- 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 Minnesota, 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)
The importance of
spatial data science and
is growing with the rise of
spatial and spatiotemporal big data (e.g.,
trajectories, remote-sensing imagery, census and geo-social media)
for making bigger faster richer maps, solving inverse geo-problems,
fact-checking geo-content, answering geo-content-based queries, discovering novel, intererstting and useful
spatiotemporal patterns, etc.
Societal use cases include
Smart Cities ,
monitoring global crops or disease-spread,
and apps for navigation, ride-sharing, delivery, etc.
Traditional data science and AI (e.g., machine learning) perform poorly on spatial data due to many reasons.
First, spatial data is embedded in a continuous space and traditional statistics (e.g., correlation) are not robust to
the modifiable areal unit problem.
Second, spatial data-items have extended footprints (e.g., polygons) and implicit
relationships (e.g., distance, touch). Third, high cost of spurious patterns requires guardrails (e.g., noise
tolerance) to reduce false positives.
spatial autocorrelation and variability violate the common assumption of data
samples being generated independently from identical distributions, and risk inaccuracy.
Thus, the talk calls for including
spatial perspectives in data science courses and currricula
surveys methods for spatial
classification and prediction (e.g., spatial autoregression,
spatial decision trees,
spatial variability aware neural networks
) along with techniques for discovering interesting, useful and non-trivial patterns such as
noise-robust hotspots (e.g.,
arbitrary shapes ),
spatial outliers ,
spatio-temporal counterparts (e.g.,
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).
A UCGIS Call to Action: Bringing the Geospatial Perspective to Data Science Degrees and Curricula , 2019.
AM-97 - An Introduction to Spatial Data Mining ,
The Geographic Information Science & Technology Body of Knowledge (4th Quarter 2020
Edition), John P. Wilson (Ed.). DOI:10.22224/gistbok/2020.4.5.
What is special about spatial data science and Geo-AI?
In 33rd International Conference on Scientific and Statistical Database Management (SSDBM 2021),
ACM, page 271, 2021. DOI:https://doi.org/10.1145/3468791.3472263.
L. Chauhan and S. Shekhar.
GeoAI-Accelerating a Virtuous Cycle between AI and Geo,
Proc. 13th ACM Intl. Conference on Contemporary Computing (IC3-2021), pp. 355-370.
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 ,
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10.3390/ijgi4042306). (w/ Z. Jiang, R. Ali, E. Efteliglu, X. Tang, V. Gunturi, and X. Zhou).
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S. Shekhar, M. R. Evans, J. M. Kang and P. Mohan,
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193-214, 1(3), May/June 2011. (DOI: 10.1002/widm.25).
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( 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 Big Data Congress 2017: 232-250 (with S. Prasad et al.)..
PAPERS ON SPECIFIC PATTERN FAMILIES
Towards Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach ,
ACM Transactions on Intelligent Systems and Technology
12(6):1-21, December 2021. https://doi.org/10.1145/3466688
(Note A Summary of Results
appeared in the 1st ACM SIGKDD
Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
(Deepspatial 2020), 2020, Best Paper)
(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.)