Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.Note: A new version of this data was released November 22, 2022 and is available here. There are significant changes, see the Justice40 Initiative criteria for details.This layer assesses and identifies communities that are disadvantaged according to Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the Version 0.1 criteria are shaded in a semi-transparent blue to work with a variety of basemaps.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 0.1 of the source data downloaded May 30, 2022.Use this layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.From the source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40The layer has some transparency applied to allow it to work sufficiently well on top of many basemaps. For optimum map display where streets and labels are clearly shown on top of this layer, try one of the Human Geography basemaps and set transparency to 0%, as is done in this example web map.Browse the DataView the Data tab in the top right of this page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.

This hosted feature layer has been published in RI State Plane Feet NAD 83.The Land Use 2025 dataset was developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. It evolved from a GIS overlay analysis of land suitability and availability and scenario planning for future growth. The analysis focused on the 37% of the State identified as undeveloped and unprotected in a land cover analysis from RIGIS 1995 land use land cover data. The project studied areas for suitability for conservation and development, based on the location of key natural resources and public infrastructure. The results identified areas with future use potential, under three categories of development intensity and two categories of conservation.These data are presented in the Plan as Figure 121-02-(01), Future Land Use Map. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The intent of the Plan is to bring together the elements of the State Guide Plan such as natural resources, economic development, housing and transportation to guide conservation and land development in the State. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas.These data have several purposes and applications: They are intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, these data are a guide to assist the state and communities in making land use policies. It is important to note these data are a generalized portrayal of state land use policy. These are not a statewide zoning data. Zoning matters and individual land use decisions are the prerogative of local governments. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. It depicts where different intensities of development (e.g. parks, urban development, non-urban development) should occur by color. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the USB, most land is served by public water service; many areas also have public sewer service, as well as, public transit. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts.NOTE: These data will be updated when the associated plan is updated or upon an amendment approved by the State Planning Council. NOTE: Wetlands were not categorized within the Land Use 2025 dataset.When using this dataset, the RIGIS wetlands dataset should be overlaid as a mask. Full descriptions of the categories and intended uses can be found within Section 2-4, Future Land Use Patterns, Categories, and Intended Uses, of the Plan. https://www.planning.ri.gov/documents/guide_plan/landuse2025.pdf

Census Blocks in Macon-Bibb County.

A census block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data (data collected from all houses, rather than a sample of houses). The number of blocks in the United States, including Puerto Rico, for the 2010 Census was 11,155,486.[1]

Census blocks are grouped into block groups, which are grouped into census tracts. There are on average about 39 blocks per block group. Blocks typically have a four-digit number; the first number indicates which block group the block is in. For example, census block 3019 would be in block group 3.

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 population of a census block varies greatly. As of the 2010 census, there were 4,871,270 blocks with a reported population of zero,[2] while a block that is entirely occupied by an apartment complex might have several hundred inhabitants.

Census blocks covering the entire country were introduced with the 1990 census. Before that, back to the 1940 census, only selected areas were divided into blocks.

To review a table detailing Census Block information in the United States visit https://www.census.gov/geo/maps-data/data/tallies/tractblock.

This data layer is an element of the Oregon GIS Framework. This theme delineates urban growth boundaries (UGBs) in the state of Oregon. The line work was created by various sources including the Oregon Department of Land Conservation and Development (DLCD), the Oregon Department of Transportation (ODOT), Metro Regional Council of Governments (Metro), county and city GIS departments, and the Oregon Department of Administrative Services - Geospatial Enterprise Office (DAS-GEO). UGB areas consist of unincorporated lands surrounding a city that show where the city plans to grow over the next 20 years. When a city needs to develop more residential, commercial, industrial, or public land, it annexes the needed area from its UGB. If a city runs out of needed land within the UGB, it can expand its UGB. Original UGBs were established under the Oregon Statewide Planning Goals in 1973 by the Oregon State Legislature (Senate Bill 100). Goal 14 of the statewide planning program is, "To provide for an orderly and efficient transition from rural to urban land use, to accommodate urban population and urban employment inside urban growth boundaries, to ensure efficient use of land, and to provide for livable communities." The process and requirements for designating and amending UGBs are in Oregon Administrative Rules, Chapter 660, Division 24 (OAR 660-024). Designating or amending a UGB requires a public process, as required by Planning Goal 1, followed by approval by both the city and county elected officials and acknowledgement by the DLCD. This process includes the city submitting a Post Acknowledgement Plan Amendment (PAPA) to DLCD to review for consistency with Goal 14. The PAPA submittal includes GIS files that delineate the changes to the UGB. DLCD aggregates the local GIS layers into the statewide UGB layer. UGB line work and attributes are verified with the city PAPA submittals entered in DLCD’s tabular database to ensure that all UGB updates reported to DLCD have been included in this dataset. UGBs that are currently in the appeal process at the time of publication of this layer are not included. The effDate attribute indicates the year in which the UGB amendment was acknowledged by DLCD. In 2022, DLCD acknowledged amendments to the following UGBs: Central Point, Dayton, Phoenix, and Turner. Corrections were also made to the Astoria and Condon UGBs to reflect the current acknowledged boundary.

The Road Segment table describes the administration and ownership of the segment of road. It contains tabular polyline data showing the log miles/measures, road name, functional class, government control, and U.S. Routes. Road names are derived from visual surveys by field crew or official GIS maps. Functional class is set by the Federal Highway Administration (FHWA). All other categories are determined by state and local agencies. This dataset is updated weekly. County – County in Tennessee where associated features and attributes are located.Route Number – Route in Tennessee with corresponding attributes.Special Case – Route designator for non-standard routes such as By-Pass.00 None01 Spur - S02 Alternate - A03 State Connector - C04 Bypass - BP05 Business Route - BR06 Northbound - N07 Southbound - S08 Eastbound - E09 Westbound - WCounty Sequence – This number indicates the sequential number of times a route enters and leaves the county, begins with zero (0).Beginning Log Mile (BLM) – The beginning log mile (measure) for the route segment.Ending Log Mile (ELM) - The ending log mile (measure) for the route segment.Functional Classification – These codes, set by the FHWA, provide a statewide highway functional classification in rural and urban areas to determine functional usage of the existing roads and streets.01 Rural Interstate02 Rural Other Principal Arterial03 Rural Freeway or Expressway06 Rural Minor Arterial07 Rural Major Collector08 Rural Minor Collector09 Rural Local11 Urban Interstate12 Urban Freeway or Expressway14 Urban Other Principal Arterial16 Urban Minor Arterial17 Urban Collector19 Urban LocalGovernment Control – These codes determine ownership and maintenance responsibility.01 State Highway Agency02 County04 Municipal11 State Park12 Local Park21 Other State Agency25 Other Local Agency26 Private27 Railroad40 Other Public60 Other Federal Agency63 US Fish and Wildlife64 US Forest Service66 National Park Service67 TVA68 Bureau of Land Management70 Corps of Engineers (Civil)72 Air Force73 Navy or Marines74 Army80 OtherUS Route Number – US Route Number assigned to roadway segment.

