Spatial and Spatiotemporal Data Mining

Shashi Shekhar, Yan Li, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Zhe Jiang

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

The significant growth of spatial and spatiotemporal (SST) data collection as well as the emergence of new technologies has heightened the need for automated discovery of spatiotemporal knowledge. SST data mining techniques are crucial to organizations which make decisions based on large SST datasets. The interdisciplinary nature of SST data mining and the complexity of SST data and relationships pose statistical and computational challenges. This article provides an overview of recent advances in SST data mining and reviews common SST data mining techniques organized by major pattern families, based on an introduction to SST data types and relationships as well as the statistical background. New trends and research needs are also summarized, including SST data mining in network space and SST big data platform development.

Original languageEnglish (US)
Title of host publicationGis Applications for Socio-Economics and Humanity
PublisherElsevier Inc.
Pages264-286
Number of pages23
Volume3
ISBN (Electronic)9780128046609
ISBN (Print)9780128047934
DOIs
StatePublished - Jul 21 2017

Keywords

  • Anomaly
  • Association
  • Change footprint
  • Computing
  • Data mining
  • Hotspots
  • Partition
  • Prediction model
  • Spatial
  • Spatiotemporal
  • Statistics
  • Summarization

Fingerprint

Dive into the research topics of 'Spatial and Spatiotemporal Data Mining'. Together they form a unique fingerprint.

Cite this