This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

My ArcGIS StoryMap is centered around The Green Book, an annual travel guide that allowed African Americans to travel safely during the height of the Jim Crow Era in the United States. More specifically, The Green Book listed establishments, such as hotels and restaurants, that would openly accept and welcome black customers into their businesses. As someone who is interested in the intersection between STEM and the humanities, I wanted to utilize The Science of Where to formulate a project that would reveal important historical implications to the public. Therefore, my overarching goal was to map each location in The Green Book in order to draw significant conclusions regarding racial segregation in one of the largest cities in the entire world.Although a more detailed methodology of my work can be found in the project itself, the following is a step by step walkthrough of my overall scientific process:Develop a question in relation to The Green Book to be solved through the completion of the project.Perform background research on The Green Book to gain a more comprehensive understanding of the subject matter.Formulate a hypothesis that answers the proposed question based on the background research.Transcribe names and addresses for each of the hotel listings in The Green Book into a comma separated values file.Transcribe names and addresses for each of the restaurants listings in The Green Book into a comma separated values file.Repeat Steps 4 and 5 for the 1940, 1950, 1960, and 1966 publications of The Green Book. In total, there should be eight unique database files (1940 New York City Hotels, 1940 New York City Restaurants, 1950 New York City Hotels, 1950 New York City Restaurants, 1960 New York City Hotels, 1960 New York City Restaurants, 1966 New York City Hotels, and 1966 New York City Restaurants.)Construct an address locator that references a New York City street base map to plot the information from the databases in Step 6 as points on a map.Manually plot locations that the address locator did not automatically match on the map.Repeat Steps 7 and 8 for all eight database files.Find and match the point locations for each listing in The Green Book with historical photographs.Generate a map tour using the geotagged images for each point from Step 10.Create a point density heat map for the locations in all eight database files.Research and obtain professional and historically accurate racial demographic data for New York City during the same time period as when The Green Book was published.Generate a hot spot map of the black population percentage using the demographic data.Analyze any geospatial trends between the point density heat maps for The Green Book and the black population percentage hot spot maps from the demographic data.Research and obtain professional and historically accurate redlining data for New York City during the same time period as when The Green Book was published.Overlay the points from The Green Book listings from Step 9 on top of the redlining shapefile.Count the number of point features completely located within each redlining zone ranking utilizing the spatial join tool.Plot the data recorded from Step 18 in the form of graphs.Analyze any geospatial trends between the listings for The Green Book and its location relative to the redlining ranking zones.Draw conclusions from the analyses in Steps 15 and 20 to present a justifiable rationale for the results._Student Generated Maps:New York City Pin Location Maphttps://arcg.is/15i4nj1940 New York City Hotels Maphttps://arcg.is/WuXeq1940 New York City Restaurants Maphttps://arcg.is/L4aqq1950 New York City Hotels Maphttps://arcg.is/1CvTGj1950 New York City Restaurants Maphttps://arcg.is/0iSG4r1960 New York City Hotels Maphttps://arcg.is/1DOzeT1960 New York City Restaurants Maphttps://arcg.is/1rWKTj1966 New York City Hotels Maphttps://arcg.is/4PjOK1966 New York City Restaurants Maphttps://arcg.is/1zyDTv11930s Manhattan Black Population Percentage Enumeration District Maphttps://arcg.is/1rKSzz1930s Manhattan Black Population Percentage Hot Spot Map (Same as Previous)https://arcg.is/1rKSzz1940 Hotels Point Density Heat Maphttps://arcg.is/jD1Ki1940 Restaurants Point Density Heat Maphttps://arcg.is/1aKbTS1940 Hotels Redlining Maphttps://arcg.is/8b10y1940 Restaurants Redlining Maphttps://arcg.is/9WrXv1950 Hotels Redlining Maphttps://arcg.is/ruGiP1950 Restaurants Redlining Maphttps://arcg.is/0qzfvC01960 Hotels Redlining Maphttps://arcg.is/1KTHLK01960 Restaurants Redlining Maphttps://arcg.is/0jiu9q1966 Hotels Redlining Maphttps://arcg.is/PXKn41966 