The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.

为了实现气候可持续性，全球转向电动汽车（EV），但缺乏对建筑环境对电动汽车拥有量影响的全面洞察，尤其是在不同的空间环境下。这项研究以纽约州为重点，整合了不同数据集的数据融合技术，以研究社会经济和建筑环境因素对电动汽车拥有量的影响。空间回归模型的利用揭示了一致的系数值，突出了结果的鲁棒性，空间滞后模型更好地捕捉空间自相关。结果强调了 10 英里半径内充电站的重要性，表明人们对方便的充电选项的偏好会影响电动汽车的拥有决策。较高的教育水平、较低的租赁人口以及老年人口的集中等因素与电动汽车拥有量的增加相一致。利用公开数据为了解各地区的电动汽车拥有量提供了更容易的途径，补充了传统的调查方法。