Integrating GIS and the Urban Adaptation Assessment Framework for Climate Vulnerability and Resilience in Louisville, Kentucky

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Urban centers face compound risks such as flooding and extreme heat, disproportionately impacting socioeconomically marginalized communities. Despite recognition of these challenges, there remains a critical need for integrated, spatially detailed assessments to inform equitable adaptation strategies. This study represents the first application of the Urban Adaptation Assessment (UAA) tool in Louisville, Kentucky, integrating fine-scale spatial data with Geographic Information Systems to rigorously evaluate vulnerability to these dual threats. A multidimensional framework encompassing environmental exposure, social sensitivity, and adaptive capacity was employed, incorporating both equal and context-sensitive weighting schemes to reveal nuanced spatial patterns of risk. Our findings demonstrate a significant convergence of flood and heat vulnerabilities within historically underserved neighborhoods, including Shively, Pleasure Ridge Park, and Newburg, where deteriorating infrastructure, economic precarity, and limited healthcare access heighten climate risk. Notably, critical infrastructure such as hospitals and fire stations is disproportionately situated in these high-risk zones, potentially impeding emergency response effectiveness. This analysis elucidates the intersection of environmental hazards, infrastructure deficits, and social inequities at a granular spatial scale, providing a robust, policy-relevant foundation for advancing equitable urban adaptation strategies. Climate vulnerability Flood and heat risk social equity urban adaptation urban resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Recent years have witnessed a dramatic escalation in the frequency, intensity, and spatial reach of climate-induced disasters globally. Scientific evidence confirms a fivefold increase in extreme heat events since the 1960s (Thompson et al., 2022 ), while urban flooding has intensified due to the expansion of impervious surfaces (Gao et al., 2023 ). Land disturbance has further exacerbated these challenges by increasing sediment load that fill waterways and covered wetlands (Best, 2019 ), as well as replacing natural drainage with stormwater drains (Miller et al., 2014 ) In 2023, unprecedented flooding displaced thousands in Libya and Greece (UNDRR, 2025), while catastrophic rainfall submerged critical infrastructure in China and Pakistan, overwhelming drainage systems despite advanced urban planning (Shah et al., 2020 ). Concurrently, record-shattering heatwaves, amplified by atmospheric blocking patterns, scorched Europe, North America, and Asia, triggering higher mortality rates in cities where income inequality drives intra-urban thermal disparities (Shreevastava et al., 2025 ). Climate hazards are increasingly manifesting as cascading threats, where interactions between floods, heatwaves, and droughts amplify risks and overwhelm response systems. For example, in 2023, concurrent heat–drought crises across Asia, Europe, and other parts of the world disrupted agriculture, energy grids, and public health simultaneously. Such systemic failures reveal that conventional, hazard-specific mitigation approaches are insufficient for compound extremes. Moreover, their impacts remain socially uneven: marginalized communities face greater exposure during floods due to limited access to essential services (Balasuriya et al. 2023) and endure significantly higher temperatures, sometimes up to 6°C more, because of historic redlining and current income inequality (Shreevastava et al., 2025 ). This underscores the need for hyper-local, equity-centered vulnerability assessments that capture where environmental exposure intersects with infrastructural deficits, particularly in cities where inequality multiplies risk (IPCC, 2023 ; Jones et al., 2024 ). Urban areas are uniquely susceptible to climate extremes because of high population density, concentrated economic activity, and extensive built infrastructure (Wilhelmi & Hayden, 2010 ; Johnson & Munshi-South, 2017 ; Chapman et al., 2017 ; Santamouris, 2020 ). At the same time, cities are major contributors to climate change, responsible for a large share of global greenhouse gas emissions. Projections suggest an intensification of extreme urban events, including heatwaves and the urban heat island effect, posing increasing threats to vulnerable populations (Santamouris et al., 2020; Yuan et al., 2025 ; Kumar & Mishra, 2025 ). These dynamics highlight the dual role of cities as both drivers and victims of climate change, requiring resilience strategies that address local risk while contributing to broader mitigation efforts (Yeboah et al., 2025 ). Louisville, Kentucky, in the United States, exemplifies these challenges. The city faces intensifying heat island effects and recurrent flooding, contributing to property damage, public health risks, and economic strain (Stone et al., 2023). Impacts are unevenly distributed: elderly residents, children, low-income households, and individuals with pre-existing health conditions face disproportionately higher risks during extreme weather events due to limited adaptive resources (Lindsay et al., 2023 ; Leap et al., 2024 ; Noor et al., 2025 ). These realities confirm the importance of localized, equity-focused assessments of climate vulnerability. Numerous studies have demonstrated that capturing the multifaceted nature of resilience requires both quantitative and qualitative methodologies (Gallina et al., 2016 ; Jones & Tanner, 2017 ; Jufri et al., 2019 ; Elnagar et al., 2023 ; Xiong et al., 2023 ). Several models and studies have highlighted the complexities and limitations in assessing climate resilience. Key contributions highlight the significance of local knowledge (Bosher & Chmutina, 2017 ), community preparedness (Pfefferbaum et al., 2017 ; Norris et al., 2008 ), and structured indices (Cutter et al., 2014; Peacock et al., 2011 ), though these often face challenges like data quality issues and regional variability. Tools like the Resilience Capacity Index (RCI) (Wu et al., 2020 ), the Climate Change Adaptation Strategy Assessment Tool (CCASAT) (Li Liu et al., 2011 ), and the Urban Climate Resilience Framework (UCRF) (Baba et al., 2019 ) each offer valuable insights but fall short in areas such as socio-economic integration or hazard specificity. Collectively, the literature points to the urgent need for standardized, multi-dimensional, and adaptive assessment frameworks that integrate diverse data sources and capture resilience as a dynamic process (Sun et al., 2020 ; Meehl et al., 2000 ; Sharifi, 2020 ; Quinlan et al., 2016 ). Despite a growing body of literature on climate resilience and vulnerability assessment, key gaps remain. First, the Urban Adaptation Assessment (UAA) model has not previously been applied to Louisville, despite the city’s documented challenges with flooding and extreme heat. Second, existing UAA applications are typically conducted at the census tract level, offering a coarser spatial resolution. Third, there is a lack of studies that integrate critical infrastructure analysis into UAA-based vulnerability assessments. Finally, few studies provide a comparative evaluation using both equal and unequal weighting schemes to examine how indicator prioritization affects vulnerability outcomes. To address these gaps, this study applies the UAA and Geographic Information Systems (GIS) to Louisville, Kentucky. The UAA model, developed by the Notre Dame Global Adaptation Initiative (ND-GAIN, 2018a), assesses urban climate resilience across risk, readiness, and social equity, yielding an overall score that reflects a city’s capacity to adapt to hazards including heatwaves, flooding, drought, extreme cold, and sea-level rise (ND-GAIN, 2018a). GIS enables the integration of socio-economic, environmental, and infrastructure data for comprehensive, spatially explicit vulnerability analysis and targeted intervention planning (Sowmiya & Manimaran, 2024; Chakraborty et al., 2023 ). It supports near-real-time monitoring, reduces bias through consistent spatial data, and informs community-based planning (Kijewski-Correa et al., 2020 ; Li et al., 2024 ). Using UAA and GIS at the block group level, we assess Louisville’s vulnerability to climate hazards based on three components: exposure, sensitivity, and adaptive capacity, including critical infrastructure and alternative weighting schemes. 2. Methodology 2.1 Study area Louisville, Kentucky, was selected for this study due to its heightened vulnerability to climate-related hazards, notably extreme heat and flooding, which disproportionately impact marginalized and underserved communities. As the state’s largest city, with a population exceeding half a million (US Census Bureau, 2024), Louisville embodies a complex urban environment shaped by both geographical and socio-economic factors that make it crucial for climate resilience assessment. Geographically, Louisville lies within the relatively flat Ohio River Valley, which has historically supported urban expansion and agriculture. However, this topographic advantage also renders the city’s low-lying areas particularly prone to flooding during heavy rainfall events. Climatically, Louisville experiences a humid subtropical climate with hot, humid summers and mild winters. The city has an average growing season of 223 days and receives approximately 45 inches (1143 mm) of precipitation annually. Average temperatures range from 34°F (1°C) in January to 79°F (26°C) in July. The city suffers from a pronounced urban heat island (UHI) effect due to the high density of impervious surfaces, such as roads and buildings, which exacerbates temperature extremes and contributes to heightened heatwave risks, especially for vulnerable populations (Geos Institute, 2020 ; World Media Group, 2024). Louisville’s demographic landscape adds another layer of complexity. Children under five and adults over 65, who are particularly sensitive to extreme weather, constitute significant portions of the population. Nearly 15.1% of residents live below the poverty line, and about 17.8% face severe housing cost burdens (U.S. Census Bureau, 2024 ). Furthermore, 7.8% of households lack access to a vehicle, complicating evacuation and emergency response during crises (U.S. Census Bureau, 2024 ). In conclusion, Louisville's strategic location along the Ohio River has fostered its growth as a trade and transportation hub but also exposed it to significant environmental vulnerabilities, particularly in the face of climate change. Figure 1 shows the geographical location and the map of Louisville/ Jefferson County, Kentucky. 2.2 Data Collection To assess Louisville’s climate resilience, this study adopted the Urban Adaptation Assessment (UAA) framework developed by the University of Notre Dame. This model assesses urban climate vulnerability through three interrelated dimensions: exposure, sensitivity, and adaptive capacity, to offer a comprehensive analysis of a city’s resilience to hazards such as flooding and urban heat. As shown in Tables 1 and 2 , data was collected from multiple sources to support robust spatial and socio-economic analysis following the framework of the Urban Adaptation Assessment (UAA) model. The Census Block Groups were the smallest spatial units on which the data was collected to provide the most detailed analysis results. Spatial datasets including critical infrastructure (schools, hospitals, fire stations), transportation networks (rails and roads), environmental features (waterbodies, tree canopy, impervious surfaces), and hazard zones (floodplains, heat-affected areas) shapefiles were acquired from the Louisville/Jefferson County Information Consortium (LOJIC), the National Land Cover Database (NLCD), and FEMA to facilitate comprehensive analysis. Socioeconomic data at the census block group level, including indicators such as age distribution, public assistance rates, vehicle access, and housing age, were obtained from the National Historical Geographic Information System (NHGIS). All datasets were processed using ArcGIS Pro to align with the UAA model. Sensitivity indicators focused on vulnerable populations, children under five, elderly individuals living alone, and residents of older housing stock (built before 1979), which are more susceptible to heat, and flood impacts due to inadequate building standards compared to housing built before 1999. Flood exposure was mapped using FEMA flood zone data, while urban heat vulnerability was analyzed using health insurance data and 30-meter-resolution tree canopy coverage derived from NLCD. This integrative spatial analysis provided a nuanced, data-driven basis for identifying climate vulnerabilities across Louisville, informing targeted resilience strategies. Table 1 Data sources used for Flood Vulnerability Assessment Indicators Data for Flood Vulnerability Source Exposure Buildings in high-risk flood zone Cars in high-risk flood zone Population living in high-risk flood zone LOJIC NHGIS NHGIS Sensitivity Area of Impervious surfaces Buildings built before 1999 Households without access to a vehicle Population spending over 50% of income on rent Population of 65 years or older living alone Population under 5 years old Mobile homes LOJIC NHGIS NHGIS NHGIS NHGIS NHGIS NHGIS Adaptive Capacity Population with Health Insurance NHGIS Adapted from the Urban Adaptation Assessment methodology (ND-GAIN, https://gain.nd.edu/our-work/urban-adaptation/methodology/ ). Table 2 Data Sources of Urban Heat Vulnerability Assessment Indicators Data for Urban Heat Vulnerability Source Exposure Population Density NHGIS Sensitivity Building built before 1979 Households receiving public assistance Population spending over 50% of income on rent Population of 65 years or older living alone Population under 5 years old NHGIS NHGIS NHGIS NHGIS NHGIS Adaptive Capacity Tree canopy coverage Population with health insurance NLCD NHGIS Adapted from the Urban Adaptation Assessment methodology (ND-GAIN, https://gain.nd.edu/our-work/urban-adaptation/methodology/ ). This study employed a multi-layered spatial analysis using ArcGIS Pro to evaluate Louisville’s vulnerability to flooding and urban heat. Louisville/Jefferson County boundary was overlaid to ensure spatial consistency. Socioeconomic data stored in CSV format were joined to the spatial block group layer using a shared geographic identifier (GISJOIN), enabling the calculation of population, building, and vehicle counts within high-risk flood zones, as well as population density and impervious surface coverage in heat-prone areas. This integration allowed for a nuanced assessment of exposure, sensitivity, and adaptive capacity at the neighborhood level. 