Restaurants Redlining Maphttps://arcg.is/uCD05_Bibliography:Image Credits (In Order of Appearance)Header/Thumbnail Image:Student Generated Collage (Created Using Pictures from the Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library, https://digitalcollections.nypl.org/collections/the-green-book#/?tab=about.)Mob Violence Image:Kelley, Robert W. “A Mob Rocks an out of State Car Passing.” Life Magazine, www.life.com/history/school-integration-clinton-history, The Green Book Example Image:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library Digital Collections, https://images.nypl.org/index.php?id=5207583&t=w. 1940s Borough of Manhattan Hotels and Restaurants Photographs:“Manhattan 1940s Tax Photos.” NYC Municipal Archives Collections, The New York City Department of Records & Information Services, https://nycma.lunaimaging.com/luna/servlet/NYCMA~5~5?cic=NYCMA~5~5.Figure 1:Student Generated GraphFigure 2:Student Generated GraphFigure 3:Student Generated GraphGIS DataThe Green Book Database:Student Generated (See Above)The Green Book Listings Maps:Student Generated (See Above)The Green Book Point Density Heat Maps:Student Generated (See Above)The Green Book Road Trip Map:Student GeneratedLION New York City Single Line Street Base Map:https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page 1930s Manhattan Census Data:https://s4.ad.brown.edu/Projects/UTP2/ncities.htm Mapping Inequality Redlining Data:https://dsl.richmond.edu/panorama/redlining/#loc=12/40.794/-74.072&city=manhattan-ny&text=downloads 1940 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1940" The New York Public Library Digital Collections, 1940, https://digitalcollections.nypl.org/items/dc858e50-83d3-0132-2266-58d385a7b928. 1950 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1950" The New York Public Library Digital Collections, 1950, https://digitalcollections.nypl.org/items/283a7180-87c6-0132-13e6-58d385a7b928. 1960 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Travelers' Green Book: 1960" The New York Public Library Digital Collections, 1960, https://digitalcollections.nypl.org/items/a7bf74e0-9427-0132-17bf-58d385a7b928. 1966 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "Travelers' Green Book: 1966-67 International Edition" The New York Public Library Digital Collections, 1966, https://digitalcollections.nypl.org/items/27516920-8308-0132-5063-58d385a7bbd0. Hyperlink Credits (In Order of Appearance)Referenced Hyperlink #1: Coen, Ross. “Sundown Towns.” Black Past, 23 Aug. 2020, blackpast.org/african-american-history/sundown-towns.Referenced Hyperlink #2: Foster, Mark S. “In the Face of ‘Jim Crow’: Prosperous Blacks and Vacations, Travel and Outdoor Leisure, 1890-1945.” The Journal of Negro History, vol. 84, no. 2, 1999, pp. 130–149., doi:10.2307/2649043. Referenced Hyperlink #3:Driskell, Jay. “An Atlas of Self-Reliance: The Negro Motorist's Green Book (1937-1964).” National Museum of American History, Smithsonian Institution, 30 July 2015, americanhistory.si.edu/blog/negro-motorists-green-book. Referenced Hyperlink #4:Kahn, Eve M. “The 'Green Book' Legacy, a Beacon for Black Travelers.” The New York Times, The New York Times, 6 Aug. 2015, www.nytimes.com/2015/08/07/arts/design/the-green-book-legacy-a-beacon-for-black-travelers.html. Referenced Hyperlink #5:Giorgis, Hannah. “The Documentary Highlighting the Real 'Green Book'.” The Atlantic, Atlantic Media Company, 25 Feb. 2019, www.theatlantic.com/entertainment/archive/2019/02/real-green-book-preserving-stories-of-jim-crow-era-travel/583294/. Referenced Hyperlink #6:Staples, Brent. “Traveling While Black: The Green Book's Black History.” The New York Times, The New York Times, 25 Jan. 2019, www.nytimes.com/2019/01/25/opinion/green-book-black-travel.html. Referenced Hyperlink #7:Pollak, Michael. “How Official Is Official?” The New York Times, The New York Times, 15 Oct. 2010, www.nytimes.com/2010/10/17/nyregion/17fyi.html. Referenced Hyperlink #8:“New Name: Avenue Becomes a Boulevard.” The New York Times, The New York Times, 22 Oct. 1987, www.nytimes.com/1987/10/22/nyregion/new-name-avenue-becomes-a-boulevard.html. Referenced Hyperlink #9:Norris, Frank. “Racial Dynamism in Los Angeles, 1900–1964.” Southern California Quarterly, vol. 99, no. 3, 2017, pp. 251–289., doi:10.1525/scq.2017.99.3.251. Referenced Hyperlink #10:Shertzer, Allison, et al. Urban Transition Historical GIS Project, 2016, https://s4.ad.brown.edu/Projects/UTP2/ncities.htm. Referenced Hyperlink #11:Mitchell, Bruce. “HOLC ‘Redlining’ Maps: The Persistent Structure Of Segregation And Economic Inequality.” National Community Reinvestment Coalition, 20 Mar. 2018,

Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.

Heatmap of the five linkage groups. (RAR 151 kb)

Ipsos Global @dvisor wave 67 was conducted from February 23 - March 6, 2015. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo; EI: Political Heat Map; EK: Tech Tracker.

AI Ambulance Demand Heat-Map Planner Market Outlook

As per our latest research, the global AI Ambulance Demand Heat-Map Planner market size reached USD 627 million in 2024, reflecting the rapid adoption of artificial intelligence in emergency response systems. The market is projected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching a forecasted value of USD 2,068 million by 2033. The primary growth driver for this market is the increasing need for real-time data analytics and predictive modeling to optimize ambulance deployment and reduce emergency response times.

A key growth factor propelling the AI Ambulance Demand Heat-Map Planner market is the surging global urbanization, which has led to higher population densities and more complex city infrastructures. These conditions significantly heighten the demand for efficient emergency medical services, requiring advanced solutions that can dynamically allocate ambulance resources based on predictive analytics. AI-powered heat-map planners are transforming how emergency medical services (EMS) respond to incidents by analyzing vast datasets, including historical call data, traffic patterns, and demographic information, to forecast demand hotspots. This capability allows EMS providers to proactively position ambulances in anticipation of emergencies, substantially reducing response times and improving patient outcomes. Additionally, the integration of AI with IoT and real-time GPS tracking further enhances the precision and reliability of these solutions, making them invaluable for modern urban environments.

Another significant driver is the increasing governmental emphasis on public health and safety, which has resulted in enhanced funding for digital transformation within healthcare and emergency response sectors. Governments and municipal bodies across the globe are investing heavily in smart city initiatives, where AI-based ambulance demand heat-map planners play a critical role in optimizing resource allocation. These investments are not only aimed at improving operational efficiency but also at achieving better regulatory compliance and meeting stringent response time targets set by health authorities. The COVID-19 pandemic further accelerated this trend, as healthcare systems worldwide recognized the need for scalable, data-driven solutions to manage surges in emergency calls and optimize ambulance fleet deployment during crises.

Technological advancements in machine learning, big data analytics, and cloud computing are further catalyzing the growth of the AI Ambulance Demand Heat-Map Planner market. Modern AI algorithms can process and learn from massive, heterogeneous data sources, providing actionable insights that were previously unattainable. The adoption of cloud-based platforms has democratized access to these advanced analytics tools, enabling even mid-sized hospitals and regional EMS providers to benefit from AI-driven planning solutions. Furthermore, ongoing research and development activities are enhancing the accuracy and scalability of these systems, allowing for customization according to local needs and regulations. This technological evolution is expected to continue driving market expansion over the forecast period.

From a regional perspective, North America currently dominates the AI Ambulance Demand Heat-Map Planner market, owing to its advanced healthcare infrastructure, significant investments in AI technologies, and supportive regulatory environment. Europe follows closely, with growing adoption in countries such as Germany, the UK, and France, driven by robust healthcare systems and increasing focus on smart city initiatives. The Asia Pacific region is expected to witness the fastest growth, fueled by rapid urbanization, expanding healthcare networks, and rising government investments in digital health. Latin America and the Middle East & Africa are also emerging as promising markets due to increasing awareness of the benefits of AI in emergency response and gradual improvements in healthcare infrastructure.

