Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on streets data from StreetMap Premium, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...which presumes they have access to a car or public transit. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Pro or ArcGIS Online to use it as a backdrop to your local analysis work. Or open it in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email. See this web map for a map with a popup layer.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more.Data source: see this map package.

This web map is part of the Centers for Disease Control and Prevention (CDC) PLACES. It provides model-based estimates of binge drinking prevalence among adults aged 18 years and old at county, place, census tract and ZCTA levels in the United States. PLACES is an expansion of the original 500 Cities Project and a collaboration between the CDC, the Robert Wood Johnson Foundation, and the CDC Foundation. Data sources used to generate these estimates include the Behavioral Risk Factor Surveillance System (BRFSS), Census 2020 population counts or Census annual county-level population estimates, and the American Community Survey (ACS) estimates. For detailed methodology see www.cdc.gov/places. For questions or feedback send an email to places@cdc.gov.Measure name used for binge drinking is BINGE.

The HD Live Map market is experiencing robust growth, projected to reach a market size of $1279 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.8% from 2025 to 2033. This expansion is fueled by several key market drivers, including the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies in both commercial and military applications. The rising demand for precise and real-time location data for improved navigation, safety, and traffic management further contributes to this growth. Technological advancements, such as the development of high-resolution sensor technologies and improved data processing capabilities, are enhancing the accuracy and reliability of HD Live Maps, making them an indispensable component of next-generation vehicle systems. The market is segmented by crowdsourcing and centralized models, reflecting the varied approaches to data acquisition and map creation. Furthermore, application-based segmentation highlights the significant roles of commercial and military sectors, with the former encompassing automotive, logistics, and ride-sharing applications, while the latter emphasizes defense and security operations. Leading players such as TomTom, Google, Alibaba (AutoNavi), and Baidu are actively investing in R&D and strategic partnerships to consolidate their market positions. The competitive landscape is dynamic, with established players and emerging technology firms competing to deliver superior map data and services. The geographical distribution of the HD Live Map market is diverse, with North America and Asia Pacific expected to dominate due to significant investments in autonomous vehicle technology and robust infrastructure development. Europe is also a significant market, driven by strong government support for technological innovation and the growing adoption of connected car services. The market growth will be influenced by factors such as government regulations related to autonomous driving, the cost of data acquisition and processing, and the increasing integration of HD Live Maps into various smart city initiatives. The ongoing development of 5G networks and the rise of IoT devices are also expected to further stimulate market growth in the coming years. Continuous improvement in map accuracy and detail, coupled with wider industry adoption, will remain pivotal to the market's sustained expansion throughout the forecast period.

Author: A Evans, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 6Resource type: lessonSubject topic(s): history, gisRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context

Standard 6. Geographic factors influence the distribution, functions, growth and patterns of cities and other human settlements.

Standard 18. Economic expansion and the conquest of indigenous and Mexican territory spurred the agricultural and industrial growth of the United States; led to increasing regional, economic and ethnic divisions; and inspired multiple reform movements. (Expansion and Reform: 1792-1861)Objectives: Students will be able to:

1. Develop skills to manipulate and use ArcGIS software.
2. Use geospatial technology to locate features.
3. Analyze map layers to determine the impact of minerals, agriculture, and income on reserved lands.
4. Locate and label Minnesota’s major bodies of water on a blank map.
5. Identify and explain the locations of the Ojibwe and the Dakota.
6. Identify and explain the locations of Minnesota’s reservations.Summary: Students will layer maps using ArcGIS to analyze Minnesota’s landscape and the locations of reservations.

