Identifying users profiles from mobile calls habits
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
A visual analytics framework for spatio-temporal analysis and modelling
Data Mining and Knowledge Discovery
A visual exploration of mobile phone users, land cover, time, and space
Pervasive and Mobile Computing
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We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios.