Interactive Visualization of Cellphone Network Data Using D3: The Case of Ivory Coast

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Conference Proceeding

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The ability to implement a plethora of data mining algorithms on large data sets to gain valuable information and insights empowers today’s decision makers with decision support tools previously unavailable. Historic patterns, relationships as well as predictions are some of the valuable insights that can be garnered. Even though a primary focus of analyzing mobile phone datasets focus on revealing structural properties with the calling network (zang07, belik10, hidalgo08, nanavai08, ozgul11, onnela07, doran12), researchers also focused on using the datasets to capture communication and mobility patterns within a society. Recent advances in visualizing such complex patterns incorporate the social relationships among users (onnella07) integrated with their spatial positions (shen08). Techniques have also been proposed to visualize human mobility across a large city (kwan12), and to integrate sensor and mobile phone data for the purpose of infrastructure planning (pattath06). More recent advances integrate algorithmic techniques to intelligently collapse edges in network visualizations in order to remove ambiguity (luo12).


Presented at NetMob 2013, Boston, MA, May 1-3, 2013.