This dataset contains model-based census tract level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The Underground Utility Mapping market is experiencing robust growth, projected to reach a market size of $1.32 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) exceeding 9.61% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and infrastructure development necessitate accurate and efficient utility mapping to prevent costly damages during excavation projects. Furthermore, stringent safety regulations and rising awareness of potential risks associated with damaging underground utilities are pushing adoption of advanced mapping technologies. The growing demand for smart city initiatives and the implementation of digital twin technologies further fuel market growth. The market is segmented by component type (solutions – including Ground Penetrating Radar (GPR), electromagnetic locators, and other solutions – and services) and end-user industry (public safety, oil and gas, building and construction, telecommunications, electricity, and others). The solutions segment currently dominates, reflecting the high capital expenditure associated with acquiring advanced mapping equipment. However, the services segment is expected to witness significant growth as businesses increasingly outsource utility mapping tasks to specialized firms. North America and Europe currently hold significant market share, driven by advanced infrastructure development and early adoption of advanced technologies. However, emerging economies in Asia and the Middle East are expected to show rapid growth in the coming years due to increased investments in infrastructure and utility modernization projects. Competitive landscape is characterized by both established players and emerging technology providers, leading to innovation and wider accessibility of utility mapping solutions. The continued growth trajectory is expected to be influenced by technological advancements, such as the integration of artificial intelligence (AI) and machine learning (ML) into mapping systems to improve accuracy and efficiency. The development of more user-friendly and portable equipment will broaden accessibility, particularly for smaller businesses and municipalities. However, the high initial investment cost for advanced mapping equipment could pose a challenge for some smaller companies, especially in developing nations. Nevertheless, the overall outlook for the Underground Utility Mapping market remains positive, fueled by long-term infrastructural development plans globally and a growing awareness of the safety and economic benefits of accurate utility mapping. Recent developments include: March 2024: WSB LLC (“WSB”), one of the nation’s fastest-growing infrastructure engineering and consulting firms, partnered with 4M Analytics, the nation’s leading subsurface utility AI mapping and analytics solution. This partnership is intended to support infrastructure projects across the United States, focusing on data integrity and real-time digital delivery. Leveraging artificial intelligence, computer vision, and change detection techniques, 4M Analytics synthesizes, digitizes, and geo-locates millions of utility data sources into a single platform and visually validates each line using vertical and horizontal imagery dating back to the 1940s. This enables ‘real-time’ access to the utility landscape for infrastructure projects through an intuitive user interface. The mapping resources will decrease the time it takes to locate underground utilities for owners, civil engineering firms, general contractors, subsurface utility engineering firms, and many other utility stakeholders., February 2024: Exodigo announced that it would offer the accurate and complete subsurface maps needed to improve undergrounding processes for power lines as part of the Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security (GOPHURRS) program led by the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E).. Key drivers for this market are: Emerging Technologies Combined With Utility Maps to Improve the Exploration Activities, Increasing Availability of Detecting Applications and Increased Return on Marketing Spending. Potential restraints include: Emerging Technologies Combined With Utility Maps to Improve the Exploration Activities, Increasing Availability of Detecting Applications and Increased Return on Marketing Spending. Notable trends are: Ground Penetrating Radar is Expected to be the Largest Component Type Solution.

Boundaries show on this map are derived from legal descriptions contained in petitions to the Kansas Secretary of State for the creation or extension of watershed districts and in petitions to the Chief Engineer of the Division of Water Resources, Kansas Board of Agriculture, for transfers or mergers between adjacent watersheds. These petitions and other records relating to watershed districts in Kansas are maintained by the Division of Water Resources in the Kansas Department of Agriculture. Most of the legal descriptions were converted to digital geographic coordinates of longitude and latitude using the KGS's LEO II software. Exterior boundaries of the lands included in each watershed were compiled using the Dpoly software, also developed at the KGS. Portions of some boundaries were digitized directly from 1:24,000 scale topographic maps, from the United States Geological Survey. Some incorporated cities are explicitly excluded from watersheds in the legal descriptions. The digital representation of the boundaries of these cities, as used in this coverage, were extracted from the 1990 Pre-Census TIGER files distributed by the U.S. Census Bureau.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

