As climate change causes average temperatures to rise across the European continent, this will inevitably lead to an increasing number of heat-related deaths, or deaths as a result of excess exposure to high temperatures. This is particularly an issue for southern European countries, such as Italy, Greece, Spain, and Portugal, who are already experiencing a massive uptick in the number of extreme heat days per year, with temperatures often exceeding 40 degrees Celsius in these countries during the Summer. In the Summer of 2022, Italy recorded 295 heat deaths per million inhabitants, resulting in a total of over 18,000 people dying due to excess heat exposure. Europe-wide, this rate was 114 heat deaths per million inhabitants.

This statistic shows the number of fatalities due to significant heat or cold waves worldwide from 1900 to 2016*. The heat wave in Russia in June 2010 led to 55,736 deaths.

The costliest heat wave occurred in China in 2008. The heat wave caused estimated economic damage of about 21.2 billion U.S. dollars.

Heat-related mortality in US cities is expected to more than double by the mid-to-late 21st century. Rising heat exposure in cities is projected to result from: 1) climate forcings from changing global atmospheric composition; and 2) local land surface characteristics responsible for the urban heat island effect. The extent to which heat management strategies designed to lessen the urban heat island effect could offset future heat-related mortality remains unexplored in the literature. Using coupled global and regional climate models with a human health effects model, we estimate changes in the number of heat-related deaths in 2050 resulting from modifications to vegetative cover and surface albedo across three climatically and demographically diverse US metropolitan areas: Atlanta, Georgia, Philadelphia, Pennsylvania, and Phoenix, Arizona. Employing separate health impact functions for average warm season and heat wave conditions in 2050, we find combinations of vegetation and albedo enhancement to offset projected increases in heat-related mortality by 40 to 99% across the three metropolitan regions. These results demonstrate the potential for extensive land surface changes in cities to provide adaptive benefits to urban populations at risk for rising heat exposure with climate change.

The HWs are sorted by excess mortality in percentage.

Heat-related mortality in US cities is expected to more than double by the mid-to-late 21st century. Rising heat exposure in cities is projected to result from: 1) climate forcings from changing global atmospheric composition; and 2) local land surface characteristics responsible for the urban heat island effect. The extent to which heat management strategies designed to lessen the urban heat island effect could offset future heat-related mortality remains unexplored in the literature. Using coupled global and regional climate models with a human health effects model, we estimate changes in the number of heat-related deaths in 2050 resulting from modifications to vegetative cover and surface albedo across three climatically and demographically diverse US metropolitan areas: Atlanta, Georgia, Philadelphia, Pennsylvania, and Phoenix, Arizona. Employing separate health impact functions for average warm season and heat wave conditions in 2050, we find combinations of vegetation and albedo enhancement to offset projected increases in heat-related mortality by 40 to 99% across the three metropolitan regions. These results demonstrate the potential for extensive land surface changes in cities to provide adaptive benefits to urban populations at risk for rising heat exposure with climate change. CULE_pop2050 population projections for Atlanta, Philadelphia, and PhoenixWRF scenario outputWRF model output for each scenario, metropolitan area, and temperature metric combination.PLOSone_files.zip

Notice: this is not the latest Heat Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. The Heat Anomalies is also reclassified into a Heat Severity raster also published on this site. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Full Range Heat Anomalies - USA 2022Full Range Heat Anomalies - USA 2021Full Range Heat Anomalies - USA 2020Federal 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 or cooler than the average temperature for that same city as a whole. 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.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This statistic presents the number of deaths reported due to extreme heat events in select countries worldwide as of 2016. In July of 2010, approximately 55,736 additional deaths occurred during heat waves in Russia (including deaths from smog and wildfires).

This dataset is associated with the following publication, where further details are provided and which should be cited for further applications:

Huang et al. 2022, Non-linear response of temperature-related mortality risk to global warming in England and Wales, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ac50d5.

The data contain daily temperature-related mortality estimates for ten NUTS 1 regions of England and Wales over the period 1900 to 2099, based on temperatures from the 2018 UK Climate Projections (UKCP18) simulations. Exposure-response relationships are based on present-day observations and extrapolated where necessary.

Variables in the csv files are as follows:

"tmean": daily mean temperature (in degrees C)
"bAD": backward attributable deaths
"fAD": forward attributable deaths

RCP 8.5 and RCP 2.6 scenarios are considered, as indicated in the file names. Model numbers refer to UKCP18 climate models.

This statistic shows the share of occurrence and death tolls for weather-related disasters worldwide in the period from 1995 to 2015, by national income level. During the past 20 years, around ** percent of weather-related disasters affected lower-income countries.

