  Self-organizing map(SOM) have been widely applied in clustering, this paper
focused on centroids of clusters and what they reveal. When the input vectors
consists of time, latitude and longitude, the map can be strongly linked to
physical world, providing valuable information. Beyond basic clustering, a
novel approach to address the temporal element is developed, enabling 3D SOM to
track behaviors in multiple periods concurrently. Combined with adaptations
targeting to process heterogeneous data relating to distribution in time and
space, the paper offers a fresh scope for business and services based on
temporal-spatial pattern.