How Does 1930-1940 Land Use Policy Affect Our Communities Today, and Can Qualified Opportunity Zones Be Used to Remedy the Problem?HOLC Redlining Practicesn the 1930s-1940s, the U.S. government created the Home Owners' Loan Corporation to provide loans to families at risk of foreclosing on their mortgages. HOLC created maps of cities with populations of 40,000 or above to grade areas on the perceived risk of loan default. The maps contained racist evaluations of land tracts. Although there is no evidence of HOLC loans being denied to people of color, their assessments were shared with the FHA, National Board of Realtors, and Lenders. There is substantial evidence that these organizations used a similar grading technique to deny home loans to non-white families. This historic lending practice of denying loans and economic opportunities to people of color and economic disadvantage is called "Redlining," due to the fact that the lowest-grade HOLC areas were outlined in red. This map contains the HOLC grading layer, provided by the Esri Living Atlas, in which Graded maps for 149 U.S. cities can be found. Map grades are opaque red, yellow, green, and blue to designate HOLC grading levels on the map.Current Community Indicators of DisadvantageIn 2021, President Biden issued Executive Order 14008, which did several things. One of these things was to create a screening tool to identify communities disproportionately impacted by climate change and economic hardship. The purpose of this tool, the Justice 40 Initiative, is to identify communities in need of economic and environmental assistance. It was created in 2022 by the Council on Environmental Quality. In the map, this layer is the Justice 40 Initiative Layer. Disadvantaged areas are shaded in transparent grey/blue.Qualified Opportunity ZonesThe 2017 Tax Cuts and Jobs Act designates thousands of "Opportunity Zones" in which investment is incentivized to help create jobs and strengthen low-income areas. Under this act, states may designate up to 1/4 of low-income census tracts as Opportunity Zones. However, not all are located in low-income areas. In 2022, the Opportunity Zones Transparency, Extension, and Improvement Act was introduced in Congress but failed to become law. The Qualified Opportunity Zones layer designates Opportunity Zones in transparent pink shading. Questions to consider:What areas on the map show overlap between formerly HOLC Redlined grades and current Justice 40 "Disadvantaged" evaluations? How could past discriminatory practices have shaped communities into what we see today?What "Disadvantaged" areas overlap with "Qualified Opportunity Zones"? Is the Opportunity Zone program being well utilized to boost economic and social well-being in disadvantaged communities?This map contains 3 layers:1. HOLC Graded areas 1930-19402. Justice 40 Initiative -Climate and Justice Screening Tool for Disadvantaged Communities (2022)3. Qualified Opportunity Zones in effect now, created in 2017All data links for this map were taken from the Esri Living Atlas, with additional information from the University of Richmond Mapping Inequality Project.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This web map is part of the Centers for Disease Control and Prevention (CDC) PLACES. It provides model-based estimates of stroke prevalence among adults aged 18 years and older at county, place, census tract, and ZCTA levels in the United States. PLACES is an expansion of the original 500 Cities Project and a collaboration between the CDC, the Robert Wood Johnson Foundation, and the CDC Foundation. Data sources used to generate these estimates include the Behavioral Risk Factor Surveillance System (BRFSS), Census 2020 population counts or Census annual county-level population estimates, and the American Community Survey (ACS) estimates. For detailed methodology see www.cdc.gov/places. For questions or feedback send an email to places@cdc.gov.Measure name used for stroke is STROKE.

