A Spatial Adaptation Framework for Urban Climate Resilience

Thesis Abstract

Urban areas face mounting climate-related risks driven by heat, flooding, and evolving urban form, yet existing resilience measures often neglect spatially explicit pathways that translate climate adaptation into localized urban outcomes. This study develops a Spatial Adaptation Framework (SAF) for urban climate resilience that integrates place-based dynamics, infrastructure networks, and social-ecological processes to guide planning and policy. The aim is to conceptualize and empirically validate a framework that links spatial configuration, exposure and vulnerability, adaptive capacity, and governance to resilience outcomes across neighborhoods. Specific objectives are (1) to delineate spatially explicit indicators of climate exposure, vulnerability, and adaptive capacity at the neighborhood scale; (2) to model the relationships between urban form characteristics (land-use mix, green–grey infrastructure, and street-network topology) and resilience outcomes (cooling potential, flood mitigation, and social well-being); (3) to identify spatially prioritized adaptation strategies through scenario analysis and multi-criteria decision analysis; (4) to test the SAF in a mid-sized coastal city with diverse topographies and housing stock, and (5) to formulate policy-relevant recommendations for spatial planning, infrastructure investment, and governance arrangements. Methodologically, the study adopts a mixed-methods research design anchored in positivist and pragmatic paradigms. The population comprises 48 neighborhoods in the coastal metropolitan area of Porthaven, with a target sample of all 48 neighborhoods for comprehensive spatial analysis. Spatial data layers include high-resolution land-use maps, building footprints, green space density, surface temperature from satellite-derived land surface temperature (LST) data, and stormwater drainage network performance. A household survey (n = 1,200 respondents, stratified by neighborhood deprivation and housing tenure) will capture subjective resilience indicators, perceived heat exposure, and adaptation behaviors. Semi-structured interviews (n = 24) with municipal planners, utility managers, and community leaders will elucidate governance processes and cross-sector coordination. Data collection instruments comprise GIS-based data extraction templates, a structured resilience survey instrument with validated scales for exposure, vulnerability, adaptive capacity, and well-being, and interview guides aligned with the SAF constructs. Validity and reliability will be ensured through pilot testing, Cronbach’s alpha assessment (aiming for ? ? 0.70 for scales), and triangulation across spatial, survey, and interview data. Analyses will proceed in four stages. First, a spatial diagnostics phase will compute exposure indices (heat, flood, and wind risk) and resilience indicators using GIS and remote sensing (including normalized difference vegetation index, impervious surface fraction, and LST). Second, confirmatory factor analysis (CFA) will validate measurement models for exposure, vulnerability, and adaptive capacity constructs. Third, structural equation modeling (SEM) will test the hypothesized SAF relationships among urban form, exposure, vulnerability, adaptive capacity, and resilience outcomes, with multi-group SEM to explore variation across neighborhoods by deprivation tertiles. Fourth, scenario analysis using a multi-criteria decision analysis (MCDA) framework will compare adaptation pathways under different climate projections (RCP 4.5 and 8.5) and policy constraints, incorporating stakeholder weightings derived from a Delphi process. The theoretical grounding draws on the Social-Ecological System (SES) resilience framework and the Urban Network Theory, with explicit integration of place-based adaptation principles into a spatially explicit model. Expected findings include (a) quantification of the strength and direction of links between urban form metrics (e.g., green space per capita, connectivity indices) and resilience outcomes; (b) identification of spatial hotspots where maladaptation risks persist despite governance investments; (c) demonstration that neighborhoods with high social adaptive capacity exhibit disproportionate benefits from targeted green infrastructure and retrofit programs; and (d) explicit policy pathways that optimize spatial distribution of nature-based solutions, permeable surfaces, and blue-green corridors to reduce heat and flood risk while enhancing social well-being. The study contributes to knowledge by operationalizing a Spatial Adaptation Framework that merges urban morphology, climate risk, governance, and community resilience into a testable, policy-relevant model. It provides a replicable methodology for other mid-sized coastal cities and informs spatial planning, climate adaptation budgeting, and interdepartmental coordination. Recommendations emphasize prioritizing neighborhood-scale green–grey infrastructure, revising zoning to unlock adaptive capacity, and institutionalizing cross-sector governance mechanisms to implement spatially targeted resilience investments.