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.

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

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

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.

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

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

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

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.

explain the relationship between DMA, FIPS and county

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

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

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

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

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

Used to uniquely identify geographic areas like states and counties.

It consists of:

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

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

Together: 06037 (California + Los Angeles County)

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

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

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

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

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

One DMA contains multiple counties.

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

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

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

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

👉 DMA for FIPS 02013?

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

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

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."

Spatial Mapping of HITECH Grants

Visualizing Health and Community Grant Locations in the U.S

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides crucial geographic data related to two of the U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. As part of the American Recovery and Reinvestment Act (ARRA), these grants were made available to provide citizens with access to health IT infrastructure investments throughout diverse communities across the United States. This crosswalk is an essential resource for anyone looking to link regional, state, county and zip code level program financials with performance metrics for visualization or comparison. With detailed information about region counties, codes, states, FIPS codes and ZIP codes associated with local HITECH grantees, this data presentation helps shed light on a financially impactful initiative from our federal government that can drastically improve healthcare delivery in thousands of cities nationwide!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides geographic data for the service areas of two of the HITECH programs, the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This can be used to map and visualize data related to those programs. Here is a guide on how to use this dataset:

• Get familiar with key columns: Familiarize yourself with the columns included in this dataset, including column names and descriptions for each column such as region, region_code, county_name, state_fips, county_fips and zip.
• Review data formats: If there are any discrepancies between your current format of data presented in this dataset versus what you may have currently in your system or within other sources of information - make sure to review those discrepancies prior exploring more from here onwards.
• Understand regional coverage: Refine the analysis by filtering out different grantee located based on specific regions or states - use necessary filters such as Region code or County FIPs code that will give you an easier view on which region/county grantee has been provided funding through these HHS programs as part of Hitech Act program distribution.
• Map & Visualize grantees: We can visualise geographically where are REC-Program & Beacon Communities Grants distributed across US by making a heatmap while taking desired geolocation coordinates like zip codes; query all available details under columns we need like zip codes along their respective countyp location & state value so that grants can be highlighted after it renders practical Map visuals for us giving an ease if further status / details required about entities who had taken these grants within certain area / regions!

Research Ideas

• Creating an interactive map to visualize grant program performance by region and county.
• Using the data to create a color-coded scatterplot graphic to show active grant program sites in the US.
• Generating reports on HITECH Grantee performance over time, grouped by geographic area or region

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: healthit-dashboard-areatype-crosswalk-csv-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | region | The region of the grantee. (String) | | region_code | The code for the region of the grantee. (String) | | county_name | The name of county where the grantee is located. (String) | | state_fips | The Federal Information Processing Standard (FIPS) code for knowledge of which state it is located in. (String) | | county_fips | The Federa...

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

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

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

NOTES:

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lake St. Croix Beach was incorrect. It was changed from 2394379 to 2395599 to match GNIS.

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lilydale was incorrect. It was changed from 2394457 to 2395708 to match GNIS.

- On 11/9/2010 it was discovered that the CTU ID used for the City of Crystal was incorrect. It was changed from 2393541 to 2393683 to match GNIS.

- Effective April 2008, a change was made in GNIS to match the FIPS place codes to the "civil" feature for each city instead of the "populated place" feature. Both cities and townships are now "civil" features within GNIS. This means that the official GNIS unique ID for every city in Minnesota has changed.

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

- Cities and townships have also been referred to as ''MCDs'' (a census term), however this term technically refers to the part of each city or township within a single county. Thus, a few cities in the metro area that are split by county boundaries are actually comprised of two different MCDs. This was part of the impetus for a proposed MN state data standard that uses the ''CTU'' terminology for clarity.

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

- A variety of civil divisions of the land exist within the United States. In Minnesota, only three types exist - cities, townships and unorganized territories. All three of these exist within the Twin Cities seven county area. The only unorganized territory is Fort Snelling (a large portion of which is occupied by the MSP International Airport).

- Some cities are split between two counties. Only those parts of cities within the 7-county area are included.

- Prior to the 2000 census, the FIPS Place code for the City of Greenwood in Hennepin County was changed from 25928 to 25918. This dataset reflects that change.

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.

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.