Biclustering for uncovering spatial-temporal patterns in telecom data

Understanding human dynamics is of crucial importance for managing human activities for sustainable development. According to the United Nations, 68% of people will live in cities by 2050. Therefore, it is important to understand human footprints in order to develop policies that will improve the lives in urban and suburban areas. Our study aims at detecting spatial-temporal activity patterns from mobile phone data provided by a telecom service provider. To be more precise we used the activity data set which contains the amount of sent/received SMS, calls, as well as internet usage per radio-base station in defined time-stamps. The case study focus is on the capital city of Serbia, Belgrade, which has have nearly 2 million inhabitants and included the month of February 2020 in the analysis. We applied the biclustering (spectral co-clustering) algorithm on the telecom data to detect locations in the city that behave similarly in the specific time windows. Biclustering is a data mining technique that is being used for finding homogeneous submatrices among rows and columns of a matrix, widely used in text mining and gene expression data analysis.  Although, there are no examples in the literature of the algorithm usage on location-based data for urban application, we have seen the potential due to its ability to detect clusters in a more refined way, during a specific period of time that could not otherwise be detected with global clustering approach. To prepare the data for the algorithm appliance, we normalized each type of activity (SMS/Call In/Out and Internet activity) and aggregated the total activity on each antenna per hour. We transformed the data into the matrix, where rows were presenting the antennas, and columns the hours. The algorithm was applied for each day separately. On average number of discovered biclusters was 5, usually corresponding to regular based activities, such as work, home, commuting, and free time, but also to the city’s nightlife. Our results confirmed that urban spaces are the function of space and time. They revealed different functionalities of the urban and suburban parts in the city. We observed the presence of patterned behavior across the analyzed days. The type of day dictated the spatial-temporal activities that occurred. We distinguished different types of days, such as working days (Monday to Thursday), Fridays, weekends, and holidays. These findings showed the promising potential of the biclustering algorithm and could be utilized by policymakers for precisely detecting activity clusters across space and time that correspond to specific functions of the city.