This map shows the historical housing unit change in consistent 2010 census tract boundaries from 1940 to 2019. In many cities over that time period—especially in the 1950s and 1960s—federal, state, and local governments demolished thousands of housing units as part of their "urban renewal" programs. These neighborhoods were typically in the older parts of city centers, contained lower income populations, and had higher shares of Black, Hispanic, and immigrant residents than their respective cities. Homes were typically replaced with new interstate highways and thoroughfares, stadiums, civic buildings, parking lots, high rises, rights of way, and other non-residential uses. In a fraction of cases, homes were replaced with public housing. Many of these areas show up as red on this map because they still have not regained the level of housing they had before World War II.Urban renewal is not the only reason for housing loss. Many tracts in places that have been undergoing population decline—especially cities in the North and Midwest and many rural communities—have also lost considerable amounts of housing over this time period.On the other side of things, many suburban and exurban areas—especially in the South and West—have experienced significant population and housing unit growth. These places show up as blue on this map.The data used to make this map comes from the Historical Housing Unit and Urbanization Database 2010, or HHUUD10. To read more on the methodologies used to estimate the housing unit counts, please refer to the methods paper. To download the data in tabular form, please visit the data repository. To download the feature layer used to make this map or read about the attributes, see the feature layer. Please also remember that these data are estimates and are therefore imperfect. They should be treated like all interpolated data: with caution and a healthy dose of skepticism.Citation:Markley, S.N., Holloway, S.R., Hafley, T.J., Hauer, M.E. 2022. Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940–2019. Scientific Data 9 (82). https://doi.org/10.1038/s41597-022-01184-x

Abstract

The 2022 Bangladesh Demographic and Health Survey (2022 BDHS) is the ninth national survey to report on the demographic and health conditions of women and their families in Bangladesh. The survey was conducted under the authority of the National Institute of Population Research and Training (NIPORT), Medical Education and Family Welfare Division, Ministry of Health and Family Welfare (MOHFW), Government of Bangladesh.

The primary objective of the 2022 BDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the BDHS collected information on: • Fertility and childhood mortality levels • Fertility preferences • Awareness, approval, and use of family planning methods • Maternal and child health, including breastfeeding practices • Nutrition levels • Newborn care

The information collected through the 2022 BDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the population of Bangladesh. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Bangladesh.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2022 BDHS is the Integrated Multi-Purpose Sampling Master Sample, selected from a complete list of enumeration areas (EAs) covering the whole country. It was prepared by the Bangladesh Bureau of Statistics (BBS) for the 2011 population census of the People’s Republic of Bangladesh. The sampling frame contains information on EA location, type of residence (city corporation, other than city corporation, or rural), and the estimated number of residential households. A sketch map that delineates geographic boundaries is available for each EA.

Bangladesh contains eight administrative divisions: Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. Each division is divided into zilas and each zila into upazilas. Each urban area in an upazila is divided into wards, which are further subdivided into mohallas. A rural area in an upazila is divided into union parishads (UPs) and, within UPs, into mouzas. These administrative divisions allow the country to be separated into rural and urban areas.

The survey is based on a two-stage stratified sample of households. In the first stage, 675 EAs (237 in urban areas and 438 in rural areas) were selected with probability proportional to EA size. The BBS drew the sample in the first stage following specifications provided by ICF. A complete household listing operation was then carried out by Mitra and Associates in all selected EAs to provide a sampling frame for the second-stage selection of households.

In the second stage of sampling, a systematic sample of an average of 45 households per EA was selected to provide statistically reliable estimates of key demographic and health variables for urban and rural areas separately and for each of the eight divisions in Bangladesh.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four types of questionnaires were used for the 2022 BDHS: the Household Questionnaire, the Woman’s Questionnaire (completed by ever-married women age 15–49), the Biomarker Questionnaire, and two verbal autopsy questionnaires. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect population and health issues relevant to Bangladesh. In addition, a selfadministered Fieldworker Questionnaire collected information about the survey’s fieldworkers. The questionnaires were adapted for use in Bangladesh after a series of meetings with a Technical Working Group (TWG). The questionnaires were developed in English and then translated to and printed in Bangla.

Cleaning operations

The survey data were collected using tablet PCs running Windows 10.1 and Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A. The Bangla language questionnaire was used for collecting data via computer-assisted personal interviewing (CAPI). The CAPI program accepted only valid responses, automatically performed checks on ranges of values, skipped to the appropriate question based on the responses given, and checked the consistency of the data collected. Answers to the survey questions were entered into the PC tablets by each interviewer. Supervisors downloaded interview data to their computer, checked the data for completeness, and monitored fieldwork progress

Each day, after completion of interviews, field supervisors submitted data to the servers. Data were sent to the central office via the internet or other modes of telecommunication allowing electronic transfer of files. The data processing manager monitored the quality of the data received and downloaded completed files into the system. ICF provided the CSPro software for data processing and offered technical assistance in preparation of the data editing programs. Secondary editing was conducted simultaneously with data collection. All technical support for data processing and use of PC tablets was provided by ICF.

