Due to the advances in remote sensing and sensor networks, different types of dynamic and spatio-temporal datasets become increasingly available. Extracting spatial and temporal patterns from such datasets is very important as it has many applications, such as understanding climate change, geo-targeting, and environment protection. Traditional clustering techniques are inefficient in clustering spatio-temporal data because they do not incorporate the idiosyncrasies of the spatial domain and temporal domain, and do not consider interactions between the two domains. In the presented dissertation research, novel spatio-temporal clustering algorithms and frameworks will be investigated, and post-processing analysis techniques will be developed which visualize and characterize the obtained clusters. The second goal of the dissertation research is to apply the framework to challenging problems of our society while evaluating the proposed algorithms and frameworks and making contributions to their solution. Namely, traffic fatality and air pollution datasets will be used for this purpose. We also present preliminary work in which two spatio-temporal clustering algorithms called ST-SSN and ST-SEP-SSN were evaluated in a case study involving ozone pollution events in the Houston-Galveston-Brazoria (HGB) area, providing a motivation for the planned dissertation research.
['Yongli Zhang', 'Christoph F. Eick']
Novel clustering and analysis techniques for mining spatio-temporal data