Spatial Data Mining


Spatial data sets are at the heart of a variety of scientific and engineering domains, from computational fluid dynamics to distributed sensor and actuator networks to structural bioinformatics.  Rapid advances in simulation and experimentation in these domains are yielding an increasing reliance on efficient and effective spatial data mining algorithms.  These developments demand effective cross-fertilization and consolidation of computational techniques from fields such as data mining, qualitative spatial reasoning, scientific computing, and statistical methodology, in the context of significant applications.  The SIAM-DM 2006 Workshop on Spatial Data Mining provides a forum such an exchange.

In order to focus the discussion, we have formulated a set of challenge problems in one significant application area: pandemic detection and response.  Pandemic influenza viruses have demonstrated an ability to spread worldwide within months or even weeks. Controlling the spread of a pandemic requires early detection via appropriate surveillance, along with implementation of corresponding control measures (e.g., isolation of cases, quarantine of contacts, antiviral drug treatment and prophylaxis).  Spatial data mining challenges include developing a synthetic time-varying social network capturing collocation and effective contact patterns, conducting model-based data aggregation using the derived network in order to identify the onset of disease and other qualitative indicators of disease spread, and using the structure of the network to identify critical individuals and locations for targeted detection and vaccination.

The workshop includes an introduction to challenges in pandemic preparedness by Madhav Marathe, an invited talk, presentations of four papers addressing spatial data mining challenges in the pandemic preparedness context and two papers addressing general issues in spatial data mining, and a discussion period for comparing, contrasting, identifying emergent themes, and so forth.

The program chairs are Chris Bailey-Kellogg and Naren Ramakrishnan, and the program committee is as follows:

Chris Bailey-Kellogg, Dartmouth (co-chair)
Jochen Garcke, Australian National University

Jiawei Han, University of Illinois at Urbana-Champaign
George Karypis, University of Minnesota
Tao Li, Florida International University
Madhav Marathe, Virginia Bioinformatics Institute
Naren Ramakrishnan, Virginia Tech (co-chair)
Srinivasan Parthasarathy, The Ohio State University
Shashi Shekhar, University of Minnesota
Feng Zhao, Microsoft Research


1:30  Welcome and introduction
1:35  Introduction to pandemic preparedness challenge problem
    Madhav Marathe
2:00  Regular papers
    Mining and Visualizing Spatial Interaction Patterns for Pandemic Response
      Diansheng Guo
    Process Driven Spatial and Network Aggregation for Pandemic Response
      Robert Savell, Wayne Chung
    Containment Policies for Transmissible Diseases
      Shirish Tatikonda, Sameep Mehta, Srinivasan Parthasarathy
    Aggregation of Location Attributes for Prediction of Infection Risk
      Slobodan Vucetic, Hao Sun
3:00  Break
3:30  Invited talk
4:30  Short papers
    Spatial-Temporal Data Mining in Traffic Incident Detection
      Ying Jin, Jing Dai, Chang-Tien Lu
    Mining Spatial Trends by a Colony of Cooperative Ant Agents
      Ashkan Zarnani, Masoud Rahgozar
4:50  Discussion: mining spatial data
5:30  Adjourn