This feature layer displays Affordable Connectivity Plan (ACP) eligibility data provided by USAC and the Census Bureau's American Community Survey 5 Year Tables for 2017-2021. The data is shown for 2020 U.S Census Boundaries (2022 Dataset) clipped to WA State.

Geospatial data about Pierce County, Washington Zip Codes. Export to CAD, GIS, PDF, CSV and access via API.

Map used by the Washington State Broadband Office to display enrollment criteria and internet subscription information for the Affordable Connectivity Program (ACP).

Map used by the Washington State Broadband Office to display internet subscriptions provided by the Affordable Connectivity Program (ACP).

This dataset is part of the Geographical repository maintained by Opendatasoft.This dataset contains data for zip codes 5 digits in United States of America.ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery.Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.Add administrative hierarchy.

This dataset delineates the boundaries of zip codes in the Bellevue region, assisting with geographic organization and location identification within the area.

This feature layer displays Affordable Connectivity Plan (ACP) Internet Subscription data provided by USAC and the Census Bureau's American Community Survey 5-Year Tables for 2017-2021. The data is shown for 2020 U.S Census Boundaries (2022 Dataset) clipped to WA State.

This feature layer displays Affordable Connectivity Plan (ACP) enrollment data provided by USAC and the Census Bureau's American Community Survey 5-Year Tables for 2017-2021. The data is shown for 2020 U.S Census Boundaries (2022 Dataset) clipped to WA State.

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

description: Health reporting area (HRA) and zip code-level indicators for monitoring the impact of the Affordable Care Act in King County, WA. Topic areas range from access to care to population health. Imported to Socrata to allow data to be pulled as JSON from SODA to feed into Leaflet.js-based maps on an external site.; abstract: Health reporting area (HRA) and zip code-level indicators for monitoring the impact of the Affordable Care Act in King County, WA. Topic areas range from access to care to population health. Imported to Socrata to allow data to be pulled as JSON from SODA to feed into Leaflet.js-based maps on an external site.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Boundaries (polygons) of NYS Assembly districts in New York State with name and contact info for each member of the NYS Assembly. Districts based on Legislative Task Force redistricting 2024. Information on representative based on assembly website as of 5-8-2025.Please contact Geospatial Services at nysgis@its.ny.gov if you have any questions.All district boundaries have been clipped to the NYS shoreline. This affects the following counties: Bronx, Cayuga, Chautauqua, Clinton, Erie, Essex, Franklin, Jefferson, Kings, Monroe, Nassau, New York, Niagara, Orleans, Oswego, Queens, Richmond, St. Lawrence, Suffolk, Washington, Wayne, Westchester.

Thurston County, WA, US Post Office assigned zip code areas. The purpose of this data is as a general reference only and actual mailing zip codes should be obtained from the US Postal Service. This layer was originally created using USPS TIGER Zip File 2007 tied to 2000 Census Data. It was updated in 2016 using a zip code layer created by TCOMM 911 based off of addressing points. Generally the boundaries between zip code areas have been moved to be coincidental with Thurston County Assessor’s Parcel boundaries. As parcels shift, due to correction, or are changed by subdivision, merge, BLA, etc. the boundaries may become disconnected. Periodically adjustments may be made to realign these boundaries with parcels however there is no regular maintenance plan in place. This layer should be used in conjunction with the Zip Code Points layer which is used to show zip codes associated with US Post Office Box Locations. For example in smaller jurisdictions, like Bucoda, that rely on a post office box rather than rural delivery, the zip code area will reflect the greater rural zip code boundaries and not the localized PO Box zip code. Updated by ECD on 10/10/2016

Thurston County, WA, US Post Office Box assigned zip codes. The purpose of this data is as a general reference only and actual mailing zip codes should be obtained from the US Postal Service. This layer was originally created using US Post Office Data. These points are associated with the US Post Office Box locations that house a unique Zip Code.This layer should be used in conjunction with the Zip Codes layer which is used to show zip code area boundaries associated with addressing and Thurston County Assessor’s Parcels. For example in smaller jurisdictions, like Bucoda, that rely on a post office box rather than rural delivery, the zip code point will reflect the unique zip code while the surrounding area will reflect the greater rural zip code boundaries and not the localized PO Box zip code.

These data represent Zip Codes in Vancouver, WA. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Zip Codes within the study area.

Census tracts with 4, 5, 6 and 10 tier classifications. We'll be adding 2020 data when its available from the USDA or the Census.From Asnake Hailu,The schemes shared in the RUCAGuide.pdf are DOH modified layers, prepared merely for epidemiological purposes [I.e., to delineate geography for a comprehensive epidemiologic assessment, describing rural-urban differences in demographics, health outcomes, risk factors, access to services, and the like.] Those are not as such rural/urban designation tools for census block areas, nor for any of the other geography categories. The files with the DOH modified layers are available at https://doh.wa.gov/public-health-healthcare-providers/rural-health/data-maps-and-other-resources under the sub-county level: Zip Code and Census Tract sub-heading.Please note: those files are essentially a decade old. We were anticipating to update our core products that are on our website, if and when the Federal Office of Rural Health and Policy (FORHP) produces a newer version of RUCA codes based on census 2020. The FORHP customarily contracts with a university for that task. We are three years away from 2020, except there is no update posted on the webpage I am familiar to get the original RUCA delineations. Here is a path where I go to check for the newer version: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/

This data shows the State EV Registration Data by ZIP Code. A snapshot of 1/27/2020, sourced from Atlas Public Policy in Washington, DC.

This map layer shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending for meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending for food by meal typeThis map shows Esri's 2016 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2016 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabulary

This layer shows the market potential that an adult has visited facebook.com in the last 30 days in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to visit FacebookMarket Potential Index and count of adults expected to visit various social media websitesMarket Potential Index and count of adults expected to visit various news websitesEsri's 2016 Market Potential (MPI) data measures the likely demand for a product or service in an area. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. An MPI compares the demand for a specific product or service in an area with the national demand for that product or service. The MPI values at the US level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the US average; an index of 80 implies that demand is 20 percent lower than the US average. See Market Potential database to view the methodology statement and complete variable list.Esri's Electronics & Internet Data Collection includes data that measures the likely demand for electronics and internet usage. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product, activity, or service. See the United States Data Browser to view complete variable lists for each Esri demographics collection.Additional Esri Resources:U.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

This layer shows the average amount spent on health insurance per household in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level: Average annual health insurance spending per householdBreakdown of average annual health insurance spendingBreakdown of Blue Cross/Blue Shield insurance spendingThis map shows Esri's 2016 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2016 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in categories including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabulary