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Neighborhood-scale planning plays a crucial role in addressing climate change through adaptation strategies, particularly concerning thermal environmental factors such as temperature and humidity. While various numerical models estimate local thermal environments, their complexity limits broader applications in performance-oriented neighborhood planning. To bridge this gap, the Urban Weather Generator (UWG), a simplified model based on energy conservation principles, was developed to meet the needs of neighborhood planners. Although UWG has been utilized in cities like Singapore, Basel, and Toulouse, further validation is needed for hot and humid conditions such as those in the Gulf Coast of the US. This study evaluates the performance of the UWG model in the Houston area as a first step toward estimating thermal environments at a fine neighborhood scale. We conducted a comprehensive sensitivity analysis of key model input parameters and neighborhood size, revealing that urban building density is the most significant variable, while neighborhood size has minimal impact on model accuracy, indicating potential scale flexibility. Multiple reference weather stations in suburban/rural areas were used, with outputs compared to actual observations in a representative urban site in Houston. Over 11 months of observations across multiple years, the Sugar Land weather station and Wharton weather station were identified as the optimal choices for simulating neighborhood-scale weather in Houston’s urban area. 

Abstract This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts. 

Urban synthetic data has emerged as a transformative tool for planning research, yet its full potential remains constrained by an overwhelming focus on physical spaces and object recognition. Current digital twin analysis prioritizes built environments over human interactions, while privacy concerns in social media-based behavioral studies limit reproducibility. This commentary argues for a paradigm shift—one that integrates human vibrancy into urban synthetic data approaches to foster human-centered digital twins. By addressing key challenges of privacy, reproducibility, and technical feasibility, human-centered synthetic data can redefine urban informatics, bridging the gap between digital representations and lived urban experiences. 

GeoDesign is undergoing a methodological shift through its integration with Urban Digital Twins (UDTs) and artificial intelligence (AI), moving from static spatial analysis to interactive and justice-oriented planning practices. This editorial reframes GeoDesign as both ethical and civic efforts. While digital twin technologies enable participatory planning and multiscalar data integration, they also raise concerns about bias, transparency, and governance. The six contributions in this special issue examine frameworks for ethical design, participatory tools, data interoperability, housing policy modeling, and planning pedagogy. Collectively, they advance the field of Ethical GeoDesign, emphasizing accountability, representation, and equity in UDTs. 

Abstract 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. 

Extreme heat poses significant environmental and health risks. These risks often disproportionately affect marginalized and disenfranchised communities. Neighborhood-scale planning is essential for addressing climate change through adaptation strategies. Currently, there is a lack of long-term weather data at the neighborhood level, limiting the ability to analyze localized weather trends and compare variations across areas. This gap persists due to the challenges of collecting fine-scale local data, including significant time demands, limited resources, and potential privacy concerns. To bridge this gap, this project employs the Urban Weather Generator model to generate neighborhood-scale temperature and relative humidity data for the city of Houston. The neighborhood size is defined using a 500-by-500-m grid. Based on the comprehensive analysis of heat stress variation across these neighborhoods, we identify areas with notably higher or lower heat stress duration. This dataset is intended to inform targeted interventions in microclimate design and personal-level heat adaptation. 

Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise.