Mapping the dynamics of electric vehicle charging demand within Beijing's spatial structure

• Data mining from smartphone offers a new lens to study EV charging behaviors. • Location-based service big data reveals spatial heterogeneity of public charging demands in the large city. • Mapping demonstrates 45% of public charging stations in Beijing may be underutilized. • Spatial regression model confirms correlation between EV travel pattern and urban spatial structure. • EV charging behaviors vary significantly over weekdays and weekend. • High-density residential areas beyond the city center with imperfect public service facilities tend to be more reliant on PCS, which is often underestimated in infrastructure planning. Electric vehicles have been proliferating in large cities across the world, and we increasingly face challenges in estimating charging demand and in planning EV infrastructure. Focusing on Beijing as a case study, this research uses a novel data-driven method to measure the Charging Demand Indicators (CDI) derived from location-based service big data. Analyses through kernel density function reveal dynamic relations between the spatial patterns of CDI and the distribution of Public Charging Stations (PCS). Spatial match examination is conducted to discover areas of mismatch between charging demand and infrastructure supply. The results expose a CDI pattern which, although largely complies with the city's centripetal structure, demonstrates variations between weekdays and weekends and by EV travel distances. A spatial regression model confirms the influence of urban structure and distribution of amenities on EV charging behavior and suggests that particular land uses and location features have a significant association with EV charging demand. These findings shed light on the understanding of the spatial disparity between the CDI pattern and the current PCS distribution, which could inform future urban policies and planning of EV infrastructure with an emphasis on its coordination with land use, physical layout, and transit.