Digital traces (like call detail records and mobile phone data) have been widely used in many different fields and domains, such as criminology, epidemiology, public transportation, urban sensing, and data protection, to assess and predict human communication behaviors and mobility patterns because smartphone use has grown so rapidly. These digital footprints contain important spatiotemporal (geospatial and time-related) and communication (incoming and outgoing call) information, revealing not just travel patterns but also social networks and interactions. Every user activity or contact (such as making a call, sending a text message, or using a smartphone to access the internet) is recorded by service providers in order to collect smartphone data, which is then stored in their databases. This study compares and contrasts several methods and approaches for analyzing and predicting human communication patterns and mobility from mobile phone data, taking into account both their benefits and drawbacks. It also offers call, location, and temporal information that has been extracted from mobile phone data and used to simulate human speech and movement. We analyze mobile phone data research published between 2013 and 2021 using eight main databases: the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Our inclusion and exclusion criteria led to the selection of 148 studies in total
A large metropolitan university's College of Nursing faculty members' papers were reviewed. The h-index for each author from each database was noted after author-name searches were conducted in Scopus, WOS, and POP (Publish or Perish, which searches Google Scholar). Furthermore, a bibliographic management program was used to import the citing articles of their published articles. Each author's aggregated h-index was calculated using these data.
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