Big data-enabled social sensing in spatial analysis: Potentials and pitfalls

Chuan Liao, Daniel Brown, Ding Fei, Xuewen Long, Dan Chen, Shengquan Che

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

Over the past decade, there has been an unprecedented boom in the application of big data derived from location-aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta-analysis of big data-enabled social sensing in spatial analysis. Big data-enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data-enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.

Original languageEnglish (US)
Pages (from-to)1351-1371
Number of pages21
JournalTransactions in GIS
Volume22
Issue number6
DOIs
StatePublished - Dec 2018

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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