• Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits

      Oosterlinck, Dieter; Benoit, Dries F.; Baecke, Philippe; Van de Weghe, Nico (Applied Geography, 2017)
      Intelligence about the spatio-temporal behaviour of individuals is valuable in many settings. Generating tracking data is a necessity for this analysis and requires an appropriate methodology. In this study, the applicability of Bluetooth tracking in an indoor setting is investigated. A wide variety of applications can benefit from indoor Bluetooth tracking. This paper examines the value of the method in a marketing application. A Belgian shopping mall served as a real-life test setting for the methodology. A total of 56 Bluetooth scanners registered 18.943 unique MAC addresses during a 19-day period. The results indicate that Bluetooth tracking is a sound approach for capturing tracking data, which can be used to map and analyse the spatio-temporal behaviour of individuals. The methodology also provides a more efficient and more accurate way of obtaining a variety of relevant metrics in the field of consumer behaviour research. Bluetooth tracking can be implemented as a new and cost effective practice for marketing research, that provides fast and accurate results and insights. We conclude that Bluetooth tracking is a viable approach, but that certain technological and practical aspects need to be considered when applying Bluetooth tracking in new cases.
    • Home location prediction with telecom data: Benchmarking heuristics with a predictive modelling approach

      Oosterlinck, Dieter; Baecke, Philippe; Benoît, Dries (Expert Systems with Applications, 2021)
      Correctly identifying the home location is crucial for human mobility analysis with telecom data, more specifically call detail record (CDR) data. To that end, multiple heuristics have been developed in literature. Nevertheless, due to the lack of ground truth home location data, no study has thoroughly validated these widely used methods so far. We present a detailed performance analysis of existing home detection heuristics, using a unique dataset that enables this important validation on the lowest level, being the level of the cell tower. Our research indicates that simple heuristics surprisingly outperform their more complex counterparts. The benchmark study revealed that the best heuristic is able to identify the home location with an average error of approximately 4.5 kilometres and selects the correct home tower in 60.69% of the cases. Based on the insights provided by our study, we propose a new heuristic that increases the accuracy to 61% and lowers the average distance error to 4.365 kilometres. Secondly, if the home location is known for possibly only a fraction of the instances, we propose a labelled predictive modelling approach. Adding social network based variables in this predictive model further enhances the predictive performance. Our best model reduces the average distance error to 2.848 kilometres and selects the correct home location in 72.08% of the cases. Furthermore, this result provides an indication of the upper bound for home detection with CDR data. Finally, models that only make use of social network based data are developed as well. Results show that even without using data of the focal individual, these models are able to select the correct home tower in 37.65% of the cases and achieve an average distance error of 8.1 kilometres.