This paper explores the evolution of Geodesign in addressing spatial and environmental challenges from its early foundations to the recent integration of artificial intelligence (AI). AI enhances existing Geodesign methods by automating spatial data analysis, improving land use classification, refining heat island effect assessment, optimizing energy use, facilitating green infrastructure planning, and generating design scenarios. Despite the transformative potential of AI in Geodesign, challenges related to data quality, model interpretability, and ethical concerns such as privacy and bias persist. This paper highlights case studies that demonstrate the application of AI in Geodesign, offering insights into its role in understanding existing systems and designing future changes. The paper concludes by advocating for the responsible and transparent integration of AI to ensure equitable and effective Geodesign outcomes.

Public transportation is an essential component of building sustainable communities. However, its ridership remains low in most cities in the United States. Among the major barriers is the long distance to the bus stops, called the first-mile problem. Using a stated preference survey among 1056 residents of El Paso, Texas, this study addresses this problem by estimating additional transit trips that can be expected from the implementation of hypothetical, free shuttles between one’s home and the closest bus stops. Participants reported 7.73 additional transit trips per week (469% increase from the current baseline), including 3.03 additional trips for work, 1.94 for daily errands, 1.64 for leisure or social, and 0.93 for exercise or sports. The percentage of transit non-users dropped from 77.6% (baseline) to 38.2%. With the free shuttle service, respondents would favor bus rapid transit more than regular buses (4.72 vs. 3.00 additional trips). Residents identifying as an existing transit user, being Hispanic/Latino, owning at least one automobile, living within 1 mile of a transit stop, and feeling safe while riding the bus would make significantly more transit trips due to the service. This study suggests that programs to address/reduce the first-mile problem could increase transit demand and, therefore, contribute to creating sustainable and more connected communities.

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones.