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 2023.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.Methodology: This 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.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 Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/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 scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. 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 scene 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.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 Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/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.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: air temperatureUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Afternoon Air Temperature and Evening Air Temperature. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Morning Air Temperature Observations in Floating-Point (°F)

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Afternoon Heat Index and Evening Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Morning Heat Index Observations in Floating-Point (°F)

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: air temperatureUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Air Temperature and Afternoon Air Temperature. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Evening Air Temperature Observations in Floating-Point (°F)

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Heat Index and Evening Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Afternoon Heat Index Observations in Floating-Point (°F)

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Afternoon Heat Index and Evening Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Morning Heat Index Observations in Floating-Point (°F)

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

Data is archived here: https://doi.org/10.5281/zenodo.4818011Data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: 10.1038/s43016-021-00372-z). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.This data and code archive contains the following files and folders:* READMEDescription: text file with this description* flowchart.pdfDescription: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.* runAll.shDescription: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)* Folder "DataRaw"Description: folder for raw data filesThis folder contains the following files:- DataRaw/COWS.xlsxDescription: MS-Excel file with the number of cows per countySource: USDA NASS QuickstatsObservations: All available counties and years from 2002 to 2012- DataRaw/milk_state.xlsxDescription: MS-Excel file with average monthly milk yields per cowSource: USDA NASS QuickstatsObservations: All available states from 1981 to 2018- DataRaw/TMAX.csvDescription: CSV file with daily maximum temperaturesSource: PRISM Climate Group (spatially averaged)Observations: All counties from 1981 to 2018- DataRaw/VPD.csvDescription: CSV file with daily maximum vapor pressure deficitsSource: PRISM Climate Group (spatially averaged)Observations: All counties from 1981 to 2018- DataRaw/countynamesandID.csvDescription: CSV file with county names, state FIPS codes, and county FIPS codesSource: US Census BureauObservations: All counties- DataRaw/statecentroids.csvDescriptions: CSV file with latitudes and longitudes of state centroidsSource: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" functionObservations: All states* Folder "DataGenerated"Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).* Folder "Results"Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).* Folder "Figures"Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.* Folder "Tables"Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.* Folder "logFiles"Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.* PrepareCowsData.RDescription: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses* PrepareWeatherData.RDescription: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses* PrepareMilkData.RDescription: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses* CalcFrequenciesTHI_Temp.RDescription: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state* CalcAvgTHI.RDescription: R script that calculates the average THI in each state* PreparePanelTHI.RDescription: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins* PreparePanelTemp.RDescription: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins* PreparePanelFinal.RDescription: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses* EstimateTrendsTHI.RDescription: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set* EstimateModels.RDescription: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications* CalcCoefStateYear.RDescription: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification* SearchWeightMonths.RDescription: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term* TestModelSpec.RDescription: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10* CreateFigure1a.RDescription: R script that creates subfigure a of Figure 1* CreateFigure1b.RDescription: R script that creates subfigure b of Figure 1* CreateFigure2a.RDescription: R script that creates subfigure a of Figure 2* CreateFigure2b.RDescription: R script that creates subfigure b of Figure 2* CreateFigure2c.RDescription: R script that creates subfigure c of Figure 2* CreateFigure3.RDescription: R script that creates the subfigures of Figure 3* CreateFigure4.RDescription: R script that creates the subfigures of Figure 4* CreateFigure5_TableS6.RDescription: R script that creates the subfigures of Figure 5 and Table S6* CreateFigureS1.RDescription: R script that creates Figure S1* CreateFigureS2.RDescription: R script that creates Figure S2* CreateTableS2_S3_S7.RDescription: R script that creates Tables S2, S3, and S7* CreateTableS4_S5.RDescription: R script that creates Tables S4 and S5* CreateTableS8.RDescription: R script that creates Table S8* CreateTableS9.RDescription: R script that creates Table S9

United States US: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.214 % in 2009. United States US: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.214 % from Dec 2009 (Median) to 2009, with 1 observations. United States US: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Heat Index and Afternoon Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Evening Heat Index Observations in Floating-Point (°F)

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA Strategies, U.S. Census BureauPublication Date: April 1, 2022What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Evening Heat Index (°F) for U.S. Census Block Groups for Cities in Texas, Vermont, Ohio, Michigan, Mississippi, New Mexico, Florida, Louisiana, Rhode Island, Virginia, California, Washington, Massachusetts, Hawaii, New York, Florida, Missouri, New Jersey, Indiana, North Carolina, and Puerto Rico

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Sacramento, CAMorning air temperatures in cities

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Albuquerque NM

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Seattle, WAMorning air temperatures in cities

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Harrisonburg, VAEvening air temperatures in cities

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Harrisonburg, VAEvening air temperatures in cities