The 2013 International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-13) builds on the success of previous workshops (SSTDM/ICDM-06, SSTDM/ICDM-07, STDM/ICDE-07, SSTDM/ICDM-08, SSTDM/ICDM-09, SSTDM/ICDM-10, SSTDM/ICDM-11). SSTDM provides a unique platform for researchers dealing will all types of spatial, temporal, and spatiotemporal data to share and dissiminate recent research results.

Synopsis: Advances in remote sensors and sensor networks have resulted in the generation of massive volumes of disparate, dynamic, and geographically distributed spatiotemporal data. This has recently been complemented by advances in social media that have also resulted in new types of spatiotemporal information that is contributed by the general public. At the same time, the interest for this information is expanding, as scientists from diverse disciplines and common citizens are interested in the information that can be extracted from such spatiotemporal datasets. However, one could argue that we find ourselves in a data-rich but information-poor environment. The rate at which geospatial data are being generated by diverse sensors and platforms clearly exceeds our ability to organize and analyze them to extract patterns that signify events of importance in our dynamically changing world. Computer science and geoinformatics are collaborating in order to address these scientific and computational challenges, and to provide innovative and effective solutions.

More specifically, efficient and reliable data mining techniques are needed for extracting useful geoinformation from large heterogeneous, often multi-modal spatiotemporal datasets. Traditional data mining techniques are ineffective as they do not incorporate the idiosyncrasies of the spatial domain, which include (but are not limited to) spatial autocorrelation, spatial context, and spatial constraints. Extracting useful geoinformation from several terabytes of streaming multi-modal data per day also demands the use of modern computing in all its forms. Thus, we invite all researchers and practioners to participate in this event and share, contribute, and discuss the emerging challenges in spatial and spatiotemporal data mining.

Topics: The major topics of interest to the workshop include but are not limited to:

Theoretical foundations of spatial and spatiotemporal data mining
Social media data mining for geoinformatics
Mining linked geospatial data
Spatial and spatiotemporal analogues of interesting patterns: frequent itemsets, clusters,outliers, and the algorithms to mine them
Spatial classification: methods that explicitly model spatial context
Spatial and spatiotemporal autocorrelation and heterogeneity, its quantification and
efficient incorporation into the data mining algorithms
Image (multispectral, hyperspectral, aerial, radar) information mining, change detection
Role of uncertainty in spatial and spatiotemporal data mining
Integrated approaches to multi-source and multimodal data mining
Resource-aware techniques to mine streaming spatiotemporal data
Spatial and spatiotemporal data mining at multiple granularities (space and time)
Data structures and indexing methods for spatiotemporal data mining
Spatial and Spatiotemporal online analytical processing, data warehousing
Geospatial Intelligence
Climate Change, Natural Hazards, Critical Infrastructures
High-performance SSTDM
Applications that demonstrate success stories of spatial and spatiotemporal data mining

Proceedings: Accepted papers will be included in a ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press, which will also be included in the IEEE Digital Library.

Paper Submission: This is an open call-for-papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 6 pages) describing work-in-progress or case studies. Only original and high-quality papers conforming to the ICDM 2013 standard guidelines will be considered for this workshop.