| 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.

**
SLIDES:
**

- Summary : pdf , 6 slides, 0.75 MB, 2009 ;
- October 1st, 2019, IS-GEO : : What is special about Spatial Data Science : pptx (30 slides, 15 MB)
- Spatial data mining and Transportation:
- Oct. 2019: NSF Workshop on Road Infrastructure Reimagined: pdf (30 slides, 5 MB) , pptx (30 slides, 27 MB)
- Feb. 2019: Center for Transportation Studies: pdf (30 slides, 1 MB) , pptx (30 slides, 10 MB)

- April 2018: Spatial and spatio-temporal data mining : pdf (80 slides, 4 MB) , pptx (80 slides, 20 MB)
- Oct. 2015: Spatial and spatio-temporal data mining : pdf, 70 slides, 3 MB ;
- May 2015: SDM and Physical System Modeling : pdf (1.6 Mbyte) , 12 slides .
- April 2012: Spatial Data mining and Human Health: pdf (1.6 Mbyte) , 22 slides.
- August 2011: Spatial Data mining to Understand Climate Change: pdf (3 Mbyte) , 20 slides.
- Data mining and Transportation:
- 2011: Transportation Audience: pdf (1 Mbyte) , 30 slides.
- 2009: Computer Science and Data Mining Audience: pdf (1.6 Mbyte) , 47 slides

- Eco-Routing: pdf (2.7 Mbyte) , 30 slides, 2011.
- 2007: Spatial and spatio-temporal data mining : pdf , 94 slides, 33 MB;
- 2005: Spatial data mining pdf , 69 slides, 2 MB ;

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 with implicit relationships, whereas classical datasets (e.g. transactions) are often discrete. Second, the cost of spurious patterns (e.g., false positives, chance patterns) is often high in spatial application domains. In addition, 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 discover interesting, useful and non-trivial patterns. This talk surveys some of the new methods including those for discovering hotspots (e.g., circular, linear, rings ), interactions (e.g. co-locations , cascade , 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).

** SURVEY PAPERS**

- Transdisciplinary Foundations of Geospatial Data Science ( html , pdf ) ISPRS International Journal of Geo-Informatics, 6(12), 2017. doi:10.3390/ijgi6120395. (with Y. Xie, E. Eftelioglu, R. Ali, X. Tang, Y. Li, and R. Doshi)
- Spatiotemporal Data Mining: A Computational Perspective , ISPRS International Journal on Geo-Informtion, 4(4):2306-2338, 2015 (DOI: 10.3390/ijgi4042306). (w/ Z. Jiang, R. Ali, E. Efteliglu, X. Tang, V. Gunturi, and X. Zhou).
- 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).
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data, IEEE Transactions on Knowledge and Dat Mining, 29(10):2318-2331, June 2017. ( DOI: 10.1109/TKDE.2017.2720168 ). (w/ A. Karpatne et al.).
- Parallel Processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap. IEEE BigData Congress 2017: 232-250 (with S. Prasad et al.)..
- 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.

** PAPERS ON SPECIFIC PATTERN FAMILES **

- Discovering colocation patterns from spatial data sets: a general approach, IEEE Trans. on Know. and Data Eng., 16(12), 2004 (w/ Y. Huang et al.).
- A join-less approach for mining spatial colocation patterns, IEEE Trans. on Know. and Data Eng.,18 (10), 2006. (w/ J. Yoo).
- Cascading Spatio-Temporal Pattern Discovery , IEEE Trans. Knowl. Data Eng. 24(11): 1977-1992, 2012 (w/ P. Mohan et al.).
- Detecting graph-based spatial outliers: algorithms and applications Proc.: ACM Intl. Conf. on Knowledge Discovery & Data Mining, 2001 (with Q. Lu et al.)
- A unified approach to detecting spatial outliers, Springer GeoInformatica, 7 (2), 2003. (w/ C. Lu, et al.)
- Discovering Flow Anomalies: A SWEET Approach , IEEE Intl. Conf. on Data Mining, 2008 (w/ J. Kang).
- Discovering personally meaningful places: An interactive clustering approach, ACM Trans. on Info. Systems (TOIS) 25 (3), 2007. (with C. Zhou et al.)
- A K-Main Routes Approach to Spatial Network Activity Summarization , IEEE Trans on Know. & Data Eng., 26(6), 2014. (with D. Oliver et al.)
- Significant Linear Hotspot Discovery< IEEE Trans. Big Data 3(2): 140-153, 2017, (w/ X.Tang et al.)
- Ring-Shaped Hotspot Detection, IEEE Trans. Know. and Data Eng., 28(12): 3367-3381, 2016, (w/ E. Eftelioglu et al.)
- Spatial contextual classification and prediction models for mining geospatial data , IEEE Transactions on Multimedia, 4 (2), 2002. (with P. Schrater et al.)
- Focal-Test-Based Spatial Decision Tree Learning, IEEE Trans. Knowl. Data Eng. 27(6): 1547-1559, 2015 (summary in Proc. IEEE Intl. Conf. on Data Mining, 2013) (w/ Z. Jiang et al.).
- Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey., Wiley Interdisc. Rew.: Data Mining and Know. Discovery 4(1), 2014. (with X. Zhou et al.)

**
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 ,