The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

Minxuan Lan, Lin Liu, Andres Hernandez, Weiyi Liu, Hanlin Zhou and Zengli Wang
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Minxuan Lan: Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
Lin Liu: Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
Andres Hernandez: Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
Weiyi Liu: Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221, USA
Hanlin Zhou: Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
Zengli Wang: Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA

Sustainability, 2019, vol. 11, issue 23, 1-17

Abstract: As a measurement of the residential population, the Census population ignores the mobility of the people. This weakness may be alleviated by the use of ambient population, derived from social media data such as tweets. This research aims to examine the degree in which geotagged tweets, in contrast to the Census population, can explain crime. In addition, the mobility of Twitter users suggests that tweets as the ambient population may have a spillover effect on the neighboring areas. Based on a yearlong geotagged tweets dataset, negative binomial regression models are used to test the impact of tweets derived ambient population, as well as its possible spillover effect on theft crimes. Results show: (1) Tweets count is a viable replacement of the Census population for spatial theft pattern analysis; (2) tweets count as a measure of the ambient population shows a significant spillover effect on thefts, while such spillover effect does not exist for the Census population; (3) the combination of tweets and its spatial lag outperforms the Census population in theft crime analyses. Therefore, the spillover effect of the tweets derived ambient population should be considered in future crime analyses. This finding may be applicable to other social media data as well.

Keywords: tweet; crime; ambient population; spatial lag; neighborhood; negative binomial (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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