Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. (November 2015)

• Record Type: Journal Article
• Authors: Steiger, Enrico; Westerholt, René; Resch, Bernd; Zipf, Alexander
• Is Part Of: Computers, environment and urban systems. Volume 54(2015)
• Page Start: 255
• Page End: 265
• Publication Date: 2015-11
• Subjects: Crowdsourcing of human activities -- LBSN -- Twitter -- Spatial autocorrelation -- Semantic topic modeling; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.09.007
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Detailed knowledge regarding the whereabouts of people and their social activities in urban areas with high spatial and temporal resolution is still widely unexplored. Thus, the spatiotemporal analysis of Location Based Social Networks (LBSN) has great potential regarding the ability to sense spatial processes and to gain knowledge about urban dynamics, especially with respect to collective human mobility behavior. The objective of this paper is to explore the semantic association between georeferenced tweets and their respective spatiotemporal whereabouts. We apply a semantic topic model classification and spatial autocorrelation analysis to detect tweets indicating specific human social activities. We correlated observed tweet patterns with official census data for the case study of London in order to underline the significance and reliability of Twitter data. Our empirical results of semantic and spatiotemporal clustered tweets show an overall strong positive correlation in comparison with workplace population census data, being a good indicator and representative proxy for analyzing workplace-based activities. Highlights: Tweets show characteristic spatiotemporal semantic frequencies indicating human activities. Statistically significant hot- and cold spots of human activity clustered tweets Strong correlation (r = 0.75) of classified tweets with UK census workplace densities Weak correlation (r = 0.08) of classified tweets with UK census residential densities