As the volume of spatial and spatio-temporal data continues to increase significantly due to both the growth of database archives and the increasing number and resolution of spatio-temporal sensors, automated and semi-automated pattern analysis becomes more essential. Spatial and spatio-temporal (ST) data analyses have emerged in recent decades to develop understanding of the spatial and spatio-temporal characteristics and patterns. However, in the last decade, the growth in variety and volume of observational data, notably spatial and spatio-temporal data, has out-paced the capabilities of analytical tools and techniques.
Major limitations of existing classical data mining models and techniques include the
following. First, these do not adequately model richer temporal semantics of data observations (e.g. co-occurrence patterns of moving objects, emerging and vanishing patterns, multi-scale cascade patterns, periodic patterns). Second, these do not take into account time dimension of the data observations. Third, these do not provide sufficient interest measures and computationally efficient algorithms to discover spatial and spatio-temporal
co-occurrence patterns. These limitations represent critical barriers in several application domains that require to analyze huge datasets.
In this dissertation, I proposed addressed these limitations by i) providing a framework
to model the rich semantics of the ST patterns of data observations by developing a taxonomy of spatial and ST co-occurrence patterns, ii) designing new techniques that are taking into account the time dimension of the data, and iii) developing new monotonic composite interest measures and scalable algorithms. The proposed approaches reduced the manual effort by reducing the plausible set of hypotheses. Major focus would be on developing scalable algorithms to mine spatial and ST co-occurrence patterns.