11/22/2024 – The 4 lanes of US 85 from south of the Little Missouri to south of Watford City was added, US 2 was realigned near RP 313, about 2 miles east of Petersburg. There have also been numerous intersections and ramps "trued up" for preparation of MIRE intersections. 1/27/2022 – Business 94 at Dickinson Interchange 64 – The NE & SE ramps were realigned due to 2021 construction. The 2 ramps and 94B were adjusted to a single intersection. Hwy 20 between reference points 87 and 90 was realigned due to a grade raise several years ago.3/1/21 - Realigned the following highways: Hwy 1804 - Mileposts 300–301, 303–305, and 317-317.979, Hwy 85 - Mileposts 123-130, Hwy 23A – Milepost 911 and Hwy 35 - Mileposts 0–12/3/2020 – Minot Bypass - Added the southbound route to Hwy 83B (Route ID = 10). This includes realignment of the south bound ramp at reference point 921.5. Also realigned two ramps at the I94 and University Drive interchange in Fargo.12/4/19 - NewTown Bypass – 1.3714 miles was added to Route ID 297. A new reference point was created at the intersection of Route ID 297 and Route ID 205 (Hwy 23) at 48.684 and at 925.629 (Hwy 23B). Reference points were also created at 926 and 927 on Route ID 297 (Hwy23B). The ramps at the Sheyenne Interchange in West Fargo were updated. The Route ID = 169. The 5 existing ramps were realigned and 3 other ramps were created.9/13/18 - Route ID 4/253 - US 2 Business in Williston, alignment change as it intersects US 2. Route ID 67 - 32nd Avenue interchange on I-29, addition of exit ramp for I-29 Southbound traffic. Route ID 205 - alignment change at the intersection of ND 23 and County Roads 55 and 103/21/17 - Route ID 68 Interchange 64.252 on I-29 in Fargo. This interchange is 13th Avenue and I-29 one leg of the ramp was realigned.1/25/17 - started to maintain roads in Esri's Road and Highways. The shapes now contain measures in miles along with the associated linear referencing/roads and highways fields.9/22/16 - added Killdeer Bypass 1. ND 22 North Route ID = 272, Created a new alignment for ND 22 west of Killdeer that begins with intersection of ND 200 and travels northeasterly to a junction with ND 22B north of Killdeer. New reference points were created on the new alignment as follows: 105.710 intersection of ND 22 and ND 200, 106, 107, 108, 109, 109.518 intersection of ND 22 and ND 22B north of Killdeer.2. ND 22 Business (Killdeer) Route ID = 302, The existing ND 22 through Killdeer becomes ND 22 Business due to the completion of a bypass route constructed west of Killdeer. New reference points were created as follows: 940.466 this junction of ND 22, ND 200 and ND 22B. Current ND 22 reference points through Killdeer remain in place but the number changes as follows: 105.000 = 941.000, 106.000 = 942.000, 107.000 = 943.000, 108.000 = 944.000, 109.000 = 945.000. Reference point 945.518 intersection of ND 22B and ND 22 north of Killdeer was also created.3. ND 200 East Route ID = 200, A new reference point 93.247 was created for the intersection of ND 22 and ND 200 west of Killdeer.9/6/16 by bb - added Dickinson Bypass and associated Reference Points.3/1/16 by bb - realigned US 85 North Route ID = 261 to match 2015 NAIP. Added Ramps on 94 at Mile Marker 56.668.10/20/2015 by bb - 1. US 85 North Route ID = 261, Created new alignment for US 85 North that follows the permanent NW Bypass at Williston. New reference points were created as follows: 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 196.705 this is the junction of US 85 and US 2 north of Williston.2. US 85 South Route ID = 300, Created new alignment for US 85 South that completes the four lane project between Alexander and Williston. New reference points were created as follows: 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, and 183.743. This is the junction with the US 2 west of Williston. Created new alignment for US 85 that follows the permanent NW Bypass at Williston. New Reference points created as follows: 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 196.705 this is the junction of US 85 and US 2 north of Williston.3.US 2 East Route ID = 253, alignment change north of Williston at junction of US 85, also created new reference point 25.674 at the intersection of US 85 on north end of NW Permanent Bypass.4. US 2 West Route ID = 254, alignment change north of Williston at junction of US 85, also created new reference point 25.674 at the intersection of US 85 on north end of NW Permanent Bypass.5. ND 3 Route ID = 263, Realignment and grade raise south of Junction of ND 200 west of Hurdsfield has been corrected.2/6/15 by