Next Article in Journal
Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements
Next Article in Special Issue
Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China
Previous Article in Journal
Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery
Previous Article in Special Issue
Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction
Open AccessArticle

Spatiotemporal Data Mining: A Computational Perspective

1
Department of Computer Science and Engineering, University of Minnesota, Twin Cities. 4-192, Keller Hall, 200 Union St. SE, Minneapolis, MN 55455, USA
2
Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi. B-402 Academic Block, IIIT-Delhi Okhla, Phase III, New Delhi 110020, India
3
Department of Management Sciences, University of Iowa. S210 John Pappajohn Business Building, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Emmanuel Stefanakis and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2306-2338; https://doi.org/10.3390/ijgi4042306
Received: 8 June 2015 / Revised: 20 September 2015 / Accepted: 12 October 2015 / Published: 28 October 2015
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs. View Full-Text
Keywords: spatiotemporal data mining; survey; review; spatiotemporal statistics; spatiotemporal patterns spatiotemporal data mining; survey; review; spatiotemporal statistics; spatiotemporal patterns
Show Figures

Figure 1

MDPI and ACS Style

Shekhar, S.; Jiang, Z.; Ali, R.Y.; Eftelioglu, E.; Tang, X.; Gunturi, V.M.V.; Zhou, X. Spatiotemporal Data Mining: A Computational Perspective. ISPRS Int. J. Geo-Inf. 2015, 4, 2306-2338.

Show more citation formats Show less citations formats

Article Access Map

1
Only visits after 24 November 2015 are recorded.
Back to TopTop