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

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

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

Data Sources

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

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

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

Data Dictionary

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

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

FIPS Class Code Details Source Copied 7/29/17

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

Acknowlegement

Foto von Annie Spratt auf Unsplash

Spreadsheet of all U.S. ZIP codes by county. Map ZIP to county and county to ZIP with a ZIP code to county FIPS code crosswalk. Bulk lookup county by ZIP 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

US Geographic Codes Dataset

This code is used to generate a combined data set of US ZIP, FIPS, and County data for most ZIP Codes in the U.S. (41,867 to be exact).

Code to generate the data set from the government files listed below can be found here.

The Data

The dataset is organized as follows:

• Zip Code: USPS ZIP code from here
• State Name: Full state name (E.g. Michigan)
• State Abrv: USPS abbreviated state name (E.g: MI)
• State Code: FIPS State Code from here
• County Name: County in which ZIP is located
• County Code: FIPS County Code
• FIPS Code: FIPS State Code + FIPS County Code from here
• ANSI Code: American National Standards Institute Code
• Centroid Lat: Latitude value of the county center
• Centroid Long: Longitude value of the county center

Sources

The data used to create this data set was taken from several recent government data sets.

These are:

• 2017 US Gazetteer FIPS Dataset
• 2017 US Census County Polygons Dataset
• 2015 Department of Labor GPCI Dataset
• General Census State Code Reference Document

Disclaimers

The final csv is in 'latin1' encoding to preserve the Spanish county names in Puerto Rico.

This data is from, and shall remain in the public domain, and the onus of responsibility lies with the user of this data.

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.

Spreadsheet of all U.S. ZIP Codes to Town, State, County, etc. Map ZIP codes to all overlapping places in a spreadsheet with ZIP codes to FIPS codes crosswalk.

Look up FIPS code lists from city and county FIPS codes to state FIPS codes, map ZIP codes to FIPS codes and CBSA codes. Free FIPS code lookup in a spreadsheet.

US Zip Codes with the following columns:

Name - Description

ZIP - 5-digit ZIP Code

County - County containing the ZIP

State - State containing the ZIP

CBSA - Core Based Statistical Area

FIPS County - Federal Information Processing Standard County Code

FIPS State - Federal Information Processing Standard State Code

ZIP codes which are named also have these columns populated:

Name - Name of the place corresponding to the ZIP Code

Population - Population of the ZIP Code

Housing Units - Number of Housing Units in the ZIP Code

Total Area - Total Area of the ZIP Code (Square Meters)

Land Area - Land Area of the ZIP Code (Square Meters)

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.

This data collection relates ZIP codes to counties, to standard metropolitan statistical areas (SMSAs), and, in New England, to minor civil divisions (MCDs). The relationships between ZIP codes and other geographical units are based on 1979 boundaries, and changes since that time are not reflected. The Census Bureau used various sources to determine ZIP code-county or ZIP code-MCD relationships. In the cases where the sources were confusing or contradictory as to the geographical boundaries of a ZIP code, multiple ZIP-code records (each representing the territory contained in that ZIP-code area) were included in the data file. As a result, the file tends to overstate the ZIP code-county or ZIP code-MCD crossovers. The file is organized by ZIP code and is a byproduct of data used to administer the 1980 Census. Variables include ZIP codes, post office names, FIPS state and county codes, county or MCD names, and SMSA codes.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

This layer provides zip code data for view and analysis in GIS and interactive web mapping applications.

These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The following fields are in the overall risk datasets:

Attribute

Description

county_id

The county FIPS code

count

The count (#) of infrastructure facilities

flood_score

A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

The following fields are in the category risk datasets:

Attribute

Description

FIPS

County FIPS code

ZIP_CODE

ZIP code

count

The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

flood_score

A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

risk_direction

A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

infrastructure_category_id

1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties

This data collection relates ZIP codes to counties, to standard metropolitan statistical areas (SMSAs), and, in New England, to minor civil divisions (MCDs). The relationships between ZIP codes and other geographical units are based on 1979 boundaries, and changes since that time are not reflected. The Census Bureau used various sources to determine ZIP code-county or ZIP code-MCD relationships. In the cases where the sources were confusing or contradictory as to the geographical boundaries of a ZIP code, multiple ZIP-code records (each representing the territory contained in that ZIP-code area) were included in the data file. As a result, the file tends to overstate the ZIP code-county or ZIP code-MCD crossovers. The file is organized by ZIP code and is a byproduct of data used to administer the 1980 Census. Variables include ZIP codes, post office names, FIPS state and county codes, county or MCD names, and SMSA codes. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08051.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