2.4 Vulnerability Framework Exposure refers to the extent to which populations, infrastructure, and the built environment are located within areas susceptible to climate hazards such as flooding or extreme heat. In this study, flood exposure was measured by identifying the spatial overlap between FEMA-designated flood zones and built features such as buildings and roads. Urban heat exposure was assessed using indicators like population density and the extent of impervious surfaces, both of which are closely linked to elevated heat retention and temperature extremes. Sensitivity captures the degree to which communities are likely to be affected by climate hazards due to underlying socio-demographic and housing characteristics. Key indicators of sensitivity included the presence of young children, elderly individuals living alone, households receiving public assistance, and older housing stock built before 1979. All of which can limit a community’s ability to withstand and recover from environmental stress. Adaptive capacity was measured through indicators such as access to healthcare, emergency services, transportation, and urban tree canopy coverage, which collectively influence a community’s ability to prepare for, respond to, and recover from climate-related impacts. Together, these three components provide a comprehensive framework for identifying climate vulnerabilities and informing targeted adaptation strategies. 2.5 Normalization Using Min-Max Scaling To standardize the range of all indicators used in the vulnerability assessment, Min-Max Scaling was employed. This normalization technique rescales data to a common scale between 0 and 1, allowing for consistent comparison across variables with differing units and magnitudes such as population density, total housing units, and percentage-based indicators. The normalized value for each indicator was calculated using the formula: $$\:Normalized\:value=\frac{x-xmin}{xmax-xmin}$$ 1 where: x is the original value, xₘ i ₙ is the minimum value in the dataset, and xₘₐₓ is the maximum value in the dataset. 2.6 Weighting To ensure a comprehensive evaluation of climate vulnerability, this study employed both Equal and Unequal Weighting methodologies. The Equal Weighting approach assigned equal importance to all indicators within the three core dimensions: Exposure, Sensitivity, and Adaptive Capacity, thereby offering a neutral, transparent baseline for comparing vulnerability across communities. While advantageous for its simplicity and consistency, this method assumes uniform influence across indicators, which may oversimplify complex, context-specific realities. To address this limitation, an Unequal Weighting approach was also implemented. In this method, greater weight was assigned to indicators with stronger relevance to flood and urban heat vulnerability, such as population exposure, ageing infrastructure, and healthcare accessibility. This context-sensitive framework aligns with established literature, which emphasizes the importance of locally tailored weights to reflect the varying impact of different variables (Cutter et al., 2008 Birkmann et al., 2015 Balica et al., 2012 ; ND GAIN, 2018b). Through the incorporation of both weighting schemes, the study achieved a balance between methodological objectivity and contextual realism. This dual approach enhances the robustness of the assessment of vulnerability, providing a more accurate and policy-relevant foundation for adaptation planning. Tables 3 and 4 show equal and unequal weights for assessing flood and urban heat vulnerability in Louisville. Table 3 Indicator Weights used in the Flood Vulnerability Assessment Indicators Data for Flood Vulnerability Equal Weight Unequal Weight Exposure Percent of buildings in high-risk flood zone Percent of cars in high-risk flood zone Percent of population living in high-risk flood zone 0.33 0.33 0.33 0.35 0.25 0.40 Sensitivity Percent of area that is impervious surface Percent of buildings built before 1999 Percent of households without access to a vehicle Percent of population spending over 50 percent of income on rent Percent of population that is 65 years or older living alone Percent of population that is under 5 years old Percent of total housing units that are mobile homes 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.20 0.20 0.15 0.15 0.10 0.10 0.10 Adaptive Capacity Percent of population with health insurance 1.00 1.00 Table 4 Indicator weight used in the Urban Heat Effect Assessment Indicators Data for Urban Heat Vulnerability Equal Weight Unequal Weight Exposure Population Density 1.00 1.00 Sensitivity Percent of buildings built before 1979 Percent of households receiving public assistance Percent of population spending over 50 percent of income on rent Percent of population that is 65 years or older living alone Percent of population that is under 5 years old 0.20 0.20 0.20 0.20 0.20 0.20 0.25 0.20 0.20 0.15 Adaptive Capacity Percent of tree canopy coverage Percent of population with health insurance 0.50 0.50 0.30 0.70 2.7 Calculation of Flood and Urban Heat Vulnerability Score To evaluate Louisville’s susceptibility to flooding and urban heat, composite scores for Exposure, Sensitivity, and Adaptive Capacity were calculated using both Equal and Unequal Weighting methods. These scores were then integrated to generate an overall vulnerability index, providing a nuanced assessment of climate risk across the city. Exposure, Sensitivity, and Adaptive Capacity scores were calculated by multiplying the weights with their corresponding standardized factor scores. For the flood hazards, the weights in Table 3 were multiplied with standardized measurements of contribution factors in Table 1 . For urban heat hazards, the weights in Table 4 were multiplied with standardized measurements of contribution factors in Table 2 . The Vulnerability Score for each hazard was calculated as follows. $$\:Vulnerability\:Score=\frac{Sensitivity\:score+(1-Adaptive\:capacity\:score)}{2}$$ 2 The calculation of vulnerability scores using both Equal and Unequal Weighting approaches provided a multifaceted understanding of Louisville’s susceptibility to flood and urban heat risks. Exposure scores reflected the degree to which infrastructure and populations were physically located in hazard-prone areas, with higher scores indicating a higher risk. Sensitivity scores captured the underlying social and demographic vulnerabilities, such as aging housing stock, income constraints, and the presence of at-risk groups (e.g., elderly or young children), while lower sensitivity scores denoted greater inherent resilience. Adaptive capacity scores measured the city’s ability to withstand and recover from extreme events, with higher values indicating more robust institutional and community support systems. Consistent with prior studies, vulnerability scores were normalized on a continuous scale ranging from 0 to 1, where 0 indicates low vulnerability (least susceptible) and 1 represents high vulnerability (most susceptible) (CDC/ATSDR, 2020; Meijer et al., 2023). This approach ensures that variations in vulnerability across spatial units are readily interpretable and suitable for guiding resource allocation and policy decisions aimed at reducing climate inequities. 2.8 Spatial Analysis of Critical Infrastructure in High-Risk Areas The final vulnerability scores were spatially mapped across Louisville’s census block groups using ArcGIS Pro, providing a visual representation of the city’s flood and urban heat risks. This mapping facilitated the identification of high-risk areas characterized by the convergence of high sensitivity and low adaptive capacity, marking them as particularly susceptible to climate-related hazards. To further analyze the exposure of critical infrastructure, the vulnerability map was overlaid with spatial layers representing essential facilities, including schools, hospitals, fire stations, and water treatment plants. ArcGIS Pro’s intersect and spatial join tools were employed to calculate the proportion of these assets located within high-risk flood zones. This integrative analysis enabled the identification of critical infrastructure that is most vulnerable to climate stressors. 3. Results The results revealed distinct geographic patterns of flood and urban heat vulnerability, particularly in zones where high sensitivity intersects with low adaptive capacity. These overlapping vulnerabilities highlight areas of heightened susceptibility to climate hazards and emphasize the need for targeted resilience interventions. 3.1 Result of Flood Exposure Assessment The assessment of flood exposure was conducted at the Block Group level, with all three indicators estimated based on the percentage of each block group’s area that fell within the high-risk flood zone. As a result, the outcomes for equal and unequal weights for flood exposure remained the same. The spatial distribution of flood exposure, depicted in Fig. 2 , was expressed as a continuous index ranging from 0.0 (lowest exposure) to 1.0 (highest exposure). Analysis revealed a clear concentration of high flood exposure in the western and southwestern portions of Louisville, particularly neighborhoods adjacent to the Ohio River and major water bodies, such as Shively, Valley Station, Pleasure Ridge Park, and parts of Downtown Louisville. These areas exhibited exposure index values between 0.7 and 1.0, reflecting their low-lying topography, proximity to floodplains, and the density of infrastructure within flood hazard zones. Conversely, the eastern and southeastern regions, including neighborhoods such as Middletown, Fern Creek, and Simpsonville, displayed low flood exposure values (0.1–0.3), consistent with their higher elevation and greater distance from major water bodies. Central Louisville exhibited mid-range exposure values (0.3–0.5), reflecting a heterogeneous mix of moderate floodplain presence and urban development intensity. 3.2 Result of Flood Sensitivity The resulting sensitivity scores, ranging from 0.0 to 0.6, are illustrated in Fig. 3 . The result using equal weighting revealed that block groups in the western and south-central parts of Louisville, particularly neighborhoods such as Pleasure Ridge Park, Shively, and parts of downtown, exhibited the highest sensitivity scores (0.5–0.6). These elevated sensitivity levels reflect the concentration of elderly populations, who face greater mobility and recovery challenges during disasters, as well as the prevalence of older housing stock, which is often more susceptible to flood damage due to outdated construction standards. By contrast, the northeastern and southeastern parts of the city, including areas near Jeffersontown and Mount Washington, displayed low sensitivity scores (0.2–0.3), reflecting stronger socioeconomic conditions and the presence of newer, more resilient housing. The unequal weighting approach refined the spatial identification of highly sensitive areas, resulting in a more concentrated pattern of sensitivity in low-income, socially vulnerable urban neighborhoods, while reducing sensitivity scores in some suburban areas. 3.3 Flood Adaptive Capacity Assessment Adaptive capacity in this study reflects community resilience based on health insurance coverage, a critical factor influencing disaster preparedness and recovery. Adaptive capacity scores ranged from 0.0 to 1.0, with lower scores indicating greater vulnerability due to limited access to healthcare resources. As illustrated in Fig. 4 , most block groups across Louisville exhibited moderate to high adaptive capacity, particularly in the eastern and southeastern neighborhoods, where scores ranged from 0.7 to 1.0. These areas tend to be more affluent and benefit from greater access to healthcare and associated resources. In contrast, clusters of low adaptive capacity (scores below 0.5) were concentrated in western and south-central Louisville neighborhoods, including Shively, Newburg, Pleasure Ridge Park, and Valley Station. These areas highlight communities where residents may face significant barriers to healthcare access, compounding their vulnerability to flood impacts. Since this assessment relied on a single indicator, health insurance coverage, and was conducted at the block group level, adaptive capacity scores did not differ between equal and unequal weighting methods. This consistency underscores persistent geographic disparities in healthcare access and highlights the need for targeted strategies to strengthen resilience by improving healthcare availability in historically underserved areas. 