- Summary : pdf , 6 slides, 0.75 MB, 2009 ;
- What is special about spatial data science and GeoAI? : (55-slides :
pdf (4 Mb) ,
pptx (20 Mb)
and Quiz (
Faculty Development Programme on
Applications of Artificial Intelligence on Geospatial Data
AICTE Training and Learning Academy,
Maulana Abul Kalam Azad University of Technology, and
State Technological University of West Bengal, India
July 26th-30th, 2021.
- What is special about spatial data science and GeoAI? : (50-slides :
pdf (4 Mb) ,
ppt (20 Mb)
) at the
2021 Workshop on Data Science and Curation: Spatial Data Science ,
organized by IEEE GRSS Bangalore Chapter and
Technology Innovation Hub on Data Sc., Big Data Analytics, and Data Curation,
Indian Statistical Institute, Banglore, India,
June 24th-25th, 2021.
What is special about spatial and spatiotemporal data science? ,
Digital Technology Center , University of Minnestoa, March 11th, 2021.
- What is special about GeoAI? (
Panel on Community Voices,
GEO.AI : Redefining Geospatial Conference,
World Geospatial Council, Dec. 7th- 9th, 2020.
- What is special about spatial? : (18-slides :
ACM SIGKDD Workshop on Deep Learning for Spatio-temporal Data, Applications, and Systems,
August 24, 2020.
- October 1st, 2019, IS-GEO : : What is special about Spatial Data Science :
pptx (30 slides, 15 MB)
- Spatial data mining and Transportation:
- 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:
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 ;
The importance of spatial data science and Geo-AI is growing
with the rise of spatial and spatiotemporal big data (e.g.,
trajectories, remote-sensing images, census and geo-social media).
Societal use cases include
Agriculture ( global crop monitoring,
Location-based services (e.g., navigation, ride-sharing),
Public Health (e.g., monitoring disease spread),
Environment and Climate (change detection, land-cover classification),
Smart Cities (e.g., mapping buildings),
Classical data science and AI (e.g., machine learning) often perform poorly when applied
to spatial data sets because of the many reasons.
First, spatial data is embedded in a continuous space and classical statistics (e.g., correlation)
are not robust to the
modifiable areal unit problem.
Second, spatial data-items have extended footprints (e.g., line strings,
polygons) and implicit relationships (e.g., distance, touch).
Third, high cost of spurious patterns requires guardrails (e.g., statistical significance tests)
to reduce false positives.
Furthermore, spatial autocorrelation and variability violate
the classical assumption of data samples being generated independently from identical distributions,
which risk models that are either inaccurate or inconsistent with the data.
Thus, new methods are needed to analyze spatial data.
This talk surveys common and emerging methods for spatial classification and prediction
(e.g., spatial autoregression,
spatial variability aware neural networks),
as well as techniques for discovering interesting, useful and non-trivial patterns
such as hotspots (e.g., circular,
and their spatio-temporal counterparts.
Spatial, Spatio-temporal, Auto-correlation, Data Mining, Machine Learning, Statistics.
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).
Data Science for Earth: An Earth Day Report, ACM SIGKDD Explor. Newsl. 22(1):4-7, June 2020.
(E. Eftelioglu, S. Shekhar, J. Hudson, L. Joppa, C. Baru, and V. Janeja,)
Transdisciplinary Foundations of Geospatial Data Science
ISPRS International Journal of Geo-Informatics, 6(12), 2017.
(with Y. Xie, E. Eftelioglu, R. Ali, X. Tang, Y. Li, and R. Doshi)
Spatiotemporal Data Mining: A Computational Perspective ,
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
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 Data 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, (
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 Software
compiled by Dr. B. Kazar in 2004-2005.
PAPERS ON SPECIFIC PATTERN FAMILIES
Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results ,
1st ACM SIGKDD
Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
(Deepspatial 2020), San Diego, CA, August 24, 2020.
(J. Gupta, Y. Xie and S. Shekhar).
Significant DBSCAN towards Statistically Robust Clustering ,
Proc. 16th Intl. Symposium on Spatial and Temporal Databases (SSTD 19), 31-40, ACM (Best Paper).
(Y. Xie and S. Shekhar).
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
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.)
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 ,