- 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:
pdf (2.7 Mbyte) , 30 slides, 2011.
The importance of spatial and spatio-temporal data mining is growing
with the increasing incidence and importance of large datasets such as
maps, virtual globes, repositories of remote-sensing images,
the decennial census and collections of trajectories (e.g. gps-tracks).
Environment and Climate (global change, land-use classification),
Public Health (e.g. monitoring and predicting spread of disease),
Public Safety (e.g. crime hot spots),
Public Security (e.g. common operational picture),
M(obile)-commerce (e.g. location-based services),
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.
Spatial, Spatio-temporal, Auto-correlation, Data Mining.
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).
Some of the results discussed in this talk appeared
in the following publications:
Spatiotemporal Data Mining: A Computational Perspective ,
International Journal on Geo-Informtion, 4(4):2306-2338, 2015 (DOI:
Identifying patterns in spatial information: a survey of methods
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, (
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 ,
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.
This talk has been presented at following forums:
- Keynote at the
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.
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
- 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 ,