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.

blockgrouphomevalues # Context A home purchase is among the most import decisions, and potentially risk investments, in a person's life. Their choice can reflect interest in long term gains, housing costs and, in the U.S., part of the American Dream. Analytics of home values and rental costs, however, are commonly limited to highest level geographic aggregates and broad, even annual, periods of time. This publication produces a data file shared in the Block Groups Datasets dataverse hosted on https://dataverse.harvard.edu/dataverse/blockgroupdatasets. The data is shared under a Common Commons, open source license, without warranties, share alike, non commercial and by attribution. Method This publication attempts to cast home values down to U.S. Census block group geographies, by inheriting and averaging the measures from ZIP code level estimates. On the whole, block groups with a few hundred households are considerably smaller than ZIP code areas with several thousand. In addition, the two geographies are managed by separate Federal agencies, the U.S. Postal Service and the Census Bureau, so they are inherently dissimilar. The simplest method of projection involves overlaying the two geographies, having a block group inherit the estimates of the ZIP code level that covers it. When the block group spans ZIP code boundaries, an average is appropriate, weighted by land area lying in each parent. Data Zillow is recognized as an innovator in predicting home values, serving real estate agents, home buyers, and home sellers. Their research service publishes several estimates at a ZIP code level including measures of home value (Zillow Home Value Index ZHVI) and rental costs (Zillow Observed Rent Index ZORI). The ZHVI is broken down by housing type: single family homes and condominiums. And, each of their publications has monthly frequency dating, in some cases, to 2000. Block group geographic boundariess are maintained by the US Census' TIGER (Topologically Integrated Geographic Encoding and Referencing) publication. ZIP code boundaries are not generally published, but shared from a private company, Dotlas, in various retail marketing solutions. ZIP codes, also, have long been problematic for demographic analytics. Their boundaries span counties and states, so you cannot tiethem to familar geographies including Census tracts and block groups. The Census Bureau tries to address this by using ZIP Code Tabulation Areas (ZCTAs). These are coded very much like 5 digit ZIP codes and are equal to them most of the time. When A ZIP code geography crosses a county line, though, new ZCTAs are invented to represent each side of the split area. So, while ZIP codes cannot be aggregated, ZCTAs can total into counties, states, divisions and regions. The blockgrouphomevalues dataset offers the following columns: Column Data Type Description STATEFP string The 2-digit State FIPS code of the block group COUNTYFP string The 3-digit County FIPS code of the block group TRACTCE string The 6-digit Census Tract of the block group BLKGRPCE string The 1-digit Block Group of the block group GEOID string 12 digit concatenation of State, County, Tract and Block Group codes GEOIDFQ string The 'fully qualified' GEOID with US country prefix ALAND integer The land area if the block group in square meters AWATER integer The area if the block group, covered by water, in square meters INTPTLAT float Latitude of the block groups centroid point INTPTLON float Longitude of the block groups centroid point ZIP Codes Overlaying list List of the ZIP codes that overlay the block group ZHVI All Housing Types float Zillow Home Value Index, attributed to the block group, all housing types ZHVI Single Family Homes float Zillow Home Value Index, attributed to the block group, single family homes ZHVI Condos/Coops float Zillow Home Value Index, attributed to the block group, condominiums and cooperatively owned ZORI All Housing Types float Zillow Observed Rent Index, attributed to the block group Additional Notes When the Block Group Code BLKGRPCE is '0', that block group is under water. Block groups cover the Great Lakes, for example, making a confusing visual for chloropleth maps. To support visualization, the code also uses Census definitions of cities called Combined Statitical Areas, which group counties together. The CSA for New York includes 22 counties, distinguished as Central or Outlying. The Delineation Files publication includes the geographic IDs of state and county FIPS codes in each major city. Maps of these results may be visually biased. New York City and San Francisco Bay areas have extreme housing values, but they have small land areas. Denver by contrast has higher then median housing values with very large land areas. As a result, western Colorado looks like the dominating location of home values. When more than one ZIP code overlays a block group, values are attributed by the shared land area. This assumes that housing is uniform over...

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

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

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.