Natural disasters and loss – additional information

The years 2014 and 2015 are two of the hottest years recorded since the 1880s. In 2014, there were ** deaths caused by extreme heat in the United States. The increased risk of extreme weather due to climate change has put pressure on countries to develop regulations to better protect infrastructure and human health. Between 1995 and 2015, about a third of the global weather-related disasters occurred in lower-middle income countries, however, almost half of the deaths due to these events affected these countries. The number of deaths caused by the Cyclone Nargis in Myanmar contributed significantly to these statistics. In high-income countries, weather-related deaths are largely due to heat waves. The actual number of casualties in low-income countries is estimated to be much higher and may reflect a lack of reporting.

China and India have been among the most severely impacted countries in the world in terms of weather catastrophes, accounting for some * billion people that have been affected between 1995 and 2015. Economic loss due to these events totaled some ** billion U.S. dollars in the Asia and Oceania regions. Millions of houses as well as public institutions such as schools, clinics, and hospitals have been damaged by weather-related disasters, primarily due to floods and storms. Over the last decades, countries have improved their preparedness as well as their response to natural disasters. Several countries in Asia have begun to follow the Hyogo Framework for Action, a guideline developed to help reduce disaster risk, in efforts to reduce the losses derived from these catastrophes.

Population data was provided by the Brazilian Institute of Geography and Statistics—IBGE.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Extreme Summer Days is the number of days per year where the maximum daily temperature is above 35°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘extreme summer days’ is used to refer to the threshold and temperatures above 35°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Extreme Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of extreme summer days to previous values.What are the possible societal impacts?The Annual Count of Extreme Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 35°C. Impacts include:Increased heat related illnesses, hospital admissions or death affecting not just the vulnerable. Transport disruption due to overheating of road and railway infrastructure.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Extreme Summer Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Extreme Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Extreme Summer Days show the number of extreme summer days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘ESD’ (where ESD means Extreme Summer Days, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Extreme Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Extreme Summer Days 2.5 median’ is ‘ExtremeSummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘ESD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Extreme Summer Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (https://doi.org/10.1038/s41591-024-03452-2).

It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.

The fully reproducible analysis code using the data and producing the results included in this repository is provided in GitHub. The results can be visualised and explored in a dedicated Shiny app.

Content

This repository contains three zip files, each with an internal codebook:

• data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (https://doi.org/10.5281/zenodo.10288665). This file also include a script showing how each dataset was extracted for the purpose of this projection study.
• results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels).
• results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.

It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

This statistic depicts the projected number of deaths caused by 1-in-30-year extreme heat events in select cities in the United States, broken down by scenario. Extreme heat events in New York are forecast to cause around ***** deaths if the global temperature increases by ***** degree Celsius.

Trends in the annual HWs number are also presented. Underlined values indicate a non-statistically significant (C.I. 95%) trend based on the Mann-Kendall Trend Test. The MRs are sorted by latitude (decreasing with the distance from the Equator).

About *** heat wave days were recorded across India in 2022, a decrease compared to the previous year. In recent years, these events were more intense in the northern regions of the country, coinciding with droughts, water shortage and an already inadequate infrastructure. The impact of heatwaves Illnesses and deaths are an obvious consequence of the impact of heat waves in India. With record breaking heat year after year, temperatures were recorded in the high 40s and low ** degree Celsius. For comparison, core human temperatures of ** degree Celsius are categorized as fever, requiring medical attention. In extreme cases, permanent brain damage can occur, or even death. Precaution and mitigation Inconsistent rains or unmitigated torrential rains, along with depleting groundwater reserves and droughts have led to severe water shortages across vast areas of the country, leading to a dependence on water tankers. These include cities like Chennai, Coimbatore, parts of Jharkhand, and Madhya Pradesh. India’s National Disaster Management Authority aims to keep heat related deaths at single digits. Measures to achieve that include increasing public awareness and distributing free water. In parts of Rajasthan, advisory actions were followed by pouring water onto asphalt roads to prevent them from melting during summer.

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.

Digital Heat Stroke Meters Market Outlook

The global digital heat stroke meters market size was valued at approximately USD 450 million in 2023 and is projected to reach USD 820 million by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This growth can be attributed to increasing awareness about heat-related illnesses, advancements in wearable technology, and the rising need for precise and real-time monitoring of temperature in various sectors such as healthcare, sports, military, and industrial settings.

One of the significant growth factors in the digital heat stroke meters market is the escalating awareness concerning heat-induced illnesses and fatalities. With global temperatures on the rise due to climate change, heat waves are becoming more frequent and severe. This alarming trend has propelled the need for effective heat monitoring devices, especially in regions prone to extreme temperatures. Moreover, the healthcare sector is witnessing a surge in demand for such devices to prevent heat strokes and related complications, thereby boosting market growth.

Another pivotal factor driving market expansion is the technological advancements in wearable devices and digital health monitoring equipment. Modern digital heat stroke meters are increasingly integrated with smart technologies, enabling real-time data monitoring and reporting. These devices can now connect to smartphones and cloud platforms, offering users and healthcare providers instant access to critical health data, which significantly enhances early intervention and treatment outcomes.

