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.

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

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

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.

"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."

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

Operational Mineral Resource Mines

Operational mineral resource mines with location and contact details

By Homeland Infrastructure Foundation [source]

About this dataset

The Mines and Mineral Resources dataset provides comprehensive information on operational mines for mineral resources, excluding sand and gravel quarries. It includes detailed data on the location, contact details, and specific characteristics of these mines.

One key aspect of this dataset is that it focuses exclusively on actively operating mines. This ensures that the information is up-to-date and relevant for various stakeholders, including first responders and law enforcement teams.

To facilitate easy access to these mines, each mine's location point has been strategically placed at the intersection between the main haul road within the mine site and the nearest public road. This point serves as a starting point from which it should be straightforward to navigate to the actual mining areas or pits.

The dataset covers an extensive range of attributes related to each mine. These include detailed descriptions of the industry classification using both NAICS (North American Industry Classification System) codes and SIC (Standard Industrial Classification) codes. Additionally, information on security classification highlights any potential security considerations associated with each mine.

Contact details for each mine are provided to ensure efficient communication in case of emergencies or inquiries. These include emergency contact phone numbers along with extension numbers, titles of emergency contact persons, vendor responsible for providing data services related to a particular mine, inspecting officer's name, as well as general phone numbers for contacting the respective mining companies or plants.

Address information comprises complete addresses including additional address details when applicable such as suite or floor numbers. City names are specified along with counties where these mines are located in order to identify their geographical locations more precisely. ZIP codes provide essential postal code references while FIPS (Federal Information Processing Standards) codes offer unique identifiers for each county.

Directions from nearby public roads guide individuals towards accessing specific mines efficiently. This ensures that first responders or visitors can easily reach their intended destinations within these expansive mining areas.

The dataset also includes valuable geospatial data such as latitude (Y) and longitude (X) coordinates, enabling accurate positioning of each mine on a map. The precision level of these geospatial data points is provided to understand the accuracy and reliability of this location information.

Thematic categorization based on the Homeland Security Infrastructure Program offers insights into the specific industry focus or purpose of each mine. This classification allows for better understanding and organization within the dataset.

Additionally, quality control and quality assurance information are provided to convey the reliability and validity of the data. This includes details on how geospatial data was determined (geohow) and recorded dates for both geospatial (geodate) and general contact information (contdate).

Overall,

How to use the dataset

• Understand the Purpose:

  • This dataset includes information on mines believed to be operational, allowing first responders or law enforcement teams to easily locate and access these mines in case of emergencies or other purposes.
  • The dataset focuses on mines related to mineral resources, excluding sand and gravel quarries.

• Column Descriptions:

  • FEATTYPE: The type of feature represented by the data point.
  • SECCLASS: The security classification of the mine.
  • NAME: The name of the mine.
  • AREA_: The area covered by the mine.
  • PHONE: The contact phone number for the mine.
  • ADDRESS: The address of the mine.
  • ADDRESS2: Additional address information for the mine.
  • CITY: The city where the mine is located.
  • STATE: The state where the mine is located.
  • ZIP/ZIPP4: The ZIP code/ZIP+4 code of the mine's location COUNTY and FIPS codes provide county-related information for better understanding DIRECTIONS can be useful for reaching a particular destination EMERGTITLE represents emergency contact person's title EMERGPHONE provides emergency contact phone number along with extension (EMERGEXT) CONTHOW specifies how to contact or communicate with a particular entity associated with a specific column value (e.g., MINE_TYPE) &g...

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.