Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments so that they can quickly roll out infrastructure that enables large-scale EV adoption in all segments and accelerates the green transition of the transport sector. To this extent we propose a transport energy demand centric dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In it, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, BEV enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and interactively and continuously adjust and reoptimize network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access the grid capacity and real-estate and presence of competition) are gradually discovered/observed. The paper describes components and functional support of the system that is available as of a web based platform to support the planning of public fast charging networks for freight and long-distance private car trips in 26 European countries (https://thegordian.io/) and has been used in commercial pilots in both competitive and collaborative settings.