Component Analysis

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Ipsos Global @dvisor wave 55 was conducted from March 4 - March 18, 2014. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, IU: Socialogue, IV: European Sentiment Questions, EI: Political Heat Map, EJ: Health and Wellness, EK: Tech Tracker

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on NAVTEQ streets, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...assuming they have access to a car. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more. Populations in poverty are represented by taking the block group poverty rate (e.g. 10%) from the Census and symbolizing each block in that block group based on that percentage. Demographic data from U.S. Census 2010 and Esri Business location from infoUSAData sources: see this map package.

What is heat vulnerability? Vulnerability to heat is how likely a person is to be injured or harmed during periods of hot weather. Heat vulnerability has been linked to individuals’ characteristics (health status, age, race, income, language spoken, etc.) as well as certain aspects of the community where one lives (environment, community demographics). These characteristics or “heat vulnerability factors” can play an important role in one’s ability to adapt to heat. What is the Heat Vulnerability Index? The effects of extreme heat on health can often be prevented. Heat-related deaths and illness are more common during the summer, especially in vulnerable populations. Since vulnerability and adaptability to extreme heat in New York State (NYS) is a growing concern, the New York State Department of Health (NYSDOH) created the Heat Vulnerability Index (HVI) to help local and state public health officials identify and map heatvulnerable areas and populations in NYS (excluding New York City which has its own HVI). The HVI can assist in directing adaptation resources based on characteristics of vulnerable populations in that community and can inform long-term heat-mitigation planning efforts in the community. The HVI can help local agencies make decisions to: set up cooling centers in rural and vulnerable areas where many do not have access to air-conditioning at home provide transportation to and from cooling centers in low income neighborhoods where there may not be public transportation or few people own vehicles include risk communication and alert messaging in multiple languages especially among communities with high proportions of people who do not understand English wellarrange home visits of people in high risk groups like the elderly living alone How was the HVI developed? The HVI was developed to identify census tracts with populations that may have increased heat vulnerability. It is based on thirteen environmental and socio-demographic heat vulnerability factors that were identified from previous studies. Census tracts are subdivisions of counties and are defined by the US Census Bureau to collect, provide and present statistical data. Census tract level information for these heat vulnerability factors was obtained from the 2006-2010 and 2008-2012 US Census Bureau American Community Surveys (ACS) and 2011 National Land Cover Database (NLCD) for 2,723 census tracts in NYS (excluding New York City). Census tracts with zero population or missing census tract data were excluded. The 13 factors were grouped into four categories that represent different aspects of heat vulnerability, which in turn were used to estimate the overall HVI for each census tract. The four heat vulnerability categories include 1) language vulnerability; 2) socio-economic vulnerability; 3) environmental and urban vulnerability; and 4) elderly isolation and elderly vulnerability. The HVI and four heat vulnerability categories were mapped to display populations in NYS that are most vulnerable to heat. More Information on HVI:Heat Vulnerability Index: Statewide and County HVI maps can be found at https://www.health.ny.gov/environmental/weather/vulnerability_index/index.htm For more information on the HVI: Nayak SG et al. Development of the heat Vulnerability Index. Public Health 2017. Open access at https://www.sciencedirect.com/science/article/pii/S003335061730327X

