Assessing the Spatial Demand for PCS for EVs Using Multi-source Big Data: An Example from Jinan City, China

Under the pressure of carbon pollution and resource scarcity, electric vehicles (EVs) have gradually replaced fuel vehicles as a new trend of low-carbon transformation. However, public charging stations (PCS) face problems such as insufficient quantity and unreasonable distribution. By using multi-source big data, this paper comprehensively analyzes the population distribution, traffic organization, infrastructure, land use and regional economy in the urban area of Jinan, China, and constructs evaluation indicators and spatial demand evaluation model for PCS. We analyse: 1) Distribution of population activity areas on weekday and rest days, high road network accessibility, high density area, high travel road network, commerce, public service facilities, parks, transportation facilities, residential area, high building coverage, high floor area ratio, economic development area and housing price level. 2) The impact of 14 evaluation indicators on space demand. 3) Demand level distribution of PCS in Jinan. 4) The distribution of current PCS and the comparison with the spatial demand model. The method recovers for the lack of comprehensive consideration of factors, intuitiveness of mathematical models, and urban geographic spatial research. This is of significance for predicting the use of PCS in the future and further promoting the whole popularization of EVs.