Quantitative assessment of public sector eMobility Infrastructure development: - A Spatial Perspective in case of Germany

Spatial data-driven approaches are promising for evidence-based public policy-making (e.g. spatial planning) by combining multi-dimensional open data and emerging technologies besides traditional data sources. With this background, this study aims to assess spatial diffusion patterns its potential determinants of Electric Vehicle (EV) public charging infrastructure development in Germany. The assessment approach will be depicted beyond the administrative boundary so that can support efficient local infrastructure development. In fact, the earlier investigations have reported some initial findings on physical aspects of spatial variability of EV infrastructure in Germany. The explorative approach analyzed the intensity of EV-charging points (geolocations) at multiple administrative levels (Bundesland, Raumordnungregion and Kreis). The degree of variability in both visual analytics and statistical facts was presented in relation to transportation-related landuse indicators. For charging point development it is essential to combine administrative data sets with dynamic supply (e.g. charging point data) and demand side parameters. This contribution will be taken a more comprehensive look by involving both people and placed based indicators (e.g.population density, settlement density, income level, land use mix, road density) at a raster level. The required datasets will be harvested from Open source-API (OpenChargerMap - a community lead volunteered geoinformation platform for EV charging points) and open-access geospatial data services(IOER-Monitor API that is provided by scientific data infrastructure in the combination of multi-source basic geospatial and statistical information for whole Germany). In consideration of the open science principle, the open/freely available software/analytical tools will be used for preparation, data management, analysis and visualization. The spatial association will be studied after extracting spatial statistics: Moran’s I (Global and local) and the Gini Index by considering the variability of intensity of EV charging infrastructure. The results should give an understanding of spatial clusters of EV infrastructure development. The issue of sensitivity will be addressed by studying multiple resolutions of rater size, and issues of data quality-related uncertainties. For promoting equitable regional development, there is a need for policymakers to quantitatively evaluate the spatial distributiveness of charging infrastructure. The policy implication will be to investigate to what extent the spatial inequality of EV could be quantified to inform future mobility infrastructure development? Moreover, the result has the potential to develop robust indicators for supporting data-driven predictions/analytics of smart city infrastructure planning.