3.4 Flood Vulnerability Assessment Results Figure 5 depicts the spatial distribution of flood vulnerability across Louisville, generated using both equal-weighting and unequal-weighting approaches. The result from the equal-weighting approach, revealed the highest vulnerability values (0.4–1.0) predominantly in the western and south-central neighborhoods, including Pleasure Ridge Park, Shively, and Newburg. These areas exhibit a convergence of higher social sensitivity and limited adaptive capacity, aligning closely with regions of greater flood exposure. In contrast, the unequal weighting method prioritized indicators with stronger relevance to actual flood vulnerability, particularly social sensitivity and physical exposure. This weighting approach effectively accentuated the identification of high-risk zones, with vulnerability scores ranging from 0.4 to 0.8 in the urban core and west Louisville. These neighborhoods are characterized by disadvantaged populations, aging infrastructure, and reduced access to healthcare and transportation services. While both methods revealed similar spatial patterns, the unequal weighting method resulted in increased vulnerability measure for many of the census block groups. The color was darker almost across the study area. This distinction underscores the importance of context-sensitive weighting in vulnerability assessments and highlights its utility for informing equitable, targeted flood resilience strategies tailored to the most at-risk communities. 3.5 Critical Infrastructure Vulnerability in Louisville The result (Fig. 6 ) reveals that a substantial portion of Louisville’s critical infrastructure is situated in flood-prone areas, underscoring the city’s vulnerability to climate-related disruptions. Under the equal weighting method (Table 5 ), approximately 34% of roads, 52% of rail lines, 11% of schools, 21% of hospitals, and 29% of fire stations were located within flood risk areas. The significant presence of roads and rails in these areas raises concerns about transportation continuity and emergency mobility during flood events. Similarly, the exposure of hospitals and fire stations threatens the reliability of emergency response capabilities. In contrast, wastewater treatment plants are largely located along the Ohio River corridor but mostly outside the highest-risk flood polygons. Several facilities, particularly those near Valley Station and along the western riverfront, are positioned close to the river yet likely protected by engineered flood-control systems and topographic elevation. Their siting in comparatively lower-risk zones such as Jeffersontown and southeastern Louisville reflects planning to mitigate potential service disruptions and contamination. Overall, this distribution underscores both the spatial concentration of vulnerable infrastructure in low-lying western neighborhoods and the adaptive placement of key utilities that enhance the city’s overall flood resilience. Application of the unequal weighting method revealed notable shifts in the spatial exposure profile. The proportion of hospitals located in flood zones increased to 57%, and fire stations rose markedly to 71%, indicating the intersection of human vulnerability and emergency service exposure. Road network exposure also increased to 51%, while rail infrastructure exposure declined to 14%. Generally, all infrastructure categories except rail showed higher levels of flood exposure under the unequal-weighting approach, underscoring the heightened susceptibility of critical facilities and transportation corridors in vulnerable neighborhoods. Table 5 Critical infrastructure and flood-prone areas across Louisville–Jefferson County. Infrastructure Total Number or Length Equal Weight Unequal weight Number or length within flood risk zone % Number or length within flood-risk zone % School 249 28 11 108 43 Hospital 14 3 21 8 57 Fire Station 21 6 29 15 71 Waste Water Treatment Plant 5 0 0 1 20 Road 766.2km 263.9km 34 389.5km 51 Rail 761.0km 397.5km 52 106.1km 14 3.6 Urban Heat Vulnerability Assessment 3.6.1 Urban Heat Exposure Assessment Urban heat exposure was assessed using population density as the primary indicator, with exposure scores ranging from 0.0 (lowest exposure) to 1.0 (highest exposure), as depicted in Fig. 7 . The assessment was conducted at the Block Group level, with all three indicators estimated based on the percentage of each block group’s area that fell within the high-risk flood zone. As a result, the outcomes for urban heat exposure remained the same for both equal and unequal weights. Higher exposure values indicate areas where residents are more likely to experience elevated heat stress due to the concentration of human settlement. The highest exposure levels (0.8–1.0), represented by dark red zones on the map, were concentrated in Louisville’s urban core, particularly in densely populated neighborhoods such as Downtown and Jeffersontown. Moderate to high exposure zones (0.4–0.8) extended outward from the city center and included neighborhoods like Newburg, Shively, and Pleasure Ridge Park. In contrast, the eastern and southeastern parts of the county, encompassing Fern Creek, Middletown, and surrounding rural areas, exhibited low exposure scores (0.0–0.2), reflecting their lower population densities. 3.6.2 Results of Urban Heat Sensitivity Assessment The equal weighting method produced a broad sensitivity profile (Fig. 8 ), with elevated sensitivity scores (0.8–1.0) observed throughout central Louisville, Pleasure Ridge Park, Shively, and other urban centers. Moderate sensitivity extended into parts of southern Jefferson County, reflecting the widespread presence of at-risk populations across these urban neighborhoods. In contrast, the unequal weighting method yielded a more refined spatial distribution (Fig. 8 ), with high-sensitivity zones (0.6–1.0) becoming more concentrated in west Louisville, Pleasure Ridge Park, and selected segments of central and southern Louisville areas where disadvantaged populations and older, less resilient housing predominate. Despite these methodological differences, both approaches consistently identified urban cores, notably downtown Louisville, Pleasure Ridge Park, and Shively, as sensitivity hotspots. These neighborhoods face a convergence of aging housing stock, economically disadvantaged populations, and limited adaptive capacity. Conversely, suburban areas in the eastern and southeastern parts of the county, including Jeffersontown and Mount Washington, exhibited lower sensitivity scores (≤ 0.2), reflecting lower population densities and fewer socioeconomically vulnerable households. The similarity in overall spatial patterns confirms the persistent influence of key demographic and housing factors in shaping urban heat sensitivity. However, the unequal weighting method provided a more nuanced and policy-relevant depiction, better suited for guiding equity-focused adaptation strategies. 3.6.3 Urban Heat Adaptive Capacity Assessment The result shows a moderately varied spatial pattern across Jefferson County under the equal weighting approach, as shown in Fig. 9 . Very low adaptive capacity scores (0.0–0.4), represented by dark red areas, were concentrated in urban core neighborhoods such as downtown Louisville, Shively, and Pleasure Ridge Park, where limited tree cover and lower health insurance rates prevail. Moderate adaptive capacity scores (0.4–0.6) appeared across central neighborhoods, including Newburg, Okolona, and parts of Jeffersontown, reflecting slightly better access to green infrastructure and healthcare services. The unequal weighting method emphasized health insurance coverage as a more influential factor, recognizing the critical role of healthcare access in coping with heat-related stress. This approach yielded a more pronounced spatial differentiation: areas with low adaptive capacity expanded within west and south-central Louisville, while eastern and southeastern communities including Middletown, Jeffersontown, and areas near Mount Washington exhibited higher adaptive capacity scores (0.8–1.0) due to a combination of more extensive tree canopy and stronger healthcare access. While both methods revealed broadly similar geographic patterns, the unequal weighting approach offered sharper insights into health-vulnerable communities by highlighting locations where deficits in healthcare infrastructure and insurance coverage exacerbate sensitivity to heat stress. 3.6.4 Urban Heat Vulnerability Assessment Results Figure 10 illustrates the spatial distribution of urban heat vulnerability across Louisville, integrating sensitivity and adaptive capacity indicators under both equal-weighting and unequal-weighting methods. The equal weighting approach revealed widespread areas of high vulnerability (0.6–1.0), particularly concentrated in west and south-central Louisville, including neighborhoods such as Pleasure Ridge Park, Shively, and the urban core. These areas are characterized by limited green space, lower healthcare access, and heightened social vulnerability, including larger proportions of elderly residents and low-income households. The result of the unequal weighting approach (Fig. 10 ) highlighted a more focused pattern of vulnerability, reducing the prominence of moderate-risk suburban areas and sharpening the delineation of densely populated neighborhoods with significant socio-economic disadvantages. Vulnerability scores in many of these neighborhoods such as Pleasure Ridge Park, Newburg, and southwest Louisville exceeded 0.8, identifying them as key areas of concern. While both methods revealed broadly consistent spatial patterns, the unequal weighting approach provided greater contrast, allowing for a more precise identification of heat-vulnerable communities where gaps in social and health infrastructure exacerbate climate risk. In contrast, the equal weighting approach offered a more generalized vulnerability landscape that may mask important nuances in community needs. 4. Discussion This study presents a comprehensive assessment of climate vulnerability in Louisville, Kentucky, with a focus on the intersecting risks of flooding and urban heat. By integrating spatial exposure and social sensitivity through the Urban Adaptation Assessment (UAA) framework, the analysis moves beyond traditional, siloed hazard assessments (Gallina et al., 2016 ; Ahern, 2011 ) to capture the compounding nature of climate risks explicitly. This approach responds to recent calls in the climate adaptation literature for more holistic methodologies that account for interacting hazards and cascading impacts, especially within socially marginalized communities (IPCC, 2022; Zscheischler et al., 2018 ). The findings reveal that climate risk is not determined solely by environmental exposure but is significantly amplified by socioeconomic disadvantages and limited adaptive capacity. This underscores the critical importance of equity in urban resilience planning, reinforcing the need for targeted interventions that address both the physical and social dimensions of vulnerability (Anguelovski et al., 2016 ; Meerow & Newell, 2019 ). By highlighting the spatial and demographic patterns of risk, this study provides actionable insights for policymakers and planners seeking to build more resilient and equitable cities in the face of a changing climate. A key contribution of this research is the precise identification of Louisville neighborhoods where vulnerabilities to both flooding and urban heat intersect, namely Shively, Pleasure Ridge Park, Southside, Park Hill, and sections of downtown Louisville. These neighborhoods exhibit shared structural and demographic characteristics such as aging infrastructure, dense residential development, and higher proportions of low-income and minority populations that magnify hazard exposure and constrain adaptive capacity. The compounded risks and limited recovery resources in these areas establish them as critical hotspots for urban climate vulnerability. This spatial pattern resonates with evidence from climate vulnerability assessments in other U.S. cities, where structurally disadvantaged communities disproportionately shoulder cumulative climate risks due to build environment inadequacies and social inequities (Chakraborty et al., 2016 ; Hoffman et al., 2020 ). The findings underscore the urgent need for urban resilience strategies to evolve beyond isolated interventions addressing single hazards. Instead, they must adopt an integrated approach that acknowledges and targets the synergy of multiple interacting stressors, whether sequential or concurrent, that can escalate harm and perpetuate inequity (Raymond et al., 2020 ). Recognizing these compounding effects is vital for advancing equitable climate adaptation and disaster preparedness. By prioritizing interventions in neighborhoods identified as risk convergence zones, policymakers can more effectively reduce cumulative burdens and foster long-term resilience in the populations most vulnerable to climate extremes. The study elucidates the distinct and overlapping determinants of climate vulnerability for both flooding and heat hazards in Louisville. Flood sensitivity was most pronounced among populations experiencing elderly isolation, lack of vehicle access, and high impervious surface coverage conditions. These factors impede evacuation, increase direct exposure to floodwaters, and limit individual agencies during crisis events. These determinants align with prior research emphasizing how social isolation and infrastructural deficiencies amplify flood risk, particularly for those who may not be able to independently relocate or access emergency services (Fekete, 2009 ; Gallina et al., 2016 ). Conversely, heat vulnerability was substantially driven by limited tree canopy, elevated rent burden, and lack of health insurance. These factors contribute to increased exposure to extreme temperatures and undermine adaptive capacity due to both environmental conditions and socioeconomic barriers to securing adequate relief or healthcare. The absence of tree cover exacerbates the urban heat island effect, while financial strains and underinsurance restrict options for seeking cooling or medical assistance (Hoffman et al., 2020 ; Anguelovski et al., 2016 ). The study’s comparative analysis of equal versus unequal weighting methodologies for climate vulnerability assessment demonstrates that methodological choices significantly influence the identification and prioritization of at-risk populations