Spatial Data Gap Analysis of Electric Vehicle Charging Points

Despite the considerable advancement of various technologies based on broadly defined digitalization, the phenomenon of electromobility is still a niche area, especially in terms of implementing new charging points in the human sphere. Undoubtedly, this is an area of human activity where very important decisions are made regarding taking action to reduce exhaust emissions, so crucial for future generations. Therefore, the phenomenon of electromobility, related to aspects of transport tasks, is linked to all issues related to the movement of people and cargo using electric vehicles (EVs), which in turn depends on the location of charging points, which are part of the alternative fuel infrastructure.

Given the importance of this evolving phenomenon, it is crucial to demonstrate the potential for developing infrastructure for electric vehicles, but also to define areas where this development is hindered for various reasons. This led to an investigation into the existence of data gaps in the area of electromobility, with particular emphasis on the allocation of charging points within the context of human activity. The direction of the research was also determined by the existing literature gap in the analyzed research area.

The aim of this article was to interpolate the gaps in spatial data for electric vehicle charging points across the Republic of Poland. In this article, to demonstrate the gaps in spatial data, i.e., electric vehicle charging points, it was deemed appropriate to use the hypothesis that: the spatial autocorrelation of electric vehicle charging points is related to the road layout and traffic density gradient in a given area.

The research process in this article involved analyzing the general and detailed locations of electric vehicle charging points based on our own inventory, including field surveys and data from spatial information systems. ArcGIS tools, a specialized tool for analyzing data related to electromobility, were used for spatial data processing and interpolation analyses. For a more comprehensive depiction of the phenomenon, the Inverse Distance Weighting method was used to present the results in cartographic form, allowing for the presentation of both descriptive and geostatistical data.