Skip to main navigation Skip to search Skip to main content

Analyzing Big Spatial and Big Spatiotemporal Data: A Case Study of Methods and Applications

  • North Carolina State University
  • Northeastern University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Spatial and spatiotemporal data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the data collected over time and space. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies, emphasizes the need for developing new and computationally efficient methods tailored for analyzing big data. In this chapter, we study approaches to handle big spatial and spatiotemporal data by closely looking at the computational and I/O requirements of several analysis algorithms for such data. We also study applications of such methods in domains where data is encountered at a massive scale.

Original languageEnglish
Title of host publicationHandbook of Statistics
Subtitle of host publicationBig Data Analytics, 2015
EditorsVenu Govindaraju, Vijay V. Raghavan, C.R. Rao
PublisherElsevier
Pages239-258
Number of pages20
ISBN (Print)9780444634924
DOIs
StatePublished - 2015

Publication series

NameHandbook of Statistics
Volume33
ISSN (Print)0169-7161

Keywords

  • Big spatial data
  • Biomass monitoring
  • Climate analysis
  • Object recognition
  • Spatial
  • Spatiotemporal data mining

Fingerprint

Dive into the research topics of 'Analyzing Big Spatial and Big Spatiotemporal Data: A Case Study of Methods and Applications'. Together they form a unique fingerprint.

Cite this