and infrastructure. The equal weighting approach delivers a broad, generalized perspective, assigning uniform importance to all indicators and yielding vulnerability maps that may underrepresent the critical disparities faced by certain groups. In contrast, the unequal weighting scheme, which draws on established literature and expert consensus, amplifies the visibility of populations with fewer coping resources, leading to a more nuanced and equity-oriented assessment. This methodological divergence has direct consequences for resilience planning and infrastructure protection. Notably, under unequal weighting, the proportion of flood-vulnerable hospitals increased dramatically from 21% to 57%, and fire stations from 29% to 71%, revealing a substantial escalation in the recognized exposure of essential facilities when equity concerns are prioritized. Such findings echo longstanding concerns in infrastructure resilience research regarding the concentration of critical services in disadvantaged neighborhoods, where high hazard exposure and low adaptive capacity converge (Fiack et al., 2021 ; Meerow, 2020 ). While planning efforts, such as siting wastewater treatment plants outside of flood-prone zones, provide evidence of strategic risk mitigation, the ongoing vulnerability of schools, roads, and emergency services throughout the city underscores persistent shortcomings in equitable infrastructure protection. This points to the urgent need for targeted investments and adaptive strategies that address multi-hazard exposures and prioritize the resilience of facilities serving the most vulnerable populations (Yeboah et al., 2025 ). Despite its contributions, this study acknowledges several limitations that should inform the interpretation and application of its findings. First, the reliance on block–group–level data restricts spatial resolution, potentially overlooking critical vulnerability variations that emerge at the street, block, or parcel level, a limitation noted in recent methodological critiques (Hossain & Meng, 2020 ). This coarser scale may mask micro-spatial hotspots of vulnerability and hinder highly targeted intervention design. Second, the analysis is based on a static temporal snapshot, lacking longitudinal components that would capture evolving urban conditions, demographic shifts, changes in infrastructure, or future climate projections. Such dynamism is increasingly recognized as essential in resilience analysis, as ongoing processes of growth, migration, and climate change shape urban vulnerability. Third, while unequal weighting was grounded in empirical literature and expert input, the assignment of indicator weights inevitably introduces elements of subjectivity. Different prioritization choices may meaningfully influence both assessment outcomes and resulting policy recommendations (Fekete, 2009 ). Transparent documentation of these decisions is essential for critical appraisal and replication. Finally, the operationalization of adaptive capacity was necessarily constrained by data availability, resulting in the exclusion of essential resilience dimensions such as social cohesion, institutional readiness, and community preparedness (Aldrich & Meyer, 2015 ). These factors play a pivotal role in disaster response and recovery but are often difficult to quantify at granular spatial scales. Inclusion of such qualitative and dynamic indicators in future research would strengthen the comprehensiveness and actionability of vulnerability assessments. This study confirms the effectiveness of the Unified Assessment Approach (UAA) framework in advancing comprehensive climate vulnerability assessments at the city level. By tailoring the UAA to the unique spatial and demographic context of Louisville, the analysis yields a robust, data-driven basis for equitable resilience planning, pinpointing neighborhoods and populations where strategic interventions are most urgent. These localized insights directly inform policymakers and urban planners, enabling them to prioritize resources and design adaptation strategies that protect the most at-risk communities in the face of multiple, intersecting climate hazards. On a broader scale, the study’s findings contribute to the evolution of urban climate scholarship by illustrating the importance of moving beyond hazard-specific, siloed risk mapping toward integrated, equity-focused adaptation frameworks. The Louisville case study serves not only as a model for equitable vulnerability mapping but also reinforces the call for integrated resilience planning that anticipates complex risks in rapidly urbanizing settings. 5. Conclusion This study provides one of the first integrated assessments of flood and heat vulnerability in Louisville, Kentucky, applying the UAA framework and GIS to capture how environmental exposure, socio-demographic sensitivity, and adaptive capacity interact across neighborhoods. The analysis demonstrates that climate vulnerability is unevenly distributed, with western and south-central neighborhoods consistently emerging as multi-hazard hotspots due to overlapping physical, social, and infrastructural disadvantages. By contrast, eastern and southeastern neighbors benefit from stronger adaptive capacity through greater tree cover, higher socio-economic status, and improved access to healthcare. Findings call for multi-hazard strategies that address heat and flood together and for equity-centered adaptation that prioritizes urban greening, flood protection, and healthcare outreach in the most disadvantaged neighborhoods. The exposure of critical infrastructure (e.g., hospitals, emergency services) warrants systematic protection, retrofitting, or strategic relocation to ensure continuity during extremes. More broadly, the combined UAA–GIS approach offers a transferable template for localized, evidence-based planning under compound risks. Future work should incorporate temporal dynamics, compound-hazard modeling, and broader adaptive-capacity measures (e.g., social cohesion, institutional support) to enable more anticipatory, inclusive, and just resilience strategies Declarations Author Contribution J.AF: Conceptualization, methodology, data curation, formal analysis, visualization, and original draft preparation.R.Q. Z: Supervision, validation, review, and editing.C.O: Methodology, data, curation, review and editing. Acknowledgement The authors acknowledge the institutional and academic support provided by Murray State University and the Department of Earth and Environmental Sciences. 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18:59:19","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169187,"visible":true,"origin":"","legend":"","description":"","filename":"ac7503d90ea54f138acde87aea6ab1761structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/be4175438ac580ed04afb7a4.xml"},{"id":99007848,"identity":"c609f222-2432-4d83-a5bf-727fba6441bb","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180790,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/a436204ab8825a76edc2f192.html"},{"id":99007830,"identity":"27d77e91-f073-494e-8925-72a0d4c5c986","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361013,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Louisville–Jefferson County, Kentucky, illustrating major transportation networks and hydrological features, including the Ohio River and its channel. Data were projected to NAD 1983 StatePlane Kentucky FIPS 1600 (Meters) using boundary layers from the Louisville and Jefferson County Information Consortium Open data. Basemap is obtained from ESRI World Terrain Map. Map created using ArcGIS Pro\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/90bb1a0a6fc22f1f0e20c255.jpeg"},{"id":99312817,"identity":"55a400e5-b382-4ac1-b42b-d52cedb40988","added_by":"auto","created_at":"2025-12-31 16:19:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":471017,"visible":true,"origin":"","legend":"\u003cp\u003eFlood exposure across Louisville–Jefferson County, with darker shades indicating higher levels of exposure\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/54dab96d90cd31ddf3eedba8.jpeg"},{"id":99007832,"identity":"18f8522a-8a2e-47a7-af5e-6ad991e68583","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":495380,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of sensitivity scores across Louisville–Jefferson County using equal weighting (left) and unequal weighting (right). Darker shades indicate higher sensitivity\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/29a8c079cbdda0f0192659ba.jpeg"},{"id":99312421,"identity":"90f27021-a440-448a-9b18-ca9331594cfe","added_by":"auto","created_at":"2025-12-31 16:18:59","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":469076,"visible":true,"origin":"","legend":"\u003cp\u003eAdaptive capacity across Louisville–Jefferson County, with darker shades representing lower adaptive capacity\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/c6a73fd27c389b287823347d.jpeg"},{"id":99007841,"identity":"3428b238-9ca6-48d7-b64d-0d32462962d1","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477300,"visible":true,"origin":"","legend":"\u003cp\u003eVulnerability assessment maps for Louisville–Jefferson County using equal weighting (left) and unequal weighting (right). Darker shades indicate higher vulnerability\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/03e55de0b6d5aec52436391a.jpeg"},{"id":99313117,"identity":"6df7e8ab-67f6-4825-b21c-90c99e815052","added_by":"auto","created_at":"2025-12-31 16:19:47","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":458776,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of critical infrastructure in relation to flood-prone areas across Louisville–Jefferson County\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/853ceb3ec0e0b6950479fb63.jpeg"},{"id":99007835,"identity":"eb14d60f-49f2-4112-90d3-bed2099c36b9","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":487816,"visible":true,"origin":"","legend":"\u003cp\u003eUrban Heat Exposure across Louisville–Jefferson County, with darker shades representing higher exposure levels\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/32a399be90ada7c3a8dcaf5e.jpeg"},{"id":99313737,"identity":"4c085655-182d-411f-aa45-44957da01c10","added_by":"auto","created_at":"2025-12-31 16:20:28","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":523204,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity assessment for urban heat exposure across Louisville using equal weighting (left) and unequal weighting (right). Darker shades indicate higher sensitivity\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/76d78ce90613470bd0d74e78.jpeg"},{"id":99007847,"identity":"dbf84cb3-9f64-481f-b40c-7d7532212af9","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":525781,"visible":true,"origin":"","legend":"\u003cp\u003eAdaptive capacity assessment for heat exposure across Louisville using equal weighting (left) and unequal weighting (right). Darker shades indicate higher adaptive capacity\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/713fb41cf0fcee8f23d531f4.jpeg"},{"id":99007842,"identity":"e02a1800-41ff-434b-bd0e-9d80946a51ae","added_by":"auto","created_at":"2025-12-25 18:59:19","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":518088,"visible":true,"origin":"","legend":"\u003cp\u003eHeat vulnerability assessment using equal weighting (left) and unequal weighting (right). Darker shades indicate higher vulnerability\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/30f0793e06506bb87e02c091.jpeg"},{"id":107352237,"identity":"917e15fc-b30b-4020-93cd-1bb0f3fc95e3","added_by":"auto","created_at":"2026-04-20 16:13:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5312123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8147903/v1/a446337e-9a1c-43cd-b536-754f040a4d4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating GIS and the Urban Adaptation Assessment Framework for Climate Vulnerability and Resilience in Louisville, Kentucky","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecent years have witnessed a dramatic escalation in the frequency, intensity, and spatial reach of climate-induced disasters globally. Scientific evidence confirms a fivefold increase in extreme heat events since the 1960s (Thompson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while urban flooding has intensified due to the expansion of impervious surfaces (Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Land disturbance has further exacerbated these challenges by increasing sediment load that fill waterways and covered wetlands (Best, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as replacing natural drainage with stormwater drains (Miller et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) In 2023, unprecedented flooding displaced thousands in Libya and Greece (UNDRR, 2025), while catastrophic rainfall submerged critical infrastructure in China and Pakistan, overwhelming drainage systems despite advanced urban planning (Shah et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Concurrently, record-shattering heatwaves, amplified by atmospheric blocking patterns, scorched Europe, North America, and Asia, triggering higher mortality rates in cities where income inequality drives intra-urban thermal disparities (Shreevastava et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate hazards are increasingly manifesting as cascading threats, where interactions between floods, heatwaves, and droughts amplify risks and overwhelm response systems. For example, in 2023, concurrent heat\u0026ndash;drought crises across Asia, Europe, and other parts of the world disrupted agriculture, energy grids, and public health simultaneously. Such systemic failures reveal that conventional, hazard-specific mitigation approaches are insufficient for compound extremes. Moreover, their impacts remain socially uneven: marginalized communities face greater exposure during floods due to limited access to essential services (Balasuriya