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

The following crosswalks are the result of a data pipeline that pulls crosswalks from the U.S. Department of Housing and Urban Development (HUD) database, compiling a comprehensive ZIP --> FIPS crosswalk from 2010 to 2023. The crosswalks are available in four different forms: "one2one": one row, per ZIP code, per year. Each ZIP is matched to its best matching FIPS code. "one2few": Potentially multiple rows, per ZIP code, per year. All FIPS codes with a non-zero number of addresses for a given ZIP code are returned. "one2one_summy" and "one2few_summy" return the same respective types of data as the above, but summarize across chunks of years. Further description of these datasets, as well as code to reproduce and adjust results according to certain parameters, is available at the Github repo.

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

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.

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.

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

This dataset contains data about FIPS (Federal Information Processing Standards) codes for US states and counties. This dataset provides demographic data that could be necessary for evaluation and organization of medical services and population health evaluation.

Codes for the identification of the States, the District of Columbia, Puerto Rico, and the Insular Areas of the United States. Contains the 2-digit state FIPS code, postal abbreviation, and 8-digit Geographic Names Information Systems Identifier (GNISID)

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

🇺🇸 미국

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

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.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

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 data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

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

The Census Bureau maintains the ANSI (formerly FIPS) codes for Metropolitan and Micropolitan Statistical Areas.

Polygon file representing the ZIP code boundaries in Indianapolis and Marion County, Indiana.Data projection: NAD 1983 StatePlane Indiana East FIPS 1301 (US Feet)

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

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 Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the number of grandparents living with grandchildren and the number and percentage of grandparents responsible for grandchildren by Zip Code Tabulation Area in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

GrandWChild_e

# Grandparents living with grandchildren under age 18, 2017

GrandWChild_m

# Grandparents living with grandchildren under age 18, 2017 (MOE)

GrandRespChild_e

# Grandparents responsible for grandchildren under age 18, 2017

GrandRespChild_m

# Grandparents responsible for grandchildren under age 18, 2017 (MOE)

pGrandRespChild_e

% Grandparents responsible for grandchildren under age 18, 2017

pGrandRespChild_m

% Grandparents responsible for grandchildren under age 18, 2017 (MOE)

PopU18_e

# Population under age 18, 2017

PopU18_m

# Population under age 18, 2017 (MOE)

ChildrenByGrandHH_e

# Children raised by grandparent, 2017

ChildrenByGrandHH_m

# Children raised by grandparent, 2017 (MOE)

pChildrenByGrandHH_e

% Children raised by grandparent, 2017

pChildrenByGrandHH_m

% Children raised by grandparent, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.