CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such ‘Big Data’ sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources–all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks.

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.