et al. 2023) and endure significantly higher temperatures, sometimes up to 6\u0026deg;C more, because of historic redlining and current income inequality (Shreevastava et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This underscores the need for hyper-local, equity-centered vulnerability assessments that capture where environmental exposure intersects with infrastructural deficits, particularly in cities where inequality multiplies risk (IPCC, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban areas are uniquely susceptible to climate extremes because of high population density, concentrated economic activity, and extensive built infrastructure (Wilhelmi \u0026amp; Hayden, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Johnson \u0026amp; Munshi-South, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chapman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Santamouris, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, cities are major contributors to climate change, responsible for a large share of global greenhouse gas emissions. Projections suggest an intensification of extreme urban events, including heatwaves and the urban heat island effect, posing increasing threats to vulnerable populations (Santamouris et al., 2020; Yuan et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kumar \u0026amp; Mishra, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These dynamics highlight the dual role of cities as both drivers and victims of climate change, requiring resilience strategies that address local risk while contributing to broader mitigation efforts (Yeboah et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLouisville, Kentucky, in the United States, exemplifies these challenges. The city faces intensifying heat island effects and recurrent flooding, contributing to property damage, public health risks, and economic strain (Stone et al., 2023). Impacts are unevenly distributed: elderly residents, children, low-income households, and individuals with pre-existing health conditions face disproportionately higher risks during extreme weather events due to limited adaptive resources (Lindsay et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leap et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Noor et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These realities confirm the importance of localized, equity-focused assessments of climate vulnerability. Numerous studies have demonstrated that capturing the multifaceted nature of resilience requires both quantitative and qualitative methodologies (Gallina et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jones \u0026amp; Tanner, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jufri et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Elnagar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral models and studies have highlighted the complexities and limitations in assessing climate resilience. Key contributions highlight the significance of local knowledge (Bosher \u0026amp; Chmutina, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), community preparedness (Pfefferbaum et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Norris et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and structured indices (Cutter et al., 2014; Peacock et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), though these often face challenges like data quality issues and regional variability. Tools like the Resilience Capacity Index (RCI) (Wu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the Climate Change Adaptation Strategy Assessment Tool (CCASAT) (Li Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the Urban Climate Resilience Framework (UCRF) (Baba et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) each offer valuable insights but fall short in areas such as socio-economic integration or hazard specificity. Collectively, the literature points to the urgent need for standardized, multi-dimensional, and adaptive assessment frameworks that integrate diverse data sources and capture resilience as a dynamic process (Sun et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meehl et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sharifi, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quinlan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite a growing body of literature on climate resilience and vulnerability assessment, key gaps remain. First, the Urban Adaptation Assessment (UAA) model has not previously been applied to Louisville, despite the city\u0026rsquo;s documented challenges with flooding and extreme heat. Second, existing UAA applications are typically conducted at the census tract level, offering a coarser spatial resolution. Third, there is a lack of studies that integrate critical infrastructure analysis into UAA-based vulnerability assessments. Finally, few studies provide a comparative evaluation using both equal and unequal weighting schemes to examine how indicator prioritization affects vulnerability outcomes.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study applies the UAA and Geographic Information Systems (GIS) to Louisville, Kentucky. The UAA model, developed by the Notre Dame Global Adaptation Initiative (ND-GAIN, 2018a), assesses urban climate resilience across risk, readiness, and social equity, yielding an overall score that reflects a city\u0026rsquo;s capacity to adapt to hazards including heatwaves, flooding, drought, extreme cold, and sea-level rise (ND-GAIN, 2018a). GIS enables the integration of socio-economic, environmental, and infrastructure data for comprehensive, spatially explicit vulnerability analysis and targeted intervention planning (Sowmiya \u0026amp; Manimaran, 2024; Chakraborty et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It supports near-real-time monitoring, reduces bias through consistent spatial data, and informs community-based planning (Kijewski-Correa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Using UAA and GIS at the block group level, we assess Louisville\u0026rsquo;s vulnerability to climate hazards based on three components: exposure, sensitivity, and adaptive capacity, including critical infrastructure and alternative weighting schemes.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eLouisville, Kentucky, was selected for this study due to its heightened vulnerability to climate-related hazards, notably extreme heat and flooding, which disproportionately impact marginalized and underserved communities. As the state\u0026rsquo;s largest city, with a population exceeding half a million (US Census Bureau, 2024), Louisville embodies a complex urban environment shaped by both geographical and socio-economic factors that make it crucial for climate resilience assessment. Geographically, Louisville lies within the relatively flat Ohio River Valley, which has historically supported urban expansion and agriculture. However, this topographic advantage also renders the city\u0026rsquo;s low-lying areas particularly prone to flooding during heavy rainfall events. Climatically, Louisville experiences a humid subtropical climate with hot, humid summers and mild winters. The city has an average growing season of 223 days and receives approximately 45 inches (1143 mm) of precipitation annually. Average temperatures range from 34\u0026deg;F (1\u0026deg;C) in January to 79\u0026deg;F (26\u0026deg;C) in July.\u003c/p\u003e \u003cp\u003eThe city suffers from a pronounced urban heat island (UHI) effect due to the high density of impervious surfaces, such as roads and buildings, which exacerbates temperature extremes and contributes to heightened heatwave risks, especially for vulnerable populations (Geos Institute, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; World Media Group, 2024). Louisville\u0026rsquo;s demographic landscape adds another layer of complexity. Children under five and adults over 65, who are particularly sensitive to extreme weather, constitute significant portions of the population. Nearly 15.1% of residents live below the poverty line, and about 17.8% face severe housing cost burdens (U.S. Census Bureau, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, 7.8% of households lack access to a vehicle, complicating evacuation and emergency response during crises (U.S. Census Bureau, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In conclusion, Louisville's strategic location along the Ohio River has fostered its growth as a trade and transportation hub but also exposed it to significant environmental vulnerabilities, particularly in the face of climate change. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the geographical location and the map of Louisville/ Jefferson County, Kentucky.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eTo assess Louisville\u0026rsquo;s climate resilience, this study adopted the Urban Adaptation Assessment (UAA) framework developed by the University of Notre Dame. This model assesses urban climate vulnerability through three interrelated dimensions: exposure, sensitivity, and adaptive capacity, to offer a comprehensive analysis of a city\u0026rsquo;s resilience to hazards such as flooding and urban heat. As shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, data was collected from multiple sources to support robust spatial and socio-economic analysis following the framework of the Urban Adaptation Assessment (UAA) model. The Census Block Groups were the smallest spatial units on which the data was collected to provide the most detailed analysis results. Spatial datasets including critical infrastructure (schools, hospitals, fire stations), transportation networks (rails and roads), environmental features (waterbodies, tree canopy, impervious surfaces), and hazard zones (floodplains, heat-affected areas) shapefiles were acquired from the Louisville/Jefferson County Information Consortium (LOJIC), the National Land Cover Database (NLCD), and FEMA to facilitate comprehensive analysis.\u003c/p\u003e \u003cp\u003eSocioeconomic data at the census block group level, including indicators such as age distribution, public assistance rates, vehicle access, and housing age, were obtained from the National Historical Geographic Information System (NHGIS). All datasets were processed using ArcGIS Pro to align with the UAA model. Sensitivity indicators focused on vulnerable populations, children under five, elderly individuals living alone, and residents of older housing stock (built before 1979), which are more susceptible to heat, and flood impacts due to inadequate building standards compared to housing built before 1999. Flood exposure was mapped using FEMA flood zone data, while urban heat vulnerability was analyzed using health insurance data and 30-meter-resolution tree canopy coverage derived from NLCD. This integrative spatial analysis provided a nuanced, data-driven basis for identifying climate vulnerabilities across Louisville, informing targeted resilience strategies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData sources used for Flood Vulnerability Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData for Flood Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings in high-risk flood zone\u003c/p\u003e \u003cp\u003eCars in high-risk flood zone\u003c/p\u003e \u003cp\u003ePopulation living in high-risk flood zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLOJIC\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea of Impervious surfaces\u003c/p\u003e \u003cp\u003eBuildings built before 1999\u003c/p\u003e \u003cp\u003eHouseholds without access to a vehicle\u003c/p\u003e \u003cp\u003ePopulation spending over 50% of income on rent\u003c/p\u003e \u003cp\u003ePopulation of 65 years or older living alone\u003c/p\u003e \u003cp\u003ePopulation under 5 years old\u003c/p\u003e \u003cp\u003eMobile homes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLOJIC\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation with Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdapted from the Urban Adaptation Assessment methodology (ND-GAIN, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gain.nd.edu/our-work/urban-adaptation/methodology/\u003c/span\u003e\u003cspan address=\"https://gain.nd.edu/our-work/urban-adaptation/methodology/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Sources of Urban Heat Vulnerability Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData for Urban Heat Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilding built before 1979\u003c/p\u003e \u003cp\u003eHouseholds receiving public assistance\u003c/p\u003e \u003cp\u003ePopulation spending over 50% of income on rent\u003c/p\u003e \u003cp\u003ePopulation of 65 years or older living alone\u003c/p\u003e \u003cp\u003ePopulation under 5 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree canopy coverage\u003c/p\u003e \u003cp\u003ePopulation with health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNLCD\u003c/p\u003e \u003cp\u003eNHGIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdapted from the Urban Adaptation Assessment methodology (ND-GAIN, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gain.nd.edu/our-work/urban-adaptation/methodology/\u003c/span\u003e\u003cspan address=\"https://gain.nd.edu/our-work/urban-adaptation/methodology/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study employed a multi-layered spatial analysis using ArcGIS Pro to evaluate Louisville\u0026rsquo;s vulnerability to flooding and urban heat. Louisville/Jefferson County boundary was overlaid to ensure spatial consistency. Socioeconomic data stored in CSV format were joined to the spatial block group layer using a shared geographic identifier (GISJOIN), enabling the calculation of population, building, and vehicle counts within high-risk flood zones, as well as population density and impervious surface coverage in heat-prone areas. This integration allowed for a nuanced assessment of exposure, sensitivity, and adaptive capacity at the neighborhood level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Vulnerability Framework\u003c/h2\u003e \u003cp\u003eExposure refers to the extent to which populations, infrastructure, and the built environment are located within areas susceptible to climate hazards such as flooding or extreme heat. In this study, flood exposure was measured by identifying the spatial overlap between FEMA-designated flood zones and built features such as buildings and roads. Urban heat exposure was assessed using indicators like population density and the extent of impervious surfaces, both of which are closely linked to elevated heat retention and temperature extremes. Sensitivity captures the degree to which communities are likely to be affected by climate hazards due to underlying socio-demographic and housing characteristics. Key indicators of sensitivity included the presence of young children, elderly individuals living alone, households receiving public assistance, and older housing stock built before 1979. All of which can limit a community\u0026rsquo;s ability to withstand and recover from environmental stress. Adaptive capacity was measured through indicators such as access to healthcare, emergency services, transportation, and urban tree canopy coverage, which collectively influence a community\u0026rsquo;s ability to prepare for, respond to, and recover from climate-related impacts. Together, these three components provide a comprehensive framework for identifying climate vulnerabilities and informing targeted adaptation strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Normalization Using Min-Max Scaling\u003c/h2\u003e \u003cp\u003eTo standardize the range of all indicators used in the vulnerability assessment, Min-Max Scaling was employed. This normalization technique rescales data to a common scale between 0 and 1, allowing for consistent comparison across variables with differing units and magnitudes such as population density, total housing units, and percentage-based indicators.\u003c/p\u003e \u003cp\u003eThe normalized value for each indicator was calculated using the formula:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Normalized\\:value=\\frac{x-xmin}{xmax-xmin}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ex\u003c/em\u003e is the original value,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003exₘ\u003csub\u003ei\u003c/sub\u003eₙ\u003c/em\u003e is the minimum value in the dataset, and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003exₘₐₓ\u003c/em\u003e is the maximum value in the dataset.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Weighting\u003c/h2\u003e \u003cp\u003eTo ensure a comprehensive evaluation of climate vulnerability, this study employed both Equal and Unequal Weighting methodologies. The Equal Weighting approach assigned equal importance to all indicators within the three core dimensions: Exposure, Sensitivity, and Adaptive Capacity, thereby offering a neutral, transparent baseline for comparing vulnerability across communities. While advantageous for its simplicity and consistency, this method assumes uniform influence across indicators, which may oversimplify complex, context-specific realities.\u003c/p\u003e \u003cp\u003eTo address this limitation, an Unequal Weighting approach was also implemented. In this method, greater weight was assigned to indicators with stronger relevance to flood and urban heat vulnerability, such as population exposure, ageing infrastructure, and healthcare accessibility. This context-sensitive framework aligns with established literature, which emphasizes the importance of locally tailored weights to reflect the varying impact of different variables (Cutter et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e Birkmann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e Balica et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; ND GAIN, 2018b). Through the incorporation of both weighting schemes, the study achieved a balance between methodological objectivity and contextual realism. This dual approach enhances the robustness of the assessment of vulnerability, providing a more accurate and policy-relevant foundation for adaptation planning. Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show equal and unequal weights for assessing flood and urban heat vulnerability in Louisville.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicator Weights used in the Flood Vulnerability Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData for Flood Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEqual Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnequal Weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of buildings in high-risk flood zone\u003c/p\u003e \u003cp\u003ePercent of cars in high-risk flood zone\u003c/p\u003e \u003cp\u003ePercent of population living in high-risk flood zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of area that is impervious surface\u003c/p\u003e \u003cp\u003ePercent of buildings built before 1999\u003c/p\u003e \u003cp\u003ePercent of households without access to a vehicle\u003c/p\u003e \u003cp\u003ePercent of population spending over 50 percent of income on rent\u003c/p\u003e \u003cp\u003ePercent of population that is 65 years or older living alone\u003c/p\u003e \u003cp\u003ePercent of population that is under 5 years old\u003c/p\u003e \u003cp\u003ePercent of total housing units that are mobile homes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of population with health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicator weight used in the Urban Heat Effect Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData for Urban Heat Vulnerability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEqual Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnequal Weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of buildings built before 1979\u003c/p\u003e \u003cp\u003ePercent of households receiving public assistance\u003c/p\u003e \u003cp\u003ePercent of population spending over 50 percent of income on rent\u003c/p\u003e \u003cp\u003ePercent of population that is 65 years or older living alone\u003c/p\u003e \u003cp\u003ePercent of population that is under 5 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of tree canopy coverage\u003c/p\u003e \u003cp\u003ePercent of population with health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Calculation of Flood and Urban Heat Vulnerability Score\u003c/h2\u003e \u003cp\u003eTo evaluate Louisville\u0026rsquo;s susceptibility to flooding and urban heat, composite scores for Exposure, Sensitivity, and Adaptive Capacity were calculated using both Equal and Unequal Weighting methods. These scores were then integrated to generate an overall vulnerability index, providing a nuanced assessment of climate risk across the city. Exposure, Sensitivity, and Adaptive Capacity scores were calculated by multiplying the weights with their corresponding standardized factor scores. For the flood hazards, the weights in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were multiplied with standardized measurements of contribution factors in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For urban heat hazards, the weights in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e were multiplied with standardized measurements of contribution factors in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Vulnerability Score for each hazard was calculated as follows.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Vulnerability\\:Score=\\frac{Sensitivity\\:score+(1-Adaptive\\:capacity\\:score)}{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe calculation of vulnerability scores using both Equal and Unequal Weighting approaches provided a multifaceted understanding of Louisville\u0026rsquo;s susceptibility to flood and urban heat risks. Exposure scores reflected the degree to which infrastructure and populations were physically located in hazard-prone areas, with higher scores indicating a higher risk. Sensitivity scores captured the underlying social and demographic vulnerabilities, such as aging housing stock, income constraints, and the presence of at-risk groups (e.g., elderly or young children), while lower sensitivity scores denoted greater inherent resilience. Adaptive capacity scores measured the city\u0026rsquo;s ability to withstand and recover from extreme events, with higher values indicating more robust institutional and community support systems. Consistent with prior studies, vulnerability scores were normalized on a continuous scale ranging from 0 to 1, where 0 indicates low vulnerability (least susceptible) and 1 represents high vulnerability (most susceptible) (CDC/ATSDR, 2020; Meijer et al., 2023). This approach ensures that variations in vulnerability across spatial units are readily interpretable and suitable for guiding resource allocation and policy decisions aimed at reducing climate inequities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Spatial Analysis of Critical Infrastructure in High-Risk Areas\u003c/h2\u003e \u003cp\u003eThe final vulnerability scores were spatially mapped across Louisville\u0026rsquo;s census block groups using ArcGIS Pro, providing a visual representation of the city\u0026rsquo;s flood and urban heat risks. This mapping facilitated the identification of high-risk areas characterized by the convergence of high sensitivity and low adaptive capacity, marking them as particularly susceptible to climate-related hazards.\u003c/p\u003e \u003cp\u003eTo further analyze the exposure of critical infrastructure, the vulnerability map was overlaid with spatial layers representing essential facilities, including schools, hospitals, fire stations, and water treatment plants. ArcGIS Pro\u0026rsquo;s intersect and spatial join tools were employed to calculate the proportion of these assets located within high-risk flood zones. This integrative analysis enabled the identification of critical infrastructure that is most vulnerable to climate stressors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results revealed distinct geographic patterns of flood and urban heat vulnerability, particularly in zones where high sensitivity intersects with low adaptive capacity. These overlapping vulnerabilities highlight areas of heightened susceptibility to climate hazards and emphasize the need for targeted resilience interventions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Result of Flood Exposure Assessment\u003c/h2\u003e \u003cp\u003eThe assessment of flood exposure was conducted at the Block Group level, with all three indicators estimated based on the percentage of each block group\u0026rsquo;s area that fell within the high-risk flood zone. As a result, the outcomes for equal and unequal weights for flood exposure remained the same. The spatial distribution of flood exposure, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, was expressed as a continuous index ranging from 0.0 (lowest exposure) to 1.0 (highest exposure). Analysis revealed a clear concentration of high flood exposure in the western and southwestern portions of Louisville, particularly neighborhoods adjacent to the Ohio River and major water bodies, such as Shively, Valley Station, Pleasure Ridge Park, and parts of Downtown Louisville. These areas exhibited exposure index values between 0.7 and 1.0, reflecting their low-lying topography, proximity to floodplains, and the density of infrastructure within flood hazard zones. Conversely, the eastern and southeastern regions, including neighborhoods such as Middletown, Fern Creek, and Simpsonville, displayed low flood exposure values (0.1\u0026ndash;0.3), consistent with their higher elevation and greater distance from major water bodies. Central Louisville exhibited mid-range exposure values (0.3\u0026ndash;0.5), reflecting a heterogeneous mix of moderate floodplain presence and urban development intensity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Result of Flood Sensitivity\u003c/h2\u003e \u003cp\u003eThe resulting sensitivity scores, ranging from 0.0 to 0.6, are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The result using equal weighting revealed that block groups in the western and south-central parts of Louisville, particularly neighborhoods such as Pleasure Ridge Park, Shively, and parts of downtown, exhibited the highest sensitivity scores (0.5\u0026ndash;0.6). These elevated sensitivity levels reflect the concentration of elderly populations, who face greater mobility and recovery challenges during disasters, as well as the prevalence of older housing stock, which is often more susceptible to flood damage due to outdated construction standards. By contrast, the northeastern and southeastern parts of the city, including areas near Jeffersontown and Mount Washington, displayed low sensitivity scores (0.2\u0026ndash;0.3), reflecting stronger socioeconomic conditions and the presence of newer, more resilient housing. The unequal weighting approach refined the spatial identification of highly sensitive areas, resulting in a more concentrated pattern of sensitivity in low-income, socially vulnerable urban neighborhoods, while reducing sensitivity scores in some suburban areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Flood Adaptive Capacity Assessment\u003c/h2\u003e \u003cp\u003eAdaptive capacity in this study reflects community resilience based on health insurance coverage, a critical factor influencing disaster preparedness and recovery. Adaptive capacity scores ranged from 0.0 to 1.0, with lower scores indicating greater vulnerability due to limited access to healthcare resources. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, most block groups across Louisville exhibited moderate to high adaptive capacity, particularly in the eastern and southeastern neighborhoods, where scores ranged from 0.7 to 1.0. These areas tend to be more affluent and benefit from greater access to healthcare and associated resources.