To ensure that adequate charging infrastructure is available where it is needed, accurately estimating the spatial distribution of energy demand is essential. In this paper, we present a data-driven approach for estimating the demand for electric vehicle charging stations. For destination charging demand, we leverage publicly available sociodemographic data, a POI database, and mobility statistics, including daily trip rates, modal share, and trip length distributions. En-route charging demand is estimated using highway traffic count data. We account for EV battery capacity, maximum mileage, and driver charging behavior. Statistical parameters related to the EV fleet (penetration rate, home-charging percentage) are considered to adapt estimates for various scenarios. We present an interactive web-based GIS tool that displays energy demand densities for passenger and freight transport via heat maps, demand generated at POI, and demand along highways. By combining estimated demand with existing charging station data, the tool calculates unmet residual demand, facilitating the planning of future charging infrastructure. Our data-driven approach provides a valuable tool for estimating electric vehicle charging station demand, offering insights into spatial energy demand distribution and supporting future infrastructure planning efforts. Notably, this approach relies solely on readily available data, enabling immediate use in practice and easy adaptability to various regions.