With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

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.

Electric vehicles (EVs) have been proposed as a key solution for decarbonizing urban transportation and addressing climate change. As the use of EVs increases in cities worldwide, it may lead to significant transformation in urban development, including changes in the electrical system and people's travel behavior, such as charging preferences and choices of where to live and work. Some questions arise, will the rise of EVs lead to more suburbanization or drive people towards a more compact urban form? Additionally, how can the relationship between EV users' residential locations and new energy infrastructure be best coordinated? A study in the rapidly growing metropolis of Beijing aims to address these questions by combining geo-spatial big data analysis, machine learning, and theories of urban development to understand the relationship between EV users' residential locations and new energy infrastructure. A novel data mining strategy was proposed to identify actual EV users based on location data from smartphones. By analyzing observation data of EV users, the study applies the Gradient Boost Decision Tree model to examine the nonlinear associations between the spatial distribution of EV residents and neighborhood attributes such as employment density, GDP, land use mix, public charging accessibility, building areas, access to public transit, and suburbanization. The results indicate that a higher percentage of EV users prefer to live in areas that are neither too far away from the city center nor too close to it, particularly the threshold effects show that they are concentrated in areas where it has a 10 km distance from the city center. Additionally, the study found that most public charging activities tend to occur within 1.5 km from home, suggesting an optimal threshold for public charging station deployment. The findings of this study can help inform energy management and infrastructure planning at the local, regional, and national levels to promote sustainable urbanization and smarter energy planning in policy-making.