Heat Vulnerability Index Research Articles

Quantification of heat vulnerability using system dynamics

One of the major climate threats is extreme heat events, as they pose significant risks to public health that are well documented in the epidemiologic literature. The effects of extreme heat events have been evident over the past years by several extreme heat events worldwide. With the growing concerns of future heat exposure, numerous studies in the literature have developed heat vulnerability indices based on determinants that have heat-related impacts. However, there has been limited guidance on heat vulnerability assessment that accounts for the impacts of the characteristics of the built environment and changes in population dynamics over time. This paper focuses on developing the methodology for heat vulnerability assessment in urban areas using System Dynamics (SD) based on integrating three levels of the physical urban environment: the urban level, the building level, and the human adaptive capacity to heat exposure. We examine the viability of using SD modeling as an approach to examine the key drivers in heat vulnerability assessment in urban areas. Thus, the paper assesses the dynamic relationship between heat vulnerability components, namely, Susceptibility, Exposure, Coping Capacity, and Adaptive Capacity, and their effect on increased or decreased vulnerability under extreme heat events. The paper concludes with an applied case study in Cairo, Egypt, to test the use of the SD approach in assessing heat vulnerability in urban settings. Results from the proposed SD model confirm the underlying hypothesis that vulnerability from heat exposure is dynamically linked to the coping and adaptive capacity of the surrounding built environment with the urban population’s socioeconomic characteristics. The main contribution of this approach is that it allows for parallel examination of the effect of the human system that simulation models cannot include and the performance of the built environment system that epidemic heat vulnerability studies cannot capture.

Residential and Race/Ethnicity Disparities in Heat Vulnerability in the United States.

Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) "redlining" grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non-disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades "A"-"D," respectively. CEJST-defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non-disadvantaged tracts (16.68). The non-Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place- and race/ethnicity-based disparities in heat vulnerability.

Characteristics of Households' Vulnerability to Extreme Heat: An Analytical Cross-Sectional Study from India.

High ambient temperature is a key public health problem, as it is linked to high heat-related morbidity and mortality. We intended to recognize the characteristics connected to heat vulnerability and the coping practices among Indian urbanites of Angul and Kolkata. In 2020, a cross-sectional design was applied to 500 households (HHs) each in Angul and Kolkata. Information was gathered on various characteristics including sociodemographics, household, exposure, sensitivity, and coping practices regarding heat and summer heat illness history, and these characteristics led to the computation of a heat vulnerability index (HVI). Bivariate and multivariable logistic regression analyses were used with HVI as the outcome variable to identify the determinants of high vulnerability to heat. The results show that some common and some different factors are responsible for determining the heat vulnerability of a household across different cities. For Angul, the factors that influence vulnerability are a greater number of rooms in houses, the use of cooling methods such as air conditioning, having comorbid conditions, the gender of the household head, and distance from nearby a primary health centre (PHC). For Kolkata, the factors are unemployment, income, the number of rooms, sleeping patterns, avoidance of nonvegetarian food, sources of water, comorbidities, and distance from a PHC. The study shows that every city has a different set of variables that influences vulnerability, and each factor should be considered in design plans to mitigate vulnerability to extreme heat.

IHVI: AN OPEN-SOURCE TOOLKIT FOR CONSTRUCTING INTEGRATED HEAT VULNERABILITY INDEX IN AUSTRALIA

Abstract. To tackle the increasing issue of heat risk in Australia and pressure of population growth, this project aimed to establish a first nationwide dynamic and interactive heat vulnerability assessment toolkit. The toolkit integrated multiple data sources, cloud computing, and Web GIS technologies to deliver cool intelligence for more heat resilient Australian cities and suburbs. A cloud-based open-source tool, iGEE, was first developed to derive land surface temperature (LST), NDBI (normalized difference built-up index) and NDVI (normalized difference vegetation index) from multiple satellite imagery on Google Earth Engine (GEE), a no-code web application allowing users to retrieve satellite data for a large area at fine scales. Following that, a python-based desktop app was then developed to calculate an integrated Heat Vulnerability Index (iHVI) for any cities and area at a fine scale of Statistical Area 1 (SA1). The iHVI toolkit allows users to construct heat sensitivity, heat adaptive capability indicators, and composite heat vulnerability index, which enables modelling of the relationships between heat, environmental and socioeconomic factors.

Association of Area-Level Heat and Social Vulnerability With Recurrent Hospitalizations Among Individuals With Rheumatic Conditions.

Climate and social vulnerability contribute to morbidity and health care utilization. We examined associations between the neighborhood Social Vulnerability Index (SVI) and the Heat Vulnerability Index (HVI) and recurrent hospitalizations among individuals with rheumatic conditions. Using a Massachusetts multihospital centralized clinical data repository, we identified individuals ≥18 years of age with a rheumatic condition who received rheumatology care within 3 years of April 2021. We defined the index date as 2 years before the last encounter and the baseline period as 1 year pre-index date. Addresses were geocoded and linked by census tract to the SVI and the HVI. We used multilevel, multinomial logistic regression to examine the odds of 1-3 and ≥4 hospitalizations (reference=0) over 2 years post index date by vulnerability index, adjusting for age, gender, race/ethnicity, insurance, and comorbidities. Among 14,401 individuals with rheumatic conditions, the mean ± age was 61.9 ± 15.7 years, 70% were female, 79% White, 7% Black, and 2% Hispanic. There were 8,251 hospitalizations; 11,649 individuals (81%) had 0 hospitalizations, 2,063 (14%) had 1-3, and 689 (5%) had ≥4. Adjusting for individual-level factors, individuals living in the highest versus lowest SVI areas had 1.84 times higher odds (95% confidence interval [95% CI] 1.43-2.36) of ≥4 hospitalizations. Individuals living in the highest versus lowest HVI areas had 1.64 times greater odds (95% CI 1.17-2.31) of ≥4 hospitalizations. Individuals with rheumatic conditions living in areas with high versus low social and heat vulnerability had significantly greater odds of recurrent hospitalizations. Studies are needed to determine modifiable factors to mitigate risks.

Extreme heat vulnerability assessment in Indonesia at the provincial level

Over the last decade, the inevitable rise in temperature has resulted in more extreme heat events in Indonesia, despite very little research being conducted to investigate the heat-related vulnerability of the exposed population. The extreme heat vulnerability index (EHVI) was evaluated in this study to identify the most vulnerable districts to extreme heat events. We used both physical and sociodemographic to assess exposure, population sensitivity, and adaptive capacity. A multivariate analysis was applied from eight indicators to determine the extreme heat vulnerability in the chosen region, namely North Sumatra, East Java and Bali. The EHVI was computed by adding the normalized heat indicators and factor scores from the multivariate analysis. Our findings show that the most vulnerable populations are concentrated in the urban and coastal regions. The sources of vulnerability differed among the selected areas, with urbanization and population density increasing vulnerability in urban areas. These findings are helpful in identifying districts vulnerable to extreme heat and assisting regulatory bodies in developing effective adaptation and preparedness strategies to increase population resilience to excessive heat.

Linkages between Typologies of Existing Urban Development Patterns and Human Vulnerability to Heat Stress in Lahore

The combined effects of global warming, urbanization, and demographic change influence climate risk for urban populations, particularly in metropolitan areas with developing economies. To inform climate change adaptation and spatial planning, it is important to study urban climatic hazards and populations at risk in relation to urban growth trends and development patterns. However, this relationship has not been adequately investigated in studies dedicated to climate vulnerability. This study identifies the typologies of development patterns within Lahore, Pakistan, investigates the heat vulnerability of residents at a neighborhood scale, and establishes a relationship between both of these factors. We identified urban clusters with diverse development patterns. Fourteen context- and site-specific indicators were selected to construct a human heat vulnerability index. Weighted sum, cluster analysis, and ANOVA test of variance were conducted to analyze the data. Our results demonstrate that development patterns significantly influence human vulnerability to heat stress, e.g., vulnerability is higher in older cities and undeveloped neighborhoods with less diverse land uses. These findings are essential for informing policy-makers, decision-makers and spatial planners about proactive adaptation planning in dynamic urban environments.

Theoretical fragility curves − A novel approach to assess heat vulnerability of residential buildings

Extreme weather events, including heatwaves, are escalating with the changing climate. High outdoor temperatures increase morbidity and mortality. Heat vulnerability indices (HVIs) tend to neglect or only simplistically consider the impact of the building in their analyses. This research aims to implement the reliability analysis used in other fields of civil engineering to study the overheating of buildings. In this method, the internal temperatures in different building types are determined with dynamic simulations. These results are used to create fragility curves. Historical weather data has also been used to produce the hazard curve. With these two, the overheating probability of buildings in a city can be analysed. Based on the methodology, the vulnerability of a district of Budapest was investigated as a pilot area. From the results, it can be seen that precast concrete panel buildings are the most vulnerable, while the least vulnerable category is the single-family house for an average internal temperature limit of 26 °C. In a sensitivity analysis, adaptation measures such as shading and enhanced natural ventilation uniformly reduced the overheating probability of the panel building. The methodology developed here makes it possible to incorporate the overheating of buildings more objectively in heat vulnerability analysis.

Heat Vulnerability Index Mapping: A Case Study of a Medium-Sized City (Amiens)

Urbanization, anthropogenic activities, and social determinants such as poverty and literacy rate greatly contribute to heat-related mortalities. The 2003 strong heat wave (Lucifer) in France resulted in catastrophic health consequences in the region that may be attributed to urbanization and other anthropogenic activities. Amiens is a medium-sized French city, where the average temperature has increased since the year 2000. In this study, we evaluated the Heat Vulnerability Index (HVI) in Amiens for extreme heat days recorded during three years (2018–2020). We used the principal component analysis (PCA) technique for fine-scale vulnerability mapping. The main types of considered data included (a) socioeconomic and demographic data, (b) air pollution, (c) land use and cover, (d) elderly heat illness, (e) social vulnerability, and (f) remote sensing data (land surface temperature (LST), mean elevation, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI)). The output maps identified the hot zones through comprehensive GIS analysis. The resultant maps showed that high HVI exists in three typical areas: (1) areas with dense population and low vegetation, (2) areas with artificial surfaces (built-up areas), and (3) industrial zones. Low-HVI areas are in natural landscapes such as rivers and grasslands. Our analysis can be implemented in other cities to highlight areas at high risk of extreme heat and air pollution.

Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area

The urban heat island (UHI) phenomenon gets intensified in the process of urbanization, which increases the vulnerability of urban dwellers to heatwaves. The UHI-induced vulnerability to heatwaves has increased in Bangladesh during past decades. Thus, this study aims to examine the UHI and vulnerability to heatwaves in the city of Dhaka using a heat vulnerability index (HVI). The HVI is constructed using various demographic, socioeconomic, and environmental risk variables at thana level. Principal component analysis (PCA) was applied to the 26 normalized variables for each of the 41 thanas of Dhaka to prepare the HVI. Result shows that more than 60% of the city is under built-up areas, while vegetation cover and water bodies are in low proportion. Analysis of HVI shows that the very high- and high-risk zones comprise 6 and 11 thanas, while low- and very low-risk zones comprise only 5 and 8 thanas. The correlation of HVI with variables such as exposure (0.62) and sensitivity (0.80) was found to be highly positive, while adaptive capacity had a negative correlation (−0.26) with the HVI. Findings of this study can be utilized in the mitigation of UHI phenomenon and maintaining the thermal comfort of Dhaka.

Heat vulnerability and extreme heat risk at the metropolitan scale: A case study of Taipei metropolitan area, Taiwan

Global climate change has intensified extreme climate events, such as torrential rain, heavy snow, heatwaves, and droughts. Among all extreme disaster events, extreme heat might lead to long and intensified heatwaves and seriously impact human living environments and ecological habitats. This study applies remote sensing to identify the spatial variation in extreme heat and principal component analysis (PCA) to explore the spatial distribution of the heat vulnerability index in the Taipei metropolitan area. Finally, a bivariate local indicator of spatial association (bivariate LISA) is used to explore the relationship between extreme heat and the heat vulnerability index. The results show that most vulnerable villages are clustered in downtown Taipei. In addition, the bivariate LISA results show an aggregation of 346 high-high clusters, indicating extreme heat and relatively high vulnerability clustered in particular areas. The findings of this study could provide the basis for proposing adequate resilience strategies for areas suffering from extreme heat based on the heat vulnerability composition.

A Heat Vulnerability Index: Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity for Urbanites of Four Cities of India.

Extreme heat and heat waves have been established as disasters which can lead to a great loss of life. Several studies over the years, both within and outside of India, have shown how extreme heat events lead to an overall increase in mortality. However, the impact of extreme heat, similar to other disasters, depends upon the vulnerability of the population. This study aims to assess the extreme heat vulnerability of the population of four cities with different characteristics across India. This cross-sectional study included 500 households from each city across the urban localities (both slum and non-slum) of Ongole in Andhra Pradesh, Karimnagar in Telangana, Kolkata in West Bengal and Angul in Odisha. Twenty-one indicators were used to construct a household vulnerability index to understand the vulnerability of the cities. The results have shown that the majority of the households fell under moderate to high vulnerability level across all the cities. Angul and Kolkata were found to be more highly vulnerable as compared to Ongole and Karimnagar. Further analysis also revealed that household vulnerability is more significantly related to adaptive capacity than sensitivity and exposure. Heat Vulnerability Index can help in identifying the vulnerable population and scaling up adaptive practices.

The relationship between population heat vulnerability and urbanization levels: A county-level modeling study across China.

The purpose of this work was to assess population vulnerability to heat-related health risks and its relationship with urbanization levels to provide essential information for the future development and policy-making for climate change adaptation. We constructed a heat vulnerability index (HVI), quantified the population heat vulnerability in each county across China by a principal component analysis (PCA) of multiple factors, and assessed urbanization levels in each county using multisource data. Then, the HVI was validated using the heat-attributable fraction (heat-AF) of nonaccidental mortality based on death monitoring data and meteorological data from 95 counties across China. The results showed that our HVI was significantly positively associated with the heat AF of nonaccidental mortality. A negative correlation was observed between the urbanization level and the HVI. The HVI was generally higher in less urbanized western China and lower in the more urbanized eastern regions. The baseline mortality occupies the top position in the importance ranking of the heat-vulnerability indicators at all three urbanization levels, but the other indicators, including the aging rate, agricultural population rate, education, ethnic structure, economic status, air conditioner ownership rate, and number of hospitals, ranked differently among different urbanization levels. This finding indicates that to reduce population heat vulnerability, the most important approach is to improve the health status of the whole population and reduce baseline mortality; additionally, regional-specific measures and emphasis should be adjusted reasonably along with the process of urbanization according to the characteristics and key factors of local heat vulnerability.

Mapping Heat Vulnerability Index Based on Different Urbanization Levels in Nebraska, USA

Heatwaves cause excess mortality and physiological impacts on humans throughout the world, and climate change will intensify and increase the frequency of heat events. Many adaptation and mitigation studies use spatial distribution of highly vulnerable local populations to inform heat reduction and response plans. However, most available heat vulnerability studies focus on urban areas with high heat intensification by Urban Heat Islands (UHIs). Rural areas encompass different environmental and socioeconomic issues that require alternate analyses of vulnerability. We categorized Nebraska census tracts into four urbanization levels, then conducted factor analyses on each group and captured different patterns of socioeconomic vulnerabilities among resultant Heat Vulnerability Indices (HVIs). While disability is the major component of HVI in two urbanized classes, lower education, and races other than white have higher contributions in HVI for the two rural classes. To account for environmental vulnerability of HVI, we considered different land type combinations for each urban class based on their percentage areas and their differences in heat intensifications. Our results demonstrate different combinations of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized sections. Less urbanized areas show no spatial clustering of HVI. More studies with separation on urbanization level of residence can give insights into different socioeconomic vulnerability patterns in rural and urban areas, while also identifying changes in environmental variables that better capture heat intensification in rural settings.

Optimal cooling shelter assignment during heat waves using real-time mobile-based floating population data

As the frequency, duration, and intensity of heat waves have been increasing in recent decades, the effective and efficient allocation of cooling shelters has become a significant issue in many cities. This study presents an integer programming model for allocating cooling shelters with the two conflicting objectives of maximizing coverage for the heat-vulnerable population and minimizing total operating cost of the cooling shelters. The temperature-humidity index is included in the model to reflect the weather conditions that affect heat waves. We also introduce data analysis procedures using real-time floating population data so as to track the hourly number and locations of individuals in the heat-vulnerable population. The proposed model is then validated with an application to Ulsan Metropolitan City in the Republic of Korea in which heat-vulnerable people are assigned to existing and potential cooling shelters. Given the condition of restricted budgets, we categorize and prioritize heat-vulnerable people into several groups using a clustering method and heat vulnerability index, and we suggest effective policy recommendations, so the most vulnerable people are provided cooling services first. In addition, we perform a sensitivity analysis on weather conditions, travel distance, electricity cost, and percentage of heat-vulnerable population served by cooling shelters, so policy makers can be prepared to respond quickly to the various factors that can change during a heat wave and ultimately reduce heat-related morbidity and mortality.

Extreme heat vulnerability assessment in tropical region: a case study in Malaysia

ABSTRACT In the past decade, the inevitable increase in temperature has caused Malaysia to experience more extreme heat events, and yet very little research has been dedicated in exploring the heat-related vulnerability of exposed population. In this study, the extreme heat vulnerability index (EHVI) has been evaluated to identify the most vulnerable districts to extreme heat events. We evaluated exposure, population sensitivity and adaptive capacity from sociodemographic and remote sensing data. We have applied multivariate analysis on 13 indicators for every 87 districts to elucidate the extreme heat vulnerability in Peninsular Malaysia. The EHVI was generated by summing up the normalized extreme heat exposure scores and factor scores from the multivariate analysis. Our findings clarify that the most vulnerable populations are confined in the urban and northern region of Peninsular Malaysia. The source of vulnerability varied between both regions, with urbanization and population density increase the vulnerability in urban areas, high heat exposure and sensitive population are the dominant factors of vulnerability in the northern region. These findings are valuable in identifying districts vulnerable to extreme heat and help regulatory body; in designing effective adaptation and preparedness strategies to increase the population resilience towards extreme heat.

Extreme heat vulnerability in Phoenix, Arizona: A comparison of all-hazard and hazard-specific indices with household experiences

Practitioners and researchers use vulnerability indices to understand the conditions that influence hazard risk. However, there has been little research on how well such indices depict household-scale vulnerability to specific hazards. We examined relationships between an all-hazard index and a hazard-specific index with household-level adaptive resources, adaptive behaviors, proximate sensitivities, and self-reported health outcomes related to extreme heat. Household measures were drawn from a stratified random sample survey conducted in Phoenix, Arizona, USA (n = 163). The results point to different experiences between households in more and less heat vulnerable areas. The largest differences between households stratified by the hazard-specific index (Heat Vulnerability Index) primarily involved adaptive resources and behaviors, whereas indicators of proximate sensitivity were more strongly differentiated by the all-hazard index (Social Vulnerability Index). Differences in health outcomes between more and less vulnerable neighborhoods were more evident using HVI than SoVI, although effect sizes using either index were small and confidence intervals were wide. The relationship between vulnerability indices and several survey measures varied across four study sites. The specific ways in which more and less vulnerable communities differ from one another varies based on the adaptation, sensitivity, or outcome measure of interest, location within the city, and choice of vulnerability index.

