Applying Human-scale Understanding to Sensor-based Data : Generating Passive Feedback to Understand Urban Space Use

Abstract: The aim of this thesis is to investigate how parametrization of large-scale person movement data can contribute to describing the use of urban space. Given anonymous coordinate and timestamp data from a sensor observing an open-air mall, movement-based parameters are selected according to public life studies, behavioral mapping, and space syntax tools. The thesis aim is operationalized by answering how well the parametrizations perform in capturing urban space use, as well as investigating how the use is described when applying the parameterized data in selected urban space use tools. Also, the parameterized data are evaluated as time series to investigate possible further understanding of urban space use. The parametrization performance is evaluated by accuracy and F1-score and time series forecasts are evaluated by root mean square error (RMSE) and mean absolute error (MAE). The results indicate a parametrization accuracy of 93% or higher, while a high yet fluctuating F1 -score indicates that the parameterizations might be sensitive to imbalanced data, and that accuracy alone might not be sufficient when evaluating urban data. The parameterized data applied in the selected urban space use tools highlights the granularity achieved from sensor-based data. In the time series analysis, a Facebook Prophet forecast model is implemented, with an MAE of 8.6% and RMSE of 11.7%, outperforming a seasonal naïve forecast implementation with an MAE of 14.1% and RMSE of 18.8%. The thesis finds that time series modelling adds to understanding patterns and changes of use over time and that the approach could be developed further in future studies. In answering how the urban space is used, the thesis develops a new methodology. This methodology combines human-scale understanding of urban space use with large-scale data, generating citizen passive feedback.