Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.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.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): 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 Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne 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.

The stream geomorphic assessment (SGA) is a physical assessment competed by geomorphologists to determine the condition and sensitivity of a stream. The SGA Reach Breaks are points that indicate where the Phase 1 SGA reach breaks are. They are based on changes in topography, slope and valley setting. Where there is a significant change in any of the above there is a reach break created.

The stream geomorphic assessment (SGA) is a physical assessment competed by geomorphologists to determine the condition and sensitivity of a stream. The SGA Phase 2 Segment Breaks are points that indicate where the Phase 1 SGA reach was "segmented" into smaller Phase 2 segments. These segments are determined in the field and are based on changes in topography, slope and valley setting that were not found in phase 1, and on changes in condition found in the field. Where there found in the field a significant change in any of the above there is a segment break created.

Power plants with a capacity of at least 1 MW are included in totals. Counties with nosymbol have no utility-scale renewable electric generation. Distributed generation, such asrooftop solar, is not included. Data is classified using Jenks Natural Breaks method.Projection is WGS 1984 California (Teale) Alberts (US Feet). Data sources are the CaliforniaEnergy Commission's Quarterly Fuel and Energy Report and the Wind GenerationReporting System databases. Data provided is for the year 2024 and is current as of July1, 2025. For further inquiries contact John Hingtgen at john.hingtgen@energy.ca.gov.

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

Power plants with capacity of at least 1 MW are included in totals. Counties shaded in grey have no utility-scale renewable electric capacity. Distributed generation such as rooftop solar, is not included. Data is classified using Jenks Natural Breaks Method. Projection is WGS 1984 California *Teale Albers (US Feet). Data sources are the California Energy Commission's Quarterly Fuel and Energy Report and the Wind Generation Reporting System databases. Data provided is for the year 2023 an dis current as of June 20, 2024. For further inquiries contact John Hingtgen at john.hingtgen@energy.ca.gov.

Solar electrical generation data is reported for commercial power plants: those with a nameplate capacity of 1 MW or more. Counties in gray reported no generation in 2020. San Bernardino and Riverside county had solar thermal electric generation. Map and data from the California Energy Commission. Data is classified using the Jenk’s Natural Break’s method. Data is current as of November 22, 2021. Contact Rebecca Vail at (916)651- 0477 or John Hingtgen at (916) 510-9747 with questions.

Power plants with a capacity of at least 1 MW are included in totals. Counties displayed ingray had no utility-scale renewable electric generation. Distributed generation, such asrooftop solar, is not included. Data is classified using Jenks Natural Breaks method.Projection is WGS 1984 California (Teale) Alberts (US Feet). Data sources are the CaliforniaEnergy Commission's Quarterly Fuel and Energy Report and the Wind GenerationReporting System databases. Data provided is for the year 2022 and is current as of May26, 2023. For further inquiries contact Gordon Huang at (916) 477-0738 or John Hingtgenat (916) 510-9747.

Solar electrical generation data is reported for commercial power plants thathave a nameplate capacity of 1 MW or more. San Bernardino and Riversidecounty had solar thermal in addition to solar photovoltaic electric generation.Data originates from the California Energy Commission, and is classified usingthe Jenk’s Natural Break’s method. Projection: NAD 1983 California (Teale)Albers. Data is for 2023 and is current as of June 26, 2024. For moreinformation, contact John Hingtgen at john.hingtgen@energy.ca.gov.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.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): 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 Pete.Aniello@tpl.org with feedback.Terms of UseYou understand and agree, and will advise any third party to whom you give any or all of the data, that The Trust for Public Land is neither responsible nor liable for any viruses or other contamination of your system arising from use of The Trust for Public Land’s data nor for any delays, inaccuracies, errors or omissions arising out of the use of the data. The Trust for Public Land’s data is distributed and transmitted "as is" without warranties of any kind, either express or implied, including without limitation, warranties of title or implied warranties of merchantability or fitness for a particular purpose. The Trust for Public Land is not responsible for any claim of loss of profit or any special, direct, indirect, incidental, consequential, and/or punitive damages that may arise from the use of the data. If you or any person to whom you make the data available are downloading or using the data for any visual output, attribution for same will be given in the following format: "This [document, map, diagram, report, etc.] was produced using data, in whole or in part, provided by The Trust for Public Land."

Energy data is collected for power plants that have a nameplate capacity of 1 MW or more. Counties that are gray had no utility-scale solar capacity. Data originates from the California Energy Commission, and is classified using the Jenk’s Natural Break’s method. Projection: NAD 1983 California (Teale) Albers. Data is for 2022 and is current as of August 7, 2023. For more information, contact Gordon Huang at gordon.huang@energy.ca.gov or John Hingtgen at john.hingtgen@energy.ca.gov.

