This dataset, which represents county Federal Information Processing System (FIPS) codes for each county as a raster, is utilized by reVX to compute setbacks (distances). Setbacks can be computed either locally (on a per-county basis with specified distances or multipliers) or globally under a generic setback multiplier assumption applied to either the turbine tip height or the base setback distance. A County FIPS code is a five-digit numerical identifier that uniquely identifies counties and county equivalents in the United States The initial two digits represent the FIPS state code, while the final three digits signify the county's unique code within that state. For instance, 55025 corresponds to Dane County, Wisconsin. The first two digits - 55 - represent Wisconsin, and the last three digits - 025 - denote Dane County. Further information can be accessed at the "Federal Information Processing System (FIPS) Codes for States and Counties" resource below.

State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.

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

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).

This dataset was created to facilitate the conversion of Uniform Crime Reporting (UCR) Program state and county codes to Federal Information Processing Standards (FIPS) state and county codes. The four UCR agency-level data files archived at ICPSR in Uniform Crime Reporting Program Data: United States contain UCR state and county codes as geographic identifiers. Researchers who wish to use these data with other sources, such as Census data, may want to convert these UCR codes to FIPS codes in order to link the different data sources. This file was created to facilitate this linkage. It contains state abbreviations, UCR state and county codes, FIPS state and county codes, and county names for all counties present in the UCR data files since 1990. These same FIPS codes were used to create the UCR County-Level Detailed Arrest and Offense files from 1990-1996.

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.

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 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.

FIPS-Validated Encryption Modules Market Outlook

According to our latest research, the global FIPS-Validated Encryption Modules market size reached USD 2.13 billion in 2024, demonstrating robust momentum driven by increasing regulatory mandates and heightened awareness around data security. The market is projected to expand at a CAGR of 17.4% from 2025 to 2033, reaching a forecasted value of USD 7.19 billion by 2033. This growth is underpinned by the surging demand for secure cryptographic solutions across critical industries such as BFSI, government, and healthcare, as organizations strive to comply with stringent security standards and safeguard sensitive information against evolving cyber threats.

One of the most significant growth factors for the FIPS-Validated Encryption Modules market is the increasing regulatory emphasis on data protection and privacy. With the proliferation of digital transformation initiatives, organizations are handling vast volumes of sensitive data, making robust encryption solutions indispensable. The Federal Information Processing Standard (FIPS) validation, particularly FIPS 140-2 and the evolving FIPS 140-3, has become a benchmark for cryptographic modules in government and commercial sectors. This regulatory landscape compels enterprises to adopt FIPS-validated modules to ensure compliance, avoid hefty penalties, and maintain trust with stakeholders. As data breaches become more frequent and severe, the necessity for FIPS-validated solutions is no longer limited to government agencies but is rapidly permeating into the private sector, further fueling market expansion.

Technological advancements and the evolution of sophisticated cyber threats are also pivotal in driving the FIPS-Validated Encryption Modules market. Organizations are increasingly aware that traditional security measures are insufficient to protect against modern attack vectors, especially as IoT, cloud computing, and remote work environments become mainstream. FIPS-validated encryption modules offer a higher assurance of security, providing robust protection for data at rest, in transit, and during processing. Furthermore, the integration of artificial intelligence and machine learning with encryption technologies is enhancing the capability of these modules to detect anomalies and respond to threats in real-time. Vendors are also focusing on developing scalable, flexible, and interoperable modules, catering to diverse deployment scenarios and organizational requirements, thereby broadening the market’s addressable base.

Another major driver is the growing adoption of cloud services and the corresponding need for secure cloud environments. As businesses migrate their workloads to public, private, and hybrid clouds, the risk of data exposure and regulatory non-compliance increases. Cloud service providers and enterprises alike are deploying FIPS-validated encryption modules to secure their cloud infrastructure and ensure end-to-end data protection. The rise of cloud-native applications, coupled with the increasing prevalence of multi-cloud strategies, necessitates robust encryption mechanisms that are both standards-compliant and operationally seamless. This trend is particularly pronounced in sectors such as BFSI, healthcare, and government, where data sensitivity and regulatory scrutiny are exceptionally high.