The data presented on this page concern the 2020-2022 mapping of temperature differences, the classification maps of these temperature differences (i.e. urban heat and freshness islands) and the map of the urban heat island intensity index. These different maps are detailed below: - The mapping of Temperature differences in °C represents the temperature difference in the city compared to a nearby forest. It was produced at the scale of the ecumene of Quebec (2021 census, 185,453 km2). This mapping, provided on a grid with a spatial resolution of 15 m, was carried out with a predictive machine learning model built on Landsat-8 satellite data provided by the *United States Geological Survey (USGS) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) _ was conducted for * population centers from the 2021 census * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which is added a 2 km buffer zone. It thus covers all major urban centers, i.e. 14,072 km2. The method for categorizing ICFUs is the ranking of predicted temperature differences for each population center into 9 levels. Classes 8 and 9 are considered Urban Heat Islands and classes 1, 2, and 3 as Urban Freshness Islands. The interval values for each class and population center are shown in the production metadata file. Since the surface temperatures were analyzed at the scale of the Quebec ecumene, but the classification intervals were calculated for each population center individually, the differences in temperature grouped into the different classes vary from one region to another. Thus, there are differences observed in the predicted temperature differences between North and South Quebec and according to urban realities. For example, a temperature difference of 2°C may be present in class 1 (cooler) in a population center located in southern Quebec, but may be present in class 9 (very hot) in a population center in northern Quebec. It is therefore important to interpret the identification of heat islands in relation to the relative temperature difference data produced at the Quebec ecumene scale. In addition to this map, the map of * Temperature variations for the urbanization perimeters of the smallest municipalities 2020-2022 * covers all the urbanization perimeters that are not (or only partially) covered by the ICFU map. Thus, the two maps put side by side allow a complete coverage of all population centers and urbanization perimeters in Quebec. - The _Urban Heat Island Intensity Index (SUHII) _ map _ represents the Surface Urban Heat Island Intensity (SUHII) index _ represents the Surface Urban Heat Island Intensity (SUHII) index. This index is calculated for each * dissemination island * (ID) of Statistics Canada included in the * 2021 census population centers * (CTRPOP) * () * (CTRPOP). It highlights areas with higher heat island intensity, by calculating a weighted average from the classes of temperature differences, giving more weight to the hottest classes. This weight is proportional to the class number (for example, a class 9 surface is 9 times more important in the index than the same area with a class 1). These maps as well as those of * 2013-2014 * are used for the * Analysis of change between the mapping of heat/freshness islands 2013-2014 and 2020-2022 *. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (version complete). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Regional Heat Vulnerability Map and Cooling Solutions

The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool.

Healthy Urban Environment (HUE) Initiative - Overview

HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies.

Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air quality issues intersect to produce negative health incomes that impact the region’s communities, particularly those who are most vulnerable and least able to adapt.

This work was funded as part of the Healthy Urban Environments (HUE) initiative by the Maricopa County Industrial Development Authority (MCIDA), Award #AWD00033817. This funding facilitated collaboration between the City of Tempe, the Decision Theater at Arizona State University (ASU) and ASU researchers to build an interactive webtool to assist local municipalities, nonprofits, community members, researchers, and other stakeholders in understanding heat, vulnerabilities, and solutions to heat in urban Maricopa County region.

Supplementary Table 2 associated with the manuscript "Accounting for diverse evolutionary forces reveals mosaic patterns of selection on human preterm birth loci"Supplementary Data 2: Heatmap z-score results in tabular format for Figure 2. Each row represents an independent sPTB associated region denoted by the lead SNP (column ‘rsid’) and corresponding z-score for evolutionary measures (columns).

The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.

The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions.

The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.

The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions.

To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values.

LST (°C) DI T2m (°C) <14 y.o. (%) >65 y.o (%) Buildings per sq. m.(BD)

1-Less risk <47.2 <28 <40.6 <23 <1 <66

2-Moderate risk 47.2 ≤ LST ≤ 47.9 28≤ DI ≤ 28.2 40.6 ≤ T2m ≤ 40.8 23≤ YP ≤28 1≤ OP ≤ 3 66≤ BD ≤ 109

3-High risk >47.9 >28.2 >40.8 >28 >3 >109

LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old;

Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)

Ipsos Global @dvisor wave 30 was conducted on February 7 and February 21, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, EH: Retail Confidence, EI: Political Heat Map, EJ: Health and Wellness, EK: Tech Tracker.