\u003c/p\u003e \u003cp\u003eIn contrast, clusters of low adaptive capacity (scores below 0.5) were concentrated in western and south-central Louisville neighborhoods, including Shively, Newburg, Pleasure Ridge Park, and Valley Station. These areas highlight communities where residents may face significant barriers to healthcare access, compounding their vulnerability to flood impacts. Since this assessment relied on a single indicator, health insurance coverage, and was conducted at the block group level, adaptive capacity scores did not differ between equal and unequal weighting methods. This consistency underscores persistent geographic disparities in healthcare access and highlights the need for targeted strategies to strengthen resilience by improving healthcare availability in historically underserved areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Flood Vulnerability Assessment Results\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the spatial distribution of flood vulnerability across Louisville, generated using both equal-weighting and unequal-weighting approaches. The result from the equal-weighting approach, revealed the highest vulnerability values (0.4\u0026ndash;1.0) predominantly in the western and south-central neighborhoods, including Pleasure Ridge Park, Shively, and Newburg. These areas exhibit a convergence of higher social sensitivity and limited adaptive capacity, aligning closely with regions of greater flood exposure.\u003c/p\u003e \u003cp\u003eIn contrast, the unequal weighting method prioritized indicators with stronger relevance to actual flood vulnerability, particularly social sensitivity and physical exposure. This weighting approach effectively accentuated the identification of high-risk zones, with vulnerability scores ranging from 0.4 to 0.8 in the urban core and west Louisville. These neighborhoods are characterized by disadvantaged populations, aging infrastructure, and reduced access to healthcare and transportation services. While both methods revealed similar spatial patterns, the unequal weighting method resulted in increased vulnerability measure for many of the census block groups. The color was darker almost across the study area. This distinction underscores the importance of context-sensitive weighting in vulnerability assessments and highlights its utility for informing equitable, targeted flood resilience strategies tailored to the most at-risk communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Critical Infrastructure Vulnerability in Louisville\u003c/h2\u003e \u003cp\u003eThe result (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) reveals that a substantial portion of Louisville\u0026rsquo;s critical infrastructure is situated in flood-prone areas, underscoring the city\u0026rsquo;s vulnerability to climate-related disruptions. Under the equal weighting method (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), approximately 34% of roads, 52% of rail lines, 11% of schools, 21% of hospitals, and 29% of fire stations were located within flood risk areas. The significant presence of roads and rails in these areas raises concerns about transportation continuity and emergency mobility during flood events. Similarly, the exposure of hospitals and fire stations threatens the reliability of emergency response capabilities. In contrast, wastewater treatment plants are largely located along the Ohio River corridor but mostly outside the highest-risk flood polygons. Several facilities, particularly those near Valley Station and along the western riverfront, are positioned close to the river yet likely protected by engineered flood-control systems and topographic elevation. Their siting in comparatively lower-risk zones such as Jeffersontown and southeastern Louisville reflects planning to mitigate potential service disruptions and contamination. Overall, this distribution underscores both the spatial concentration of vulnerable infrastructure in low-lying western neighborhoods and the adaptive placement of key utilities that enhance the city\u0026rsquo;s overall flood resilience.\u003c/p\u003e \u003cp\u003eApplication of the unequal weighting method revealed notable shifts in the spatial exposure profile. The proportion of hospitals located in flood zones increased to 57%, and fire stations rose markedly to 71%, indicating the intersection of human vulnerability and emergency service exposure. Road network exposure also increased to 51%, while rail infrastructure exposure declined to 14%. Generally, all infrastructure categories except rail showed higher levels of flood exposure under the unequal-weighting approach, underscoring the heightened susceptibility of critical facilities and transportation corridors in vulnerable neighborhoods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical infrastructure and flood-prone areas across Louisville\u0026ndash;Jefferson County.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Number or Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEqual Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUnequal weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber or length within flood risk zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber or length within flood-risk zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste Water Treatment Plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e766.2km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263.9km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e389.5km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e761.0km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397.5km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.1km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Urban Heat Vulnerability Assessment\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Urban Heat Exposure Assessment\u003c/h2\u003e \u003cp\u003eUrban heat exposure was assessed using population density as the primary indicator, with exposure scores ranging from 0.0 (lowest exposure) to 1.0 (highest exposure), as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The assessment was conducted at the Block Group level, with all three indicators estimated based on the percentage of each block group\u0026rsquo;s area that fell within the high-risk flood zone. As a result, the outcomes for urban heat exposure remained the same for both equal and unequal weights. Higher exposure values indicate areas where residents are more likely to experience elevated heat stress due to the concentration of human settlement. The highest exposure levels (0.8\u0026ndash;1.0), represented by dark red zones on the map, were concentrated in Louisville\u0026rsquo;s urban core, particularly in densely populated neighborhoods such as Downtown and Jeffersontown. Moderate to high exposure zones (0.4\u0026ndash;0.8) extended outward from the city center and included neighborhoods like Newburg, Shively, and Pleasure Ridge Park. In contrast, the eastern and southeastern parts of the county, encompassing Fern Creek, Middletown, and surrounding rural areas, exhibited low exposure scores (0.0\u0026ndash;0.2), reflecting their lower population densities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Results of Urban Heat Sensitivity Assessment\u003c/h2\u003e \u003cp\u003eThe equal weighting method produced a broad sensitivity profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), with elevated sensitivity scores (0.8\u0026ndash;1.0) observed throughout central Louisville, Pleasure Ridge Park, Shively, and other urban centers. Moderate sensitivity extended into parts of southern Jefferson County, reflecting the widespread presence of at-risk populations across these urban neighborhoods. In contrast, the unequal weighting method yielded a more refined spatial distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), with high-sensitivity zones (0.6\u0026ndash;1.0) becoming more concentrated in west Louisville, Pleasure Ridge Park, and selected segments of central and southern Louisville areas where disadvantaged populations and older, less resilient housing predominate. Despite these methodological differences, both approaches consistently identified urban cores, notably downtown Louisville, Pleasure Ridge Park, and Shively, as sensitivity hotspots. These neighborhoods face a convergence of aging housing stock, economically disadvantaged populations, and limited adaptive capacity. Conversely, suburban areas in the eastern and southeastern parts of the county, including Jeffersontown and Mount Washington, exhibited lower sensitivity scores (\u0026le;\u0026thinsp;0.2), reflecting lower population densities and fewer socioeconomically vulnerable households. The similarity in overall spatial patterns confirms the persistent influence of key demographic and housing factors in shaping urban heat sensitivity. However, the unequal weighting method provided a more nuanced and policy-relevant depiction, better suited for guiding equity-focused adaptation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 Urban Heat Adaptive Capacity Assessment\u003c/h2\u003e \u003cp\u003eThe result shows a moderately varied spatial pattern across Jefferson County under the equal weighting approach, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Very low adaptive capacity scores (0.0\u0026ndash;0.4), represented by dark red areas, were concentrated in urban core neighborhoods such as downtown Louisville, Shively, and Pleasure Ridge Park, where limited tree cover and lower health insurance rates prevail. Moderate adaptive capacity scores (0.4\u0026ndash;0.6) appeared across central neighborhoods, including Newburg, Okolona, and parts of Jeffersontown, reflecting slightly better access to green infrastructure and healthcare services.\u003c/p\u003e \u003cp\u003eThe unequal weighting method emphasized health insurance coverage as a more influential factor, recognizing the critical role of healthcare access in coping with heat-related stress. This approach yielded a more pronounced spatial differentiation: areas with low adaptive capacity expanded within west and south-central Louisville, while eastern and southeastern communities including Middletown, Jeffersontown, and areas near Mount Washington exhibited higher adaptive capacity scores (0.8\u0026ndash;1.0) due to a combination of more extensive tree canopy and stronger healthcare access. While both methods revealed broadly similar geographic patterns, the unequal weighting approach offered sharper insights into health-vulnerable communities by highlighting locations where deficits in healthcare infrastructure and insurance coverage exacerbate sensitivity to heat stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4 Urban Heat Vulnerability Assessment Results\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates the spatial distribution of urban heat vulnerability across Louisville, integrating sensitivity and adaptive capacity indicators under both equal-weighting and unequal-weighting methods. The equal weighting approach revealed widespread areas of high vulnerability (0.6\u0026ndash;1.0), particularly concentrated in west and south-central Louisville, including neighborhoods such as Pleasure Ridge Park, Shively, and the urban core. These areas are characterized by limited green space, lower healthcare access, and heightened social vulnerability, including larger proportions of elderly residents and low-income households. The result of the unequal weighting approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) highlighted a more focused pattern of vulnerability, reducing the prominence of moderate-risk suburban areas and sharpening the delineation of densely populated neighborhoods with significant socio-economic disadvantages. Vulnerability scores in many of these neighborhoods such as Pleasure Ridge Park, Newburg, and southwest Louisville exceeded 0.8, identifying them as key areas of concern. While both methods revealed broadly consistent spatial patterns, the unequal weighting approach provided greater contrast, allowing for a more precise identification of heat-vulnerable communities where gaps in social and health infrastructure exacerbate climate risk. In contrast, the equal weighting approach offered a more generalized vulnerability landscape that may mask important nuances in community needs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study presents a comprehensive assessment of climate vulnerability in Louisville, Kentucky, with a focus on the intersecting risks of flooding and urban heat. By integrating spatial exposure and social sensitivity through the Urban Adaptation Assessment (UAA) framework, the analysis moves beyond traditional, siloed hazard assessments (Gallina et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ahern, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to capture the compounding nature of climate risks explicitly. This approach responds to recent calls in the climate adaptation literature for more holistic methodologies that account for interacting hazards and cascading impacts, especially within socially marginalized communities (IPCC, 2022; Zscheischler et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The findings reveal that climate risk is not determined solely by environmental exposure but is significantly amplified by socioeconomic disadvantages and limited adaptive capacity. This underscores the critical importance of equity in urban resilience planning, reinforcing the need for targeted interventions that address both the physical and social dimensions of vulnerability (Anguelovski et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meerow \u0026amp; Newell, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By highlighting the spatial and demographic patterns of risk, this study provides actionable insights for policymakers and planners seeking to build more resilient and equitable cities in the face of a changing climate.