Modelling spatio-temporal distribution of urban population - A high-resolution model for German cities

Information about the spatial distribution of urban populations is highly relevant for areas such as urban planning, infrastructure services, and hazard prevention. Conventionally, census-based population maps provide a static image of population numbers. However, the distribution of urban populations is highly dynamic and changes throughout the course of a day, which can result in significant discrepancies from static maps. We therefore developed a spatio-temporal population model, which is designed to provide the relevant space- and time-dependent distribution for individual cities in Germany. Germany has been selected due to the high data availability, which allows us to base the model on public data, making it transferable to other cities. The city of Cologne serves as a case study, but the transfer of the approach to Hamburg has been successfully tested. The model is built on extensive dasymetric mapping cycles to combine extensive socio-demographic population and building data with mobility information. The results obtained are city-wide population maps for seven time sequences, which provide the total number of people, as well as the number of people for seven population subgroups (children, retired, etc.), on building level. The results are compared to three independent data sets (ENACT-POP, emergency call locations, and mobile phone location data), which show substantial improvement towards static census data and good correlation metrics. The spatio-temporal results show strong time-dependent differences in comparison to static population distribution (e.g., up to six times more people for the inner city of Cologne at midday), underlining the relevance of time-dependent data for population-based analyses. • Model to calculate spatio-temporal population maps for German cities • Uses iterative dasymetric mapping to combine geo-, demographic-, and mobility data • Produces population maps for seven time intervals of a standard working day • Results differentiate between seven population subgroups (e.g., children, elderly) • Model validation with ambulance calls, phone locations, and European day/night data