| What is special about mining spatial and spatio-temporal datasets? |

| Shashi Shekhar : Biography , Homepage , Picture |

| Computer Science Department, University of Minnesota. |

| http://www.cs.umn.edu/~shekhar |

**
VIDEOS:
**

- A colloquium at the UCF CRCV, 2014.
- A sequence of 8 short presentations for a Coursera course titled From GPS and Google Maps to Spatial Computing in Fall 2014. These may also be accessed via Coursera by scrolling to Module 4 of this page .

**
SLIDES:
**

- Summary : pdf , 6 slides, 0.75 MB, 2009 ;
- Oct. 2015: Spatial and spatio-temporal data mining : pdf , 70 slides, 3 MB;
- 2007: Spatial and spatio-temporal data mining : pdf , 94 slides, 33 MB;
- 2005: Spatial data mining pdf , 69 slides, 2 MB ;
- SDM and Physical System Modeling : pdf (1.6 Mbyte) , 12 slides, May 2015.
- Spatial Data mining and Human Health: pdf (1.6 Mbyte) , 22 slides, April 2012.
- Spatial Data mining to Understand Climate Change: pdf (3 Mbyte) , 20 slides, August 2011.
- Data mining and Transportation:
- Transportation Audience: pdf (1 Mbyte) , 30 slides, 2011.
- Computer Science and Data Mining Audience: pdf (1.6 Mbyte) , 47 slides, 2009

- Eco-Routing: pdf (2.7 Mbyte) , 30 slides, 2011.

Classical data mining techniques often perform poorly when applied to spatial and spatio-temporal data sets because of the many reasons. First, these dataset are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Second, patterns are often local where as classical data mining techniques often focus on global patterns. Finally, one of the common assumptions in classical statistical analysis is that data samples are independently generated. When it comes to the analysis of spatial and spatio-temporal data, however, the assumption about the independence of samples is generally false because such data tends to be highly self correlated. For example, people with similar characteristics, occupation and background tend to cluster together in the same neighborhoods. In spatial statistics this tendency is called autocorrelation. Ignoring autocorrelation when analyzing data with spatial and spatio-temporal characteristics may produce hypotheses or models that are inaccurate or inconsistent with the data set.

Thus new methods are needed to analyze spatial and spatio-temporal data to interesting, useful and non-trivial patterns. This talk surveys some of the new methods including those for discovering interactions (e.g. co-locations , co-occurrences, tele-connections), detecting spatial outliers and location prediction along with emerging ideas on spatio-temporal pattern mining.

**
KEYWORDS: **
Spatial, Spatio-temporal, Auto-correlation, Data Mining.

**
ACKNOWLEDGMENTS: **
This work was supported in part by
the National Science Foundation,
the U.S. Department of Defense,
the National Aeronautics and Space Administration
the Federal Highway Authority,
and the University of Minnesota (e.g. Center for Transportation Studies).

**
NOTE:**
Some of the results discussed in this talk appeared
in the following publications:

- Spatiotemporal Data Mining: A Computational Perspective , ISPRS International Journal on Geo-Informtion, 4(4):2306-2338, 2015 (DOI: 10.3390/ijgi4042306).
- Identifying patterns in spatial information: a survey of methods ( pdf ), S. Shekhar, M. R. Evans, J. M. Kang and P. Mohan, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 193-214, 1(3), May/June 2011. (DOI: 10.1002/widm.25).
- Spatial and Spatio-temporal Data Mining: Recent Advances, ( pdf ), S. Shekhar, V. R. Raju and M. Celik, Next Generation of Data Mining, Chapman & Hall/CRC, 2008, isbn 1420085867, (Ed. H. Kargupta, J. Han, P. Yu, R. Motwani, V. Kumar). Proc. NSF 2nd workshop on Future Directions in Data Mining (2007).
- Trends in Spatial Data Mining ( pdf ) , S. Shekhar, P. Zhang, V. R. Raju and Y. Huang, Data Mining: Next Generation Challenges and Future Directions, MIT Press, 2004, isbn 0-262-61203-8 (Ed. H. Kargupta et al). Proc. NSF 1st workshop on Future Directions in Data Mining (2003).
- Spatial Data Mining Toolkit for Generating MSDS (aka TopoAssistant) (Topic No. A03-129), SBIR Phase I, US Army Topographic Eng. Center, June 2004, Architecture Technology Corporation, Final Report , Slides .
- Mining Colocation patterns from spatial datasets (slides, papers). .
- Spatial Databases: A Tour (Chapter 7 on Spatial Data Mining), S. Shekhar and S. Chawla, Prentice Hall 2003, ISBN 0-13-017480-7.
- A Summary of Spatial Statistics and Spatial Data Mining Softwares compiled by Dr. B. Kazar in 2004-2005.

**
NOTE:**
This talk has been presented at following forums:

- Keynote at the ISPRS - Symposium on Spatial-Temporal Analysis and Data Mining , University College, London, July 18-20th, 2011.
- Invited talk at National Academies Transportation Research Board, Workshop on Pervasive Data for Transportation: Innovations in Distributed and Mobile Information Discovery in ITS and LBS January 23rd, 2011, Washington D.C.
- Invited talk at NSF Next Generation Data Mining Summit: Dealing with the Energy Crisis, Greenhouse Emissions, and Transportation Challenges , (Oct. 1st - 3rd, 2009)
- Invited talk at NSF Workshop on Geospatial and Geotemporal Informatics , Jan. 2009.
- Invited C. S. Colloquium , University of Houston (Feb. 19th, 2006), Texas, USA.
- Keynote at IEEE ICDM Workshop on Spatial and Spatio-temporal Data Mining (SSTDM) , Dec. 18th, 2006, Hong Kong.
- Microsoft Virtual Earth Workshop (11/30-12/1, 2006), Seattle, USA.
- Keynote at ISPRS 2005 Spatial Data Mining Workshop (11/24-25, 2005), METU, Ankara, Turkey.
- Keynote at GeoInfo 2005 - VII Brazilan Symposium on Geoinformatics (11/20-23, 2005) Campos do Jordao, Brazil
- Keynote at Ninth Bi-annual International Symposium on Spatial and Temporal Databases (8/24-26/05) (August, 2005), Angora dos Rias, Brazil
- Keynote at NSF Workshop on Phenology (6/16-17/05) :
- Invited talk at Boston University (3/21/05)
- Keynote at GIScience 2004 ( 3rd Bi-annual Intl. Conf. on Geographic Info. Sc. )
- SAS DMT Conference 10/03
- Slides for earlier talks on Spatial Data Mining at ARL PI Workshop June 2002 , NSF Spatial Data Analysis Workshop April 2002 , UCGIS Summer Assembly 2001 ,