\u003c/p\u003e \u003cp\u003eA key contribution of this research is the precise identification of Louisville neighborhoods where vulnerabilities to both flooding and urban heat intersect, namely Shively, Pleasure Ridge Park, Southside, Park Hill, and sections of downtown Louisville. These neighborhoods exhibit shared structural and demographic characteristics such as aging infrastructure, dense residential development, and higher proportions of low-income and minority populations that magnify hazard exposure and constrain adaptive capacity. The compounded risks and limited recovery resources in these areas establish them as critical hotspots for urban climate vulnerability. This spatial pattern resonates with evidence from climate vulnerability assessments in other U.S. cities, where structurally disadvantaged communities disproportionately shoulder cumulative climate risks due to build environment inadequacies and social inequities (Chakraborty et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hoffman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The findings underscore the urgent need for urban resilience strategies to evolve beyond isolated interventions addressing single hazards. Instead, they must adopt an integrated approach that acknowledges and targets the synergy of multiple interacting stressors, whether sequential or concurrent, that can escalate harm and perpetuate inequity (Raymond et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recognizing these compounding effects is vital for advancing equitable climate adaptation and disaster preparedness. By prioritizing interventions in neighborhoods identified as risk convergence zones, policymakers can more effectively reduce cumulative burdens and foster long-term resilience in the populations most vulnerable to climate extremes.\u003c/p\u003e \u003cp\u003eThe study elucidates the distinct and overlapping determinants of climate vulnerability for both flooding and heat hazards in Louisville. Flood sensitivity was most pronounced among populations experiencing elderly isolation, lack of vehicle access, and high impervious surface coverage conditions. These factors impede evacuation, increase direct exposure to floodwaters, and limit individual agencies during crisis events. These determinants align with prior research emphasizing how social isolation and infrastructural deficiencies amplify flood risk, particularly for those who may not be able to independently relocate or access emergency services (Fekete, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gallina et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, heat vulnerability was substantially driven by limited tree canopy, elevated rent burden, and lack of health insurance. These factors contribute to increased exposure to extreme temperatures and undermine adaptive capacity due to both environmental conditions and socioeconomic barriers to securing adequate relief or healthcare. The absence of tree cover exacerbates the urban heat island effect, while financial strains and underinsurance restrict options for seeking cooling or medical assistance (Hoffman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Anguelovski et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s comparative analysis of equal versus unequal weighting methodologies for climate vulnerability assessment demonstrates that methodological choices significantly influence the identification and prioritization of at-risk populations and infrastructure. The equal weighting approach delivers a broad, generalized perspective, assigning uniform importance to all indicators and yielding vulnerability maps that may underrepresent the critical disparities faced by certain groups. In contrast, the unequal weighting scheme, which draws on established literature and expert consensus, amplifies the visibility of populations with fewer coping resources, leading to a more nuanced and equity-oriented assessment. This methodological divergence has direct consequences for resilience planning and infrastructure protection. Notably, under unequal weighting, the proportion of flood-vulnerable hospitals increased dramatically from 21% to 57%, and fire stations from 29% to 71%, revealing a substantial escalation in the recognized exposure of essential facilities when equity concerns are prioritized. Such findings echo longstanding concerns in infrastructure resilience research regarding the concentration of critical services in disadvantaged neighborhoods, where high hazard exposure and low adaptive capacity converge (Fiack et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Meerow, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While planning efforts, such as siting wastewater treatment plants outside of flood-prone zones, provide evidence of strategic risk mitigation, the ongoing vulnerability of schools, roads, and emergency services throughout the city underscores persistent shortcomings in equitable infrastructure protection. This points to the urgent need for targeted investments and adaptive strategies that address multi-hazard exposures and prioritize the resilience of facilities serving the most vulnerable populations (Yeboah et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its contributions, this study acknowledges several limitations that should inform the interpretation and application of its findings. First, the reliance on block\u0026ndash;group\u0026ndash;level data restricts spatial resolution, potentially overlooking critical vulnerability variations that emerge at the street, block, or parcel level, a limitation noted in recent methodological critiques (Hossain \u0026amp; Meng, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This coarser scale may mask micro-spatial hotspots of vulnerability and hinder highly targeted intervention design. Second, the analysis is based on a static temporal snapshot, lacking longitudinal components that would capture evolving urban conditions, demographic shifts, changes in infrastructure, or future climate projections. Such dynamism is increasingly recognized as essential in resilience analysis, as ongoing processes of growth, migration, and climate change shape urban vulnerability. Third, while unequal weighting was grounded in empirical literature and expert input, the assignment of indicator weights inevitably introduces elements of subjectivity. Different prioritization choices may meaningfully influence both assessment outcomes and resulting policy recommendations (Fekete, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Transparent documentation of these decisions is essential for critical appraisal and replication. Finally, the operationalization of adaptive capacity was necessarily constrained by data availability, resulting in the exclusion of essential resilience dimensions such as social cohesion, institutional readiness, and community preparedness (Aldrich \u0026amp; Meyer, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These factors play a pivotal role in disaster response and recovery but are often difficult to quantify at granular spatial scales. Inclusion of such qualitative and dynamic indicators in future research would strengthen the comprehensiveness and actionability of vulnerability assessments.\u003c/p\u003e \u003cp\u003eThis study confirms the effectiveness of the Unified Assessment Approach (UAA) framework in advancing comprehensive climate vulnerability assessments at the city level. By tailoring the UAA to the unique spatial and demographic context of Louisville, the analysis yields a robust, data-driven basis for equitable resilience planning, pinpointing neighborhoods and populations where strategic interventions are most urgent. These localized insights directly inform policymakers and urban planners, enabling them to prioritize resources and design adaptation strategies that protect the most at-risk communities in the face of multiple, intersecting climate hazards. On a broader scale, the study\u0026rsquo;s findings contribute to the evolution of urban climate scholarship by illustrating the importance of moving beyond hazard-specific, siloed risk mapping toward integrated, equity-focused adaptation frameworks. The Louisville case study serves not only as a model for equitable vulnerability mapping but also reinforces the call for integrated resilience planning that anticipates complex risks in rapidly urbanizing settings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides one of the first integrated assessments of flood and heat vulnerability in Louisville, Kentucky, applying the UAA framework and GIS to capture how environmental exposure, socio-demographic sensitivity, and adaptive capacity interact across neighborhoods. The analysis demonstrates that climate vulnerability is unevenly distributed, with western and south-central neighborhoods consistently emerging as multi-hazard hotspots due to overlapping physical, social, and infrastructural disadvantages. By contrast, eastern and southeastern neighbors benefit from stronger adaptive capacity through greater tree cover, higher socio-economic status, and improved access to healthcare.\u003c/p\u003e \u003cp\u003eFindings call for multi-hazard strategies that address heat and flood together and for equity-centered adaptation that prioritizes urban greening, flood protection, and healthcare outreach in the most disadvantaged neighborhoods. The exposure of critical infrastructure (e.g., hospitals, emergency services) warrants systematic protection, retrofitting, or strategic relocation to ensure continuity during extremes. More broadly, the combined UAA\u0026ndash;GIS approach offers a transferable template for localized, evidence-based planning under compound risks. Future work should incorporate temporal dynamics, compound-hazard modeling, and broader adaptive-capacity measures (e.g., social cohesion, institutional support) to enable more anticipatory, inclusive, and just resilience strategies\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.AF: Conceptualization, methodology, data curation, formal analysis, visualization, and original draft preparation.R.Q. Z: Supervision, validation, review, and editing.C.O: Methodology, data, curation, review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the institutional and academic support provided by Murray State University and the Department of Earth and Environmental Sciences. The first author expresses gratitude to the thesis committee members for their valuable feedback and to his family for their unwavering love and support throughout the course of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhern J (2011) From fail-safe to safe-to-fail: Sustainability and resilience in the new urban world. 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Nat Clim change 8(6):469\u0026ndash;477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-018-0156-3\u003c/span\u003e\u003cspan address=\"10.1038/s41558-018-0156-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate vulnerability, Flood and heat risk, social equity, urban adaptation, urban resilience","lastPublishedDoi":"10.21203/rs.3.rs-8147903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8147903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change presents an escalating threat to urban environments, where dense populations, aging infrastructure, and entrenched social inequities amplify exposure to climate hazards. Urban centers face compound risks such as flooding and extreme heat, disproportionately impacting socioeconomically marginalized communities. Despite recognition of these challenges, there remains a critical need for integrated, spatially detailed assessments to inform equitable adaptation strategies. This study represents the first application of the Urban Adaptation Assessment (UAA) tool in Louisville, Kentucky, integrating fine-scale spatial data with Geographic Information Systems to rigorously evaluate vulnerability to these dual threats. A multidimensional framework encompassing environmental exposure, social sensitivity, and adaptive capacity was employed, incorporating both equal and context-sensitive weighting schemes to reveal nuanced spatial patterns of risk. Our findings demonstrate a significant convergence of flood and heat vulnerabilities within historically underserved neighborhoods, including Shively, Pleasure Ridge Park, and Newburg, where deteriorating infrastructure, economic precarity, and limited healthcare access heighten climate risk. Notably, critical infrastructure such as hospitals and fire stations is disproportionately situated in these high-risk zones, potentially impeding emergency response effectiveness. This analysis elucidates the intersection of environmental hazards, infrastructure deficits, and social inequities at a granular spatial scale, providing a robust, policy-relevant foundation for advancing equitable urban adaptation strategies.\u003c/p\u003e","manuscriptTitle":"Integrating GIS and the Urban Adaptation Assessment Framework for Climate Vulnerability and Resilience in Louisville, Kentucky","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-25 18:59:14","doi":"10.21203/rs.3.rs-8147903/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"516a7bec-efb7-447e-94fe-c44af3b6db0d","owner":[],"postedDate":"December 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:11:48+00:00","versionOfRecord":{"articleIdentity":"rs-8147903","link":"https://doi.org/10.1007/s41651-026-00256-5","journal":{"identity":"journal-of-geovisualization-and-spatial-analysis","isVorOnly":false,"title":"Journal of Geovisualization and Spatial Analysis"},"publishedOn":"2026-04-17 15:59:39","publishedOnDateReadable":"April 17th, 2026"},"versionCreatedAt":"2025-12-25 18:59:14","video":"","vorDoi":"10.1007/s41651-026-00256-5","vorDoiUrl":"https://doi.org/10.1007/s41651-026-00256-5","workflowStages":[]},"version":"v1","identity":"rs-8147903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8147903","identity":"rs-8147903","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}