Authors: Zhe Zhang*, Texas A&M University, Ximing Cai, University of Illinois at Urbana-Champaign, Changjiang Xiao, Tongji University, Shaowen Wang, University of Illinois at Urbana-Champaign
Topics: Geographic Information Science and Systems, Cyberinfrastructure, Human-Environment Geography
Keywords: Spatial Decision Support System, Electric Vehicles, GIS, Sustainability
Session Type: Paper
Start / End Time: 5:35 PM / 6:50 PM
Room: Virtual Track 8
Presentation File: No File Uploaded
Electric vehicles (EV) are considered as one of the promising solutions in the efforts to reduce the environmental impact of road transport. A review of the literature indicates consumers’ social, cultural, and economic characteristics play an important role in affecting the decision in purchasing an EV. However, most of the studies consider all the EV adopters as one homogeneous consumer group with the same geographical, socio-economic, psychographic, and cultural background within a limited geographical region, which lacks the support in identifying the spatial pattern in EV adoptions. In this article, a spatial decision support framework for analyzing the spatial pattern of electric vehicle adoption was proposed. New York State's existing charging infrastructure data, EVs sales data, and census data were used. This spatial decision support framework consists of a set of spatial analysis processes. The spatiotemporal data visualization method and correlation analysis are used to find the spatiotemporal patterns of existing EV charging infrastructure and its relationship to a number of EV sales. Results indicate non-stationary spatial patterns of EV adoption based on consumers’ social and economic factors. People living in different regions of the state of New York with low to medium annual income have a different attitude in purchasing an EV. People living in the western and northern parts of the state would like to purchase EVs for long-distance work trips, but the situation is opposite for New York City and its suburbs areas.