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments not having a permanent location are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Fire fighting training academies is also included.

Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results.

All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

The currentness of this dataset is indicated by the [CONTDATE] attribute.Homeland SecurityUse Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. An assessment of whether or not the total fire fighting capability in a given area is adequate.2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can determine those entities that are able to respond the quickest.3. A resource for Emergency Management planning purposes.4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster.5. A resource for situational awareness planning and response for Federal Government events.Attributes:NAME = The name of the facilityAREA_ = The area code of the phone number for the facilityPHONE = The phone number for the facility.ADDRESS = The address of the facilityADDRESS2 = Additional address information for the facilitySTATE = The state where the facility is locatedZIP = The zip code where the facility is locatedZIPP4 = The "plus 4" portion of the facility's ZIP codeCOUNTY = The county that the facility is located withinFIPS = The state and county FIPS code for the facilityDIRECTIONS = The directions to the facilityEMERGTITLE = The title of the emergency contact for the facilityEMERGPHONE = The phone number of the emergency contact for the facilityEMERGEXT = The phone extension of the emergency contact for the facilityCONTDATE = The date that the facility information was last updatedCONTHOW = The best way to contact the emergency contact for the facilityGEODATE = The date that the location was geocodedGEOHOW = The method used to located the facilityNAICSCODE = The NAICS code for the facilityNAICSDESCR = The description of the NAICS code for the facilityX = The longitude of the facilityY = The latitude of the facilityST_VENDOR = The vendor of the data used to geocode the facilityST_VERSION = The version of the data used to geocode the facilityGEOPREC = The geolocator precision for the facilitySTATE_ID = The state ID for the facilityEMS = Whether an emergency medical service is located at the facilitySource: Atlanta Regional CommissionDate: 2012 (DeKalb County updated May 2016)For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

Abstract (en): This special topic poll, fielded October 16-20, 2008, is part of a continuing series of monthly surveys that solicit public opinion on a range of political and social issues. The topic of this survey was government performance in the state of Maryland, slot machines, and the budget deficit. Residents of Maryland were asked about the job performance of Governor Martin O'Malley and whether they approved of the way he is handling his job as governor. Respondents identified the most important issues facing the state of Maryland, whether the state was moving in the right direction, and rated the condition of the state economy. Respondents were also asked what the chances were that they would vote in the upcoming presidential election. Several questions asked for respondents' opinions on Question Two on the state ballot: the constitutional amendment about slot machines in Maryland. Respondents were asked whether they approved of having slot machines in Maryland, what was the main reason they either approved or disapproved of slot machines, and if the slots plan passed, they thought it would help the state's budget situation. Respondents were queried on their thoughts of the direction of the nation's economy as well as their own family's financial situation. Respondents were asked about their impressions of the candidates for Maryland governor in 2010, and who they would vote for in the election. Demographic variables include sex, age, race, household income, education level, voter registration status, political party affiliation, political philosophy, religious preference, religiosity, union membership, whether respondent is a born-again Christian, and the presence of children under age 18 living at the residence. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. The data were weighted using demographic information from the Census to adjust for sampling and non-sampling deviations from population values. Respondents customarily were classified into one of 48 cells based on age, race, sex, and education. Weights were assigned so the proportion in each of these 48 cells matched the actual population proportion according to the Census Bureau's most recent Current Population Survey. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Persons aged 18 and over living in households with telephones in the state of Maryland. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was home at the time of the interview. Please refer to the codebook documentation for more information on sampling. 2010-11-09 Updated codebook. computer-assisted telephone interview (CATI)The data available for download are not weighted and users will need to weight the data prior to analysis.The variables PCTBLACK, PCTASIAN, PCTHISP, MSAFLAG, CSA, CBSA, METRODIV, NIELSMKT, BLOCKCNT, and ZIP were converted from character variables to numeric.To preserve respondent confidentiality, codes for the variables FIPS (FIPS County) and ZIP (ZIP Code) have been replaced with blank codes.System-missing values were recoded to -1.The CASEID variable was created for use with online analysis. The data collection was produced by Taylor Nelson Sofres of Horsham, PA. Original reports using these data may be found via the ABC News Polling Unit Web site and via the Washington Post Opinion Surveys and Polls Web site.

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