LAUSR

Spatiotemporal analysis ranges from simple univariate descriptive statistics to more complex multivariate analyses. Such an analysis can be used to explore spatial and temporal patterns in different domains, i.e., spatial and temporal information of subscribers in Internet of Things networks. Most spatial and temporal analysis techniques are based on conventional quantitative and traditional data mining approaches, such as the $k$ -means algorithm. Clustering approaches based on artificial neural networks can be more efficient since they can reveal nonlinear patterns. Hence, in this work, we tailor an AI-based spatiotemporal unsupervised model such that the underlying pattern structure of a mobile phone network can be revealed, relative similarity among interactions extracted, and the associated patterns analyzed. The proposed approach is based on an optimized self-organizing feature map. It deals with high-dimensionality concerns and preserves inherent data structures. By identifying the spatial and temporal associations, decision makers can explore dominant interactions that can be used for resource optimization in network planning, content distribution, and urban planning.