• geospatial analytics
• human mobility
• social network analysis
• neo4j graph data science

Human movement geolocation datasets have grown exponentially in recent years.
Companies and governments can optimize the allocation and management of resources by combining geospatial information and business intelligence. This work aims to utilize geospatial information to help client companies.
Since cell phone data is ubiquitous today, they occupy a prominent position among human movement geolocation datasets.
In the following thesis we use these data to analyze human mobility and in particular we will deepen the human flow network built on an Italian region.
After a brief introduction of the business context and possible applications, the tools
and tables used are presented.
Subsequently, the first case study is presented, in which, in addition to explaining the work done for the client, it has been decided to implement an analysis on the constructed network based on the human flows required in order to meet the needs of clients by exploiting the information that emerges from the network topology. In particular, centrality and Community Discovery algorithms have been implemented on both static and dynamic networks.
Finally, after mentioning some work done during the internship, the results obtained are presented and discussed, focusing on possible future improvements and applications.