Explore this data in a series of maps here. The Vermont Heat Vulnerability Index draws together 17 different measures of vulnerability in six different themes: population, socioeconomic, health, environmental, climate, and heat illness. These measures are combined to measure the overall vulnerability of Vermont towns to heat-related events. This is a first step to identify populations that may be more vulnerable to extreme heat, however local knowledge should always be considered when it is available.Analytical and mapping methods are described in further detail in the Vermont Heat Vulnerability Assessment ReportData last updated 2016.Measures:Heat Vulnerability Measures Population Characteristics: 1. % population less than 5 years old 2. % population 65 years old or older Socioeconomic Characteristics: 3. % population living below Federal Poverty Line 4. % adult population with no high school diploma 5. % adults 65 and older living alone 6. % adult population with no health insurance Health Conditions: 7. % adults with diabetes 8. % adults with asthma 9. % adults with hypertension 10. % adults who are obese 11. % adults in fair or poor health 12. All-cause mortality, warm season deaths Environmental Characteristics: 13. Housing units per square mile 14. % covered with Impervious surface 15. % covered by forest canopy Climate Characteristics: 16. Average number of days per year 87° F or hotter Observed Heat Illness: 17. Heat-related emergency department visits

The Home Owners’ Loan Corporation (HOLC) was a New Deal era program that graded neighborhoods based on perceived loan risk, but largely based on immigrant status and populations of color. Affluent areas were often graded as “A” or “Best” due to the low perceived risk of loan default. The riskiest grade was “D” or “Hazardous” and were predominantly communities of color and immigrant neighborhoods. These practices, while banned in 1968, have been linked to significant and increasing economic and demographic disparities through time. We are now also finding that these redlined areas are also associated with more extreme urban heat island effects, and that this is likely due to their lack of tree canopy and greater impervious surface (things like asphalt and cement roads) percentage. A recent paper by Hoffman et al. (2020) has connected these borrowing practices with the resulting impacts on local climate impacts along with human health. This map includes the following information for U.S. city neighborhoods:HOLC Grade (from the University of Richmond Digital Scholarship Lab)Average land surface temperature difference from citywide HOLC normal (reported in Hoffman et al., 2020)Tree cover percentage (from the National Land Cover Database)Impervious surface percentage (from the National Land Cover Database)Demographic information (from the American Community Survey)

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: 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 An estimate of the indoor temperatures for individual dwellings in London across this time period 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.

Orange indicates a positive coefficient and blue indicates a negative coefficient (in both cases, where the 95% credible intervals of the posterior parameter estimate do not cross 0). White indicates the coefficient was neither positive nor negative (i.e., posterior credible intervals cross 0). An equivalent table with the parameter values can be found in the supplement (Section 4 in S2 Appendix); (m) indicates that the parameter is the coefficient for males. Fluoroquinolone resistance definitions varied between species (S1 Appendix). MRSA primarily indicates oxacillin or cefoxitin resistance, but other markers are accepted for oxacillin, if oxacillin was not reported. See protocol for details [40].

Urban Heat Island images:MorningAfternoonEveningTacoma Heat Island StudyData collected on 7/25/2018, collected by Dr. Vivek Shandas, Capa StrategiesWhat Earth Economics is working on:Through grant funding, Earth Economics is working on building out an approach and methodology using Urban Heat Island modeling (LANDSAT data) to assume health impacts (mortality rates) on a census tract level, using research on how demographics and UHI impact community health outcomes.Variables:Name: Census Block Group NamePop: Census Block Group populationIncome: Average individual Census Block Group level annual incomeOver 65: Population over age 65Under14: Population under age 14AF: Afternoon temperature (C), averaged to Census Block Group (July 25, 2018). Data collected by Dr. Vivek Shandas using this methodologyPm: Evening temperature (C), averaged to Census Block Group (July 25, 2018)Combtemp: Average of evening and afternoon temperatureHighRiskAgeGroup: Percent of population in a high risk age group for heat related illness (over age 65 and under age 14)Density: Population DensityCity of Tacoma Contact: Vanessa Simpson, Senior Technical GIS Analyst, Environmental Servicesvsimpson@cityoftacoma.org

Heat map chart of frequencies and overall percentages of publications jointly reporting outcome measures, by population type vaccinated.