Spatial and Spatio-temporal Data Mining

Summary form only given. The recent advances and price reduction of technologies for collecting spatial and spatio-temporal data like Satellite Images, Cellular Phones, Sensor Networks, and GPS devices has facilitated the collection of data referenced in space and time. These huge collections of data often hide interesting information which conventional systems and classical data mining techniques are unable to discover. Spatial and spatio-temporal data are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Spatial and spatio-temporal data require complex data preprocessing, transformation, data mining, and post-processing techniques to extract novel, useful, and understandable patterns. The importance of spatial and spatio-temporal data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, trajectories of moving objects generated by mobile devices, etc. Applications include Mobile-commerce industry (location-based services), climatologically effects of El Nino, land-use classification and global change using satellite imagery, finding crime hot spots, local instability in traffic, migration of birds, fishing control, pedestrian behavior analysis, and so on. Thus, new methods are needed to analyze spatial and spatio-temporal data to extract interesting, useful, and non-trivial patterns. The main goal of this tutorial is to disseminate this research field, giving an overview of the current state of the art and the main methodologies and algorithms for spatial and spatio-temporal data mining. This tutorial is directed to researches and practitioners, experts in data mining, analysts of spatial and spatio-temporal data, as well as knowledge engineers and domain experts from different application areas.