Analysing and Identifying Geospatial Key Factors in Smart Cities – Model Enhancements in the Use Case of Carpark Occupancy

Abstract

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.