<![CDATA[This study explores the integration of artificial intelligence, spatial analysis, and urban design to evaluate and enhance urban livability while addressing socio-economic and environmental vulnerabilities in contemporary cities. Using Austin, Texas, as a case study, the research combines ArcGIS-based spatial analysis with AI-assisted modeling to examine environmental, socio-economic, and infrastructural datasets, with urban tree canopy coverage serving as a key indicator of neighborhood health and equity. A hexagonal tessellation grid is developed to break down census tract data into standardized spatial units. This allows vulnerability scores to be calculated and correlated with tree coverage. Machine-learning techniques, including regression-based predictive modeling, are applied to identify patterns of vulnerability and forecast future urban conditions using survey-based and real-time data inputs. The findings demonstrate that an integrated GIS–AI workflow effectively reveals socio-spatial disparities in urban livability, paving the way for sustainable urban design, scientific zoning strategies, and informed form-based coding interventions.]]>
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