For a thorough discussion of the purpose, design and management of the Vermont River Corridors dataset, please see the "Vermont DEC Flood Hazard Area and River Corridor Protection Procedures December 5, 2014" http://www.vtwaterquality.org/rivers/docs/FHARCP_12.5.14.pdf . River corridors encompass an area around and adjacent to the present channel where fluvial erosion, channel evolution and down-valley meander migration are most likely to occur. River corridor widths are calculated to represent the narrowest band of valley bottom and riparian land necessary to accommodate the least erosive channel and floodplain geometry (i.e. equilibrium conditions) that would be created and maintained naturally within a given valley setting. River corridors are developed to facilitate ANR’s responsibilities in providing municipalities, regional planning commissions, and Act 250 District Commissions with technical assistance and information concerning river sensitivity and fluvial erosion hazards. Vermont river corridors include areas where active, potentially hazardous river erosion and deposition process have occurred or are likely to occur. These delineations do NOT indicate that areas outside river corridors, particularly those immediately abutting the river or river corridor are free from fluvial erosion hazards.This dataset is part of the “applicable maps” used in conjunction with other best available stream geomorphic data to implement both the Flood Hazard Area and River Corridor “Rule” and “Protection Procedure.” The data will be updated over time as described in the Procedure. The date of the version posted on the Vermont Natural Resource Atlas indicates the most recent update. Users should cite the Creation Date for the version. Data processing was done using ArcGIS 10.x, Spatial Analyst, and Arc Hydro Tools 2.0. Source and digitized data included VT Meander Centerlines (MCLs), VT Reach Break points, VT Hydrography streams, VT 10-meter DEM, VTHYDRODEM, HUC 8 Basins, VT Roads and Railroads, field-verified Valley Walls and Stream Geomorphic Assessment datasets. This 2019 version is a hybrid of Phase I and II levels of detail. River Corridor polygons are divided by subwatershed breaks and by SGA reach/segment breaks. Attributes include SGAT ID, Stream Name, Drainage Area in square miles, Bankfull width in feet, Channel Multiplier, DMS Channel Multiplier, DMS Channel Width, Erosion Power/Risk and Deposition Power/Risk. Major derived datasets include raster and vector valley walls, catchments per stream reach, variable-width MCL buffers, and the final River Corridor. A Frequently-Asked Questions page is available at: http://floodready.vermont.gov/rcfaqThis package also includes streams that have a drainage area between .25 and 2 square miles. Streams were mapped by filtering drainage areas from the VHD accumulation grids. They were then extracted from the grid and vectorized. Linework is for reference only, as it does not match the VHD perfectly. Small streams are given a simple 50-foot setback from top of bank in lieu of a mapped River Corridor.

Power plants of at least 1 MW are required to report data. Counties in gray had no utility-scale (commercial) renewable electric generation. Distributed generation (for example,rooftop solar) is not included. Data is classified using the Jenk's Natural Breaks method.Projection: WGS 1984 California (Teale) Alberts (US Feet). Data Sources: California EnergyCommission. Energy production data is from the Quarterly Fuel and Energy Report, andthe Wind Performance Reporting System databases. Data is for 2020 and is current as ofNovember 8, 2021. For more information, please contact Rebecca Vail at (916) 477-0738or John Hingtgen at (916) 510-9747.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.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.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): 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 Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne 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 Pete.Aniello@tpl.org with feedback.

Data pulled from 2023 Trust for Public Lands' image service.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.

Data has been created from hosted feature service (https://mapping.gw.govt.nz/arcgis/rest/services/GW/NRPMap_P_operative/MapServer). It has been shared to the Open Data Portal. Schedule K: Significant surf breaksThis map shows the significant surf breaks in the Wellington region. It is a campanion feature class to the swell corridors map. The identification of the surf breaks was based on information in the New Zealand wavetrack guide and undertaken with local knowledge and field surveys by Michael Gunson. This involved identification and characterisation (eg, type of break, length of ride, best surf conditions, swell, currents, water level) for each break in the Greater Wellington Region. This was followed by numerical modelling of the swell environment at each break to determine the main swell corridors that allow passage of swell waves to the break. GIS mapping of the surf breaks was undertaken by Iain Dawe, Greater Wellington Regional Council. More details can be found in the report:Atkin, E., Gunson, M. & Mead, S. (2015), Regionally Significant Surf breaks in the Greater Wellington Region. Report prepared by for Greater Wellington Regional Council by eCoast, 84p.

Water flows are represented by polylines, and are not always spatially accurate. The flows are, in essence, diagrams of water flow models that are broken up by subareas, which are similar to HUC 12 watersheds. Flows can be natural or man-made. Flows can break hydrologic rules and be carried over higher elevations, due to pumping and tunnels. The models are part of the Division of Water Resources' Water Budget.

This layer contains the relative heat severity for every pixel for every city in the contiguous United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2021, patched with data from 2020 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): 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.

SACS Census Places identify high risk places with regards to coastal storm flooding risk. These designations are assigned by the results of the SACS Tier 1 Risk Assessment, and the SACS Tier 2 Economic Risk Assessment. Tier 1 Risk AssessmentTo identify areas with potential high-risk, the Tier 1 CRI was intersected with U.S. Census Bureau place boundaries using zonal statistics in GIS to determine both the acreage and intensity of potential risk for that area. Census places include both incorporated places and unincorporated census-designated places. Two thresholds were applied to determine if a census place is considered high-risk in either the existing condition, future condition, or both. At minimum, the combination of medium-high (amber) and high (red) composite risk needed to encompass: - Fifty acres of a census place for the continental United States. This is a conservative threshold, approximately equal to an area extending 1 mile along a shoreline and two blocks inland. For Puerto Rico, 5 acres were used as the threshold and, in the U.S Virgin Islands, the threshold was more qualitative, owing to the relatively small geographic area of its territories. - One-half of one percent of the total area (of a place).Tier 2 Economic Risk AssessmentEconomic risk ratings for existing and future condition risk are determined by natural break classifications of the expected annual damages of the aggregate of census block values within a census place. Low and Upper bound classification breaks are identified within the Tier 2 Economic Risk Assessment technical report. The natural breaks are relative to census places within each state or territory, except for Florida, where the natural break classifications are sorted by planning reach: FL_06 and FL_07, FL_08 and FL_09, FL_10 and FL_11, and FL_12 and FL_13.