bb - An extension of ND 46 has been added to the State Highway system. The segment of roadway east of I-29 was removed from the State Highway system in 1977. As of May 2011, a document showing a maintenance agreement with a county, as required by State Law to transfer responsibility, has not been found. The Fargo District has been doing basic maintenance on this segment. The segment of road that begins at I-29 and extends to the east until the intersection CMC 0957 is added to the State Highway system. The addition will result in an increase of 0.4 miles. This addition has also created a new reference point 120.823 which is at the end of the highway, reference point 120.318 is moved to the center of the structure on I-29.11/26/14 by bb - The changes to ND 23A are as follows: Reference point 900.000 becomes 910.000, 901.000 becomes 911.000, and 901.526 becomes 911.526. These changes are being made to help eliminate the confusion with the reference points on ND 23B also in Watford City, currently 901.000 appears on both highways.11/18/14 by bb - added New Town Bypass11/3/14 by Gerald - extended US85 Southbound to Mile Marker 172US 85 South Route ID = 300, Created new alignment for US 85 South that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to reference point 172.000 which is north of McKenzie County Highway 16. New reference points were created as follows: 139.082 beginning of highway south of Watford City, 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander. Created all whole number reference points between 139.082 and reference point 172.000.10/29/14 by Gerald 1. ND 23 Business Route ID = 297, Changed existing ND 23 in Watford City from the previous Junction of US 85 to the Junction of ND 1806 to ND 23B also changed the reference points 0.000 = 900.000, 1.000 = 901.000, 1.350 = 901.350, 2.000 = 902.000, 3.000 = 903.000, 3.353 = 903.3532. ND 23 East Route ID = 205, Watford City SE Bypass from the previous Junction of US 85 continuing northeasterly to the current alignment of ND 23. New reference point 0.533 was created for the beginning of the route as well as reference point 3.701 which is the Junction with ND 1806 extension.3. ND 23 West Route ID = 299, Watford City SE Bypass from the previous Junction of US 85 continuing northeasterly to the current alignment of ND 23. New reference point 0.533 was created for the beginning of the route as well as reference point 3.751 which is the termination point of this route.4. ND 1806 Route ID = 271, Created an extension of ND 1806 from the Junction of ND 23 B north of Watford City to the Junction of the ND 23 along the new alignment. New reference point 311.577 was created for the intersection.5. US 85 North Route ID = 261, Created new alignment for US 85 North that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to the Junction of US 2 at Williston. New reference points were created as follows: 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander.6. US 85 South Route ID = 300, Created new alignment for US 85 South that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to the Junction of ND 200 North of Alexander. New reference points were created as follows: 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander. Created a new reference point 139.082 which is the beginning point for US 85 South along with all reference points between this point and the Junction of ND 200 North of Alexander.7. US 85 Business North Route ID = 298, The existing route US 85 through Watford City and Alexander becomes US 85 Business due to the completion of the Bypass routes. New reference points that were created are: 950.000 intersection with US 85, 950.555 intersection with ND 23, 951.000, 952.000, 952.486 intersection with ND 23 A, 952.707 intersection with ND 23 B, 953.000, 954.000, 955.000, 956.000, 956.233 intersection with US 85 west of Watford City, 970.079 intersection with US 85 south of Alexander, 971.000, 972.000, 973.000 intersection with US 85 north of Alexander.8. Several small alignment changes occurred due to construction projects: ND 8 Junction ND 50 to Bowbells. ND 22 slide repair through the badlands. ND 57 and ND 20 South of Devils Lake. ND 31 bridge replacement 13 miles north of South Dakota Border. ND 23A in Watford City, this highway had the wrong designation as ND 23B, this change corrected this error and is now correctly designated as ND 23A.9. Junction of ND 1804 and ND 58 near Trenton, the intersection of these two highways was realigned.10. US 2 alignment in Williston near 11th street to near 9th Ave NW.Changed all RTE_SIN codes to either I, U, or S.2/15/13