DTEx: A dynamic urban thermal exposure index based on human mobility patterns

BackgroundExtreme heat in light of climate change is increasingly threatening the health and comfort of urban residents. Understanding spatio-temporal patterns of heat exposure is a critical factor in directing mitigation measures. Current heat vulnerability indices provide insight into heat sensitivities within given communities but do not account for the dynamic nature of the human movement as people travel for different activities. Here, we present a new Dynamic urban Thermal Exposure index (DTEx) that captures the varying heat exposure within urban environments. MethodsWe developed the DTEx to understand human heat exposure patterns in a mid-sized city. This index incorporates the human movement pattern and the heat hazard pattern obtained via novel and advanced techniques. We generated the human movement pattern from large-scale, anonymized smartphone location data. The heat hazard patterns were extrapolated via machine learning models from air temperature data measured through vehicle-mounted sensors. The exposure index was then developed by combining the two parameters using their standard-deviation-classified indices. ResultsOur exposure index varied between 2 and 12, indicating low to high thermal exposures. Several high-temperature spots associated with a large volume of foot traffic are successfully identified through this DTEx. We observed the hottest spots at shopping plazas but not specifically in the urban center. During the selected football gameday, the exposure index surged across most places near the football stadium but was reduced considerably further away. DiscussionThe proposed DTEx is novel because it provides dynamic heat monitoring capability to facilitate heat mitigation strategies at vulnerable locations in urban environments. Combining the mobility data and extensive sensor data generates rich details on the most heat-exposed areas due to human congregation. Such information will be critical for risk communication and urban planning for policymakers. DTEx could also help smart route planning in sustainable cities to avoid heat hazards risks.

Determinants of Households’ Vulnerability to Extreme Heat: An Analytical Cross-Sectional Study from India

Background: High ambient temperature is a significant public health problem as it is associated with high heat related morbidity and mortality. We aimed to identify the factors associated with heat vulnerability, and the coping practices among Indian urbanites of Angul, and Kolkata.Methods: A cross-sectional study included 500 households (HHs) from each city with almost equal distribution of slum and non-slum HHs in the year 2020. Information was collected on sociodemographic, household characteristics, exposure, sensitivity and coping practices to heat and the heat illness history reported during the summer. Factors were computed to a Heat Vulnerability Index (HVI). Bi-variate and multi-variable logistic regression analysis used with HVI as the outcome variable to identify the determinants of high vulnerability to heat.Result: The results have shown that some common and some different factors are responsible for determining heat vulnerability of a household across different cities. For Angul, the factors which influence vulnerability are more number of rooms in houses, use of cooling methods like ACs, having co-morbid conditions, gender of household head and distance from nearby PHC. For Kolkata, the factors are unemployment, income, number of rooms, sleeping pattern, avoidance of non-veg food, sources of water, Co-morbidities and distance from the PHC.Conclusion: The study shows that every city has different set of variables that influence vulnerability and each factor should be considered for designing plans to mitigate vulnerability to extreme heat.

Health effects of heat vulnerability in Rio de Janeiro: a validation model for policy applications

Extreme heat events can lead to increased risk of heat-related deaths. Furthermore, urban areas are often hotter than their rural surroundings, exacerbating heat waves. Unfortunately, validation is difficult; to our knowledge, most validations, even if they control for temperatures, really only validate a social vulnerability index instead of a heat vulnerability index. Here we investigate how to construct and validate a heat vulnerability index given uncertainty ranges in data for the city of Rio de Janeiro. First, we compare excess deaths of certain types of circulatory diseases during heat waves. Second, we use demographic and environmental data and factor analysis to construct a set of unobserved factors and respective weightings related to heat vulnerability, including a Monte Carlo analysis to represent the uncertainty ranges assigned to the input data. Finally, we use distance to hospital and clinics and their health record data as an instrumental variable to validate our factors. We find that we can validate the Rio de Janeiro heat vulnerability index against excess deaths during heat waves; specifically, we use three types of regressions coupled with difference in difference calculations to show this is indeed a heat vulnerability index as opposed to a social vulnerability index. The factor analysis identifies two factors that contribute to >70% of the variability in the data; one socio-economic factor and one urban form factor. This suggests it is necessary to add a step to existing methods for validation of heat vulnerability indices, that of the difference-in-difference calculation.