AAG 2015 CFP: Spatial Data Mining and Big Data Analytics
Big and dynamic spatial data have been, and continue to be, collected with modern data acquisition techniques such as global positioning systems (GPS), high-resolution remote sensing, census surveys, and internet-based volunteered geographic information.While these data offer unprecedented opportunities to advance our understanding of complex geographic processes and phenomena, there are many challenging research questions in analyzing such data to obtain new knowledge. We invite research contributions in the theory, methodology, implementation, and application of spatial data mining, simulation, and visual analytics for big spatial data analytics. Potential topics include (but not limited to):
- Theories and models to represent, quantify, and enable discovery of new types of spatial patterns and relationships;
- Computational, statistical, and visual analytical methodologies for big data analytics, knowledge discovery, and decision support in geographic domains;
- Domain-specific data analytics and applications: public health, spatial epidemiology, transportation, urban mobility, climate change, crime analysis, migration, geo-social networks, among others.
- Simulation, benchmark data generation, complexity modeling, predictive analytics;
- Big data collection, curating and management methodologies for heterogeneous data, e.g., texts, videos, images, etc.
UPDATE:The special sessions at AAG went very well, with six paper sessions and one panel discussion session. Panelists include:
- Michael F. Goodchild - University of California - Santa Barbara
- May Yuan - University of Texas - Dallas
- Paul A. Longley - University College London
- Shih-Lung Shaw - University of Tennessee
- Donna J. Peuquet - Pennsylvania State University
- Sean Ahearn - Hunter College - City University
- Robert Stewart - Oak Ridge National Lab (ORNL)