The 2013 land use captures how the landscape in NSW is being used for food production, forestry, nature conservation, infrastructure and urban development. It can also be used to monitor changes in the landscape and identify resulting impacts on biodiversity values and individual ecosystems.

The state wide land use data is a compilation of the best available land use information for NSW that was available in August 2017.

Land use and tenure datasets from a variety of projects have been compiled, these include;

• Landuse mapping 2017 – 1:10,000 scale mapping using 2013 SPOT 5 satellite imagery and ADS digital aerial imagery. Covering rural zoned, agricultural areas. These areas are applicable to Local Land Services Act 2016.

• NSW Horticulture mapping project – Fine scale mapping (1:5,000) mapping of horticulture production to the commodity level (where applicable), on the mid-north and north coast of NSW. This was in response to the outbreak of Panama disease, Tropical Race 4, found in a banana plantation in Northern Queensland. It is part of a collaborative mapping project to improve Biosecurity information between state jurisdictions and the commonwealth. It also covers plantation forestry for the project area.

• Hunter 2013 mapping – 1:10,000 scale mapping covering most of the Hunter catchment based on 2013 SPOT5 imagery. It was part of a pilot land use update trial for intended use in Department of Planning Regional Growth Plans. This is an update of the 2005 Land use Hunter catchment and 2008 Upper Hunter catchment land use mapping projects, also available on SDE and P drive.

• National Parks and State Forest Tenure mapping 2017

• Sydney Map sheet 2012 – Detailed urban mapping (1:10,000 scale) trial using high resolution Digital aerial imagery

• Existing Land use information (circa 2003) has been used for non-rural zoned areas and not covered by the other projects mentioned above. This component of the 2013 land use has a reliability scale of 1:25,000. The areas where the circa 2003 land use product used include; urban, industrial, commercial and environmental local government LEP (Local Environment Planning) zones. It also includes areas of metropolitan Sydney that are excluded from the LLS Act 2016.

Land use information has been captured in accordance with standards set by the Australian Collaborative Land Use Mapping Program (ACLUMP) and using the Australian Land Use and Management ALUM Classification. The ALUM classification is based upon the modified Baxter & Russell classification and presented according to the specifications contained in http://www.agriculture.gov.au/abares/aclump/land-use/alum-classification.

The 2013 land use, that was mapped for the purposes of the Local Land Services Act, was commenced in July 2016 and completed by August 2017. It covers all rural zoned (agricultural) areas, and was based on most appropriate SPOT5 imagery to represent land use at June 2013. It includes values in the attribute fields of source, source date, source scale, reliability and land use mapping (currency) date.

The reliability scale of areas mapped form 2012 onward is 1:10,000. For areas where circa 2003 land use information has been used the reliability scale is 1:25,000. This is identified in the source scale attribute for each feature in the dataset.

The date of the data set is set as the land use occurring at the time the imagery (satellite or aerial) was acquired, which can range from 2003 to 2013. This dataset was updated in August 2017 to include values in the attribute fields of Source, Source Date, Source Scale, Reliability and LU Mapping (Currency) Date.

A national catchment scale land use product is also available as a 50m raster - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) http://www.agriculture.gov.au/abares/aclump/land-use/data-download.

