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Fluctuation before and after the callback implementation

Fluctuation before and after the callback implementation

Source publication
This paper addresses the determination of geospatial key factors, which are relevant for bike sharing stations in the city of Hamburg. They serve as an application case for limited service offers in smart cities. Our approach combines real-world empirical data with open-source data on points of interest for the determination. We apply linear regres...


... Geo-analytics allow us to identify spatiotemporal patterns that influence carpark occupancy. It is also crucial to consider geospatial factors such as nearby food services or shopping facilities that show particular patterns (Cui et al., 2018;Rolwes & Böhm, 2021;Roussel et al., 2022). Geospatial factors trigger carpark occupancy at different times. ...
... There is ample research on identifying spatiotemporal relationships in smart mobility for urban planning. Common areas of application are e-mobility (Wagner et al., 2013;Wagner et al., 2014), car sharing (Klemmer et al., 2016;Willing et al., 2017), bike sharing (Pelechrinis et al., 2017;Reiss & Bogenberger, 2016;Roussel et al., 2022;Schimohr & Scheiner, 2021;Wang et al., 2021;Wang & Chen, 2020) and parking (Rolwes & Böhm, 2021). Previous studies utilize (historical) POI data, categorize them into geospatial factors, and analyse the spatiotemporal relationships in the use cases in question. ...
Full-text available
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.