Furthermore, the growing emphasis on employee safety in industrial and military settings is contributing to the robust demand for digital heat stroke meters. In industries such as manufacturing, construction, and defense, workers are often exposed to high temperatures, which can lead to heat-related illnesses. The adoption of digital heat stroke meters in these environments ensures continuous monitoring of workers' health, thereby reducing the risk of heatstroke and enhancing overall workplace safety.

Regionally, North America is expected to dominate the digital heat stroke meters market, owing to the high adoption rate of advanced healthcare technologies and the presence of prominent market players. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, primarily due to the increasing awareness of heat-related illnesses and the growing investments in healthcare infrastructure. The rising temperature trends in countries like India and Australia further underscore the need for effective heat monitoring solutions in the region.

Product Type Analysis

The digital heat stroke meters market is segmented into handheld and stationary devices. Handheld digital heat stroke meters are witnessing significant demand due to their portability and ease of use. These devices are particularly useful in field settings where quick and accurate temperature readings are essential. Their compact design and user-friendly interface make them popular among sports professionals, healthcare providers, and military personnel who require immediate, on-the-go heat monitoring solutions.

Stationary digital heat stroke meters, on the other hand, are typically employed in fixed locations such as hospitals, clinics, and industrial facilities. These devices offer high-precision temperature monitoring and are often integrated with advanced features such as data logging and remote monitoring. Industrial facilities, in particular, rely on stationary meters to maintain a safe working environment by continuously monitoring ambient conditions and ensuring compliance with occupational safety standards.

The choice between handheld and stationary devices often depends on the specific application and setting. For instance, sports organizations and military units prefer handheld meters for their flexibility and convenience, whereas hospitals and industrial facilities may opt for stationary models for their reliability and advanced functionalities. Both segments, however, are expected to contribute significantly to market growth, driven by the overarching need for effective heat monitoring solutions.

Additionally, ongoing innovations in product design and technology are further enhancing the appeal of both handheld and stationary digital heat stroke meters. Manufacturers are focusing on integrating features such as wireless connectivity, long battery life, and user-friendly interfaces to

In 2024, the heat wave that hit the Saudi Arabia was the natural disaster with the highest number of fatalities worldwide with a death count of 1,301. Another 1,197 fatalities were recorded in Afghanistan after the same earthquake.

The data collection follows the PRISMA standards of medical systematic review and meta-analysis. Firstly, use health consequences such as "disease" and "death", as well as pathogenic factors such as "high temperature" and "heat waves", And the city Search the main literature databases using a range of Chinese and English keywords, including PubMed, Embase, Scopus, and Web of Science. After removing duplicates, read the title and abstract to obtain the full text that meets the screening criteria for thorough reading. After further screening, extract disease and death risk data caused by high temperature and heat waves from the articles that meet the criteria. The above process was independently conducted and compared by two researchers. The results of data organization were based on Merge according to the type of disease and city.

A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office generally uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer, and have historically been associated with health problems and an increase in mortality.

The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. For an example of an urban heat island map during an average summer, see this dataset. For an example of an urban heat island map during a warm summer, see this dataset.

As well as outdoor temperature, an individual’s heat exposure may also depend on the type of building they are inside, if indoors. Indoor temperature exposure may depend on a number of characteristics, such as the building geometry, construction materials, window sizes, and the ability to add extra ventilation. It is also known that people have different vulnerabilities to heat, with some more prone to negative health issues when exposed to high temperatures.

This Triple Jeopardy dataset combines:

1. Urban Heat Island information for London, based on the 55 days between May 26th -July 19th 2006, where the last four days were considered a heatwave
2. An estimate of the indoor temperatures for individual dwellings in London across this time period
3. Population age, as a proxy for heat vulnerability, and distribution

From this, local levels of heat-related mortality were estimated using a mortality model derived from epidemiological data.

The dataset comprises four layers:

• Ind_Temp_A – indoor Temperature Anomaly is the difference in degrees Celsius between the estimated indoor temperatures for dwellings and the average indoor temperature estimate for the whole of London, averaged by ward. Positive numbers show dwellings with a greater tendency to overheat in comparison with the London average
• HeatMortpM – total estimated mortality due to heat (outdoor and indoor) per million population over the entire 55 day period, inclusive of age effects
• HeatMorUHI – estimated mortality per million population due to increased outdoor temperature exposure caused by the UHI over the 55 day period (excluding the effect of overheating housing), inclusive of age effects
• HeatMorInd - estimated mortality per million population due to increased temperature exposure caused by heat-vulnerable dwellings (excluding the effect of the UHI) over the 55 day period, inclusive of age effects More information is on this website and in the Triple Jeopardy leaflet.

The maps are also available as one combined PDF. More information is on this website and in the Triple Jeopardy leaflet.