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Abstract. Geospatial data often build the basis for planning decisions in smart cities. In the decision-making process, geospatial relationships have not yet been fully considered and represented. The aim of our research is to investigate these geospatial relationships. This paper presents a four-step process to identify geospatial key factors in smart city use cases. We further develop and evaluate an existing metric to measure the impact of geospatial factors in urban area. For this, we use three variables to characterise distance decay, opening hours and an attractiveness weight for customers depends on the use case. The use case of this study is off-street parking over a period of four years in Mainz, Germany. The results show temporal relationships between parking and geospatial factors. Consequently, our research indicates the impact of different factors on car parks. This knowledge enriches decisions for sustainable planning in cities.

Urban planning benefits significantly from improved knowledge concerning spatiotemporal relationships and patterns in cities based on geospatial factors. In this context, the potential of geodata seems inexhaustible. Applications include limited-service offers like carparks, the occupancy of which is controlled by geospatial factors characterized by their spatiotemporal patterns. This paper proposes an enhanced model for identifying geospatial key factors, tying in with an existing geo-analytics model. Our approach combines real-world empirical data for off-street parking with open-source geodata on points of interest. We formulate stabilization measures in different model-enhancement stages to optimize model reliability and fit, based on analyses of statistical characteristics. Additionally, we consider modifying the choice of geospatial factors in order to reduce multicollinearity. Our results show improved reliability of geo-analytics for the identification of urban spatiotemporal relationships.