Export Data Access API

Metadata Portal Metadata Information

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for Local Government Areas in Australia.The ASGS Local Government Areas are an ABS approximation of gazetted local government boundaries as defined by each State and Territory Local Government Department. Local Government Areas cover incorporated areas of Australia. Incorporated areas are legally designated parts of a State or Territory over which incorporated local governing bodies have responsibility. The major areas of Australia not administered by incorporated bodies are the northern parts of South Australia, and all of the Australian Capital Territory and the Other Territories. These regions are identified as ‘Unincorporated’ in the ASGS Local Government Areas structure.More information on local governments can be found at the Australian Local Government Association website: http://www.alga.asn.au The suffix on Long Official Name Local Government Area indicates the Local Government Area status: Cities (C), Areas (A), Rural Cities (RC), Boroughs (B), Shires (S), Towns (T), Regional Councils (R), Municipalities/Municipal Councils (M), District Councils (DC), Regional Councils (RegC), Aboriginal Councils (AC).Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.

City council members, their positions, and term end date for municipalities in Alaska.Source: Alaska Municipal LeagueThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Municipal League.

This data is used for a broadband mapping initiative conducted by the Washington State Broadband Office. This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. The data was processed and published to ArcGIS Living Atlas by Esri.AboutSpeedtest data is used today by commercial fixed and mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. Government regulators such as the United States Federal Communications Commission and the Malaysian Communications and Multimedia Commission use Speedtest data to hold telecommunications entities accountable and direct funds for rural and urban connectivity development. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access.DataHundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of "zoom level" (or "z"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.The data can be found at: https://github.com/teamookla/ookla-open-dataUpdate CadenceThe tile aggregates start in Q1 2019 and go through the most recent quarter. They will be updated shortly after the conclusion of the quarter.Esri ProcessingThis layer is a best available aggregation of the original Ookla dataset. This means that for each tile that data is available, the most recent data is used. So for instance, if data is available for a tile for Q2 2019 and for Q4 2020, the Q4 2020 data is awarded to the tile. The default visualization for the layer is the "broadband index". The broadband index is a bivariate index based on both the average download speed and the average upload speed. For Mobile, the score is indexed to a standard of 25 megabits per second (Mbps) download and 3 Mbps upload. A tile with average Speedtest results of 25/3 Mbps is awarded 100 points. Tiles with average speeds above 25/3 are shown in green, tiles with average speeds below this are shown in fuchsia. For Fixed, the score is indexed to a standard of 100 Mbps download and 3 Mbps upload. A tile with average Speedtest results of 100/20 Mbps is awarded 100 points. Tiles with average speeds above 100/20 are shown in green, tiles with average speeds below this are shown in fuchsia.Tile AttributesEach tile contains the following adjoining attributes:The year and the quarter that the tests were performed.The average download speed of all tests performed in the tile, represented in megabits per second.The average upload speed of all tests performed in the tile, represented in megabits per second.The average latency of all tests performed in the tile, represented in millisecondsThe number of tests taken in the tile.The number of unique devices contributing tests in the tile.The quadkey representing the tile.QuadkeysQuadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.LayersThere are two layers:Ookla_Mobile_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).Ookla_Fixed_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).The layers are set to draw at scales 1:3,000,000 and larger.Time Period and update Frequency Layers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.

This dataset includes 2020 census blocks as delineated by the U.S. Census Bureau and made available through their TIGER/Line files. Census blocks are statistical areas bounded on all sides by visible features (e.g., streets, roads, streams, railroad tracks), and by non-visible boundaries (e.g., city or town limits, short line-of-sight extensions of streets and roads). Generally, census blocks are small in area (e.g., a block in a city). However, census blocks in suburban and rural areas may be large, irregular, and bounded by a variety of features. In remote areas, they may encompass hundreds of square miles. Blocks do not cross the boundaries of any entity for which the Census Bureau tabulates data. Census blocks are numbered uniquely within the boundaries of each state, county, and census tract with a 4-character census block number. The first character of the tabulation block number identifies the block group. A block number can only be unique by using the decennial census state, county, census tract, and block codes combined. There is no consistency in block numbers from census to census. For more information about census geographies, see https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc_Ch4.pdf .This file is for reference use only. NCTCOG and its members are not responsible for errors or inaccuracies in the file.