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.

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

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

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/

These datasets contain comprehensive information on current real estate listings in Washington, D.C., obtained from Zillow, and offer a detailed overview of the Washington, D.C. housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The full dataset includes all details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With over 5,000 current listings, this dataset is perfect for in-depth analysis of the Washington, D.C. housing market and the Washington, D.C. real estate scene. Potential applications include:

• Comparing listing prices and price per square foot across various neighborhoods and property types
• Mapping listings to visualize the spatial distribution of available inventory
• Analyzing the age of available housing stock using year-of-construction data
• Assessing typical HOA fees and property taxes for listings
• Identifying listings with desirable amenities
• Evaluating school quality near listings using GreatSchools ratings
• Contacting listing agents programmatically using the provided agent information

Whether you're a real estate professional, market analyst, data scientist, or simply interested in the Washington, D.C., housing market, this dataset offers a wealth of information to explore. You can begin investigating and discovering insights into Washington, D.C. real estate today.

Statewide, DCYF estimates that families of about 305,000 children age B-5 not yet in school need child care. Only about 34% of those children are enrolled in licensed child care or preschool. The percent of need met for child care and preschool for young children varies widely across the state. The maps below illustrate the estimated percent of child care need for these families currently met by licensed child care and preschool. To capture the variability of met need across the state, maps are available by County, Legislative District, and Zip Code

This is a zipcode-derived census dataset, designed for use with the Office of Public Defense web application.

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 for an adult to regularly eat organic food 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 regularly eat organic foodMarket Potential Index and count of adults expected to follow various dietary habitsEsri'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 Psychographics & Advertising Data Collection includes measurements of environmental concern, buying habits such as propensity to buy American products, likelihood to have healthy habits, and advertisement awareness. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product 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 map shows Place of Birth by Citizenship Status and Languages Spoken at Home, Zip Codes, and Dallas City Council Districts. This is shown by tract, county, and state boundaries using American Community Survey (ACS) 5-year estimates and margin of error data from Table B05002 and B16007. Current Vintage: 2017-2021ACS Table(s): B05002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey National Figures: data.census.govCurrent Vintage: 2017-2021ACS Table(s): B16007Data downloaded from: Census Bureau's API for American Community Survey National Figures: data.census.govData 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 is 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, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2021 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 RicoCensus tracts with no population that occur in areas of water, such as oceans, 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., -4444...) have been set to null, with the exception of -5555... which has been set to zero. 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.