From a regional perspective, North America maintains its dominance in the FIPS-Validated Encryption Modules market, driven by stringent regulatory frameworks, high cybersecurity awareness, and the presence of leading technology vendors. Europe follows closely, bolstered by the General Data Protection Regulation (GDPR) and similar directives that mandate rigorous data protection measures. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, increasing cyberattacks, and evolving regulatory standards in countries like China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, as governments and enterprises in these regions intensify their focus on cybersecurity infrastructure and compliance. This global landscape underscores the critical importance of FIPS-validated encryption modules across diverse regulatory and operational environments.

Component Analysis

The FIPS-Validated Encryption Modules market by component is segmented into hardware, software, and services, each playing a distinct role in the ecosy

Reference files for Federal Information Processing Series (FIPS) Geographic Codes. These FIPS Codes are a subset of a broader Population Estimates dataset. Archived from https://www.census.gov/geographies/reference-files/2023/demo/popest/2023-fips.html

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

Abstract ======== This data set consists of the MESSENGER Energetic Particle and Plasma Spectrometer (EPPS) calibrated observations, also known as DDRs. The system encompasses 2 instrument subsystems - the Energetic Particle Spectrometer (EPS) and the Fast Imaging Plasma Spectrometer (FIPS). This data set contains FIPS instrument data. FIPS covers the energy/charge range of < 46 eV/q to 13 keV/q. There are eight FIPS DDR data products (one retired).

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)

Matched Continuum of Care (CoC) to FIPS codes for all principal cities from 2019-2022. FIPS data from Census designations and CoC data from HUD Point in Time and CoC designations (see sheet 3 for complete data sources and links).

A list of Connecticut municipalities with the 3-digit tax code and the 2010 10-digit FIPS code for county subdivisions, assigned by the U.S. Census Bureau

LocalView is a database co-created by Soubhik Barari and Tyler Simko to advance the study of local government in the United States. It is the largest existing dataset of local government public meetings— the central policy-making process in American local government — as they are captured on video. To get started, select the file(s) that you'd like for your use case based on the year that the meeting took place. Note: we are no longer supporting file formats other than .parquet for space considerations. For potential use cases or further guidance on downloading the data in bulk, visit the companion website: localview.net. Change Log Scraping, parsing, identifying, and merging together meetings involves a large number of non-trivial decisions, many of which need to be adjusted over time particularly as the YouTube API changes. As such, when such decisions notably deviate from process or the outputs documented in the first version of this database, it will be logged here. Version 2.0 (2023-10) Data updated up until September 2023. ~10,000 new videos added, all belonging to existing channels in database. Change in data format: channelType column changed to channel_type. ST_FIPS correctly padded to be 7 characters (2 digit state code + 5 digit place FIPS code). videos with no caption available from YouTube are explicitly marked as “” in caption_text. caption_text_cleaned is actually consistently cleaned (previously stray timestamps/pause markers in some entries). acs_2018_* columns now prefixed as acs_18. additional ACS variables now available for each place: acs_18_median_gross_rent: Median gross rent in FIPS place. acs_18_median_hh_inc: Median household income in FIPS place. acs_18_median_age: Median age in FIPS place. acs_18_amind: American Indian population in FIPS place. acs_18_asian: Asian population in FIPS place. acs_18_nhapi: Native Hawaiian or Pacific Islander population in FIPS place. census_2015_* columns removed for redundancy. to avoid confusion and possible conflicts, .json and .csv file formats eliminated in favor of .parquet format. municipal voteshare (_dem2pv) variables have been removed from the public use files for a number of reasons: (1) high degree of missingness, (2) no columns estimates available, (3) potential sensitivity The matching process of videos to ST-FIPS and government types is as follows: videos are matched to channels’ previous ST-FIPS codes and/or government types if there is an umambiguous match, otherwise a (1) series of regex matches with video title/description are used to attempt to match video to the government and (2) state/place names are extracted from each video’s title/description/caption and used to match to an ST-FIPS entity if an unambiguous match; only identified videos are then uploaded to the database. to identify the date of the meeting that the video captures, we first try to extract the date from the title, otherwise we try to extract the date from the description, otherwise it is discarded. Version 1.0 (2023-02) See publication for full details on methodology choices for the Version 1.0 database.

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 reference table contains data elements for the 58 Counties in California that can be used to join to other data sets. This data includes the following fields:DHCS County CodeCounty NameCounty Region CodeCounty Region DescriptionFIPS County Code (xxx)FIPS State Code + FIPS County Code (06xxx)North/South Indicator

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Shortgrass Steppe (SGS) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Coweeta (CWT) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.