Analysis of the pattern and intensity of urban activities through aggregate cellphone usage

This study applies passive mobile positioning data such as Call Volume, Handover, and Erlang to detect the spatiotemporal distributions of urban activities. We obtained hourly aggregated cellphone data from a dataset of communications in Lisbon, Portugal. Fuzzy c-mean clustering algorithm was applied to the cellphone data to create clusters of locations with similar features in two aspects of activities: the pattern and intensity of urban activities along the hours of a day. In order to validate those clusters as actual predictors of human activity, we compared them with clusters formed using ground truth variables namely presence of people, buildings, points of interest, and bus and taxi movement. To identify the patterns of urban activities, the Erlang data provided a better match, with the ground truth giving 69% of overall accurate predictions. In the case of the intensity of activities, Handover data provided the highest match, with the ground truth yielding 80% of overall accuracy of predictions. Hence, the results demonstrate the potential of passive mobile positioning data in detecting intensity of activities that are superimposed on the different activity patterns, which is a fundamental piece of information for transportation and urban planning.