  Time-Spatial data plays a crucial role for different fields such as traffic
management. These data can be collected via devices such as surveillance
sensors or tracking systems. However, how to efficiently an- alyze and
visualize these data to capture essential embedded pattern information is
becoming a big challenge today. Classic visualization ap- proaches focus on
revealing 2D and 3D spatial information and modeling statistical test. Those
methods would easily fail when data become mas- sive. Recent attempts concern
on how to simply cluster data and perform prediction with time-oriented
information. However, those approaches could still be further enhanced as they
also have limitations for han- dling massive clusters and labels. In this
paper, we propose a visualiza- tion methodology for mobility data using
artificial neural net techniques. This method aggregates three main parts that
are Back-end Data Model, Neural Net Algorithm including clustering method
Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN)
for ex- tracting the features and lastly a solid front-end that displays the
results to users with an interactive system. SOM is able to cluster the
visiting patterns and detect the abnormal pattern. RNN can perform the predic-
tion for time series analysis using its dynamic architecture. Furthermore, an
interactive system will enable user to interpret the result with graph- ics,
animation and 3D model for a close-loop feedback. This method can be
particularly applied in two tasks that Commercial-based Promotion and abnormal
traffic patterns detection.

Computer Vision and Pattern Recognition