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Using crowdsourced data to analyze transport crime

Sternberg, Henrik LU and Lantz, Björn (2018) In International Journal of Logistics Research and Applications
Abstract

Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers... (More)

Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers operate more frequently. For policymakers and practitioners, these findings provide useful guidance for the planning of security measures. To the authors’ knowledge, this paper is the first exploratory study of its kind that uses a combination of official statistics and crowdsourced data.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
criminology, crowdsourcing data, social sustainability, statistical analysis, Transport crime
in
International Journal of Logistics Research and Applications
pages
15 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85040981728
ISSN
1367-5567
DOI
10.1080/13675567.2018.1431211
language
English
LU publication?
yes
id
88cfc16a-a9b9-4520-87b1-7d73f91a404d
date added to LUP
2018-02-05 13:36:31
date last changed
2018-05-29 12:29:28
@article{88cfc16a-a9b9-4520-87b1-7d73f91a404d,
  abstract     = {<p>Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers operate more frequently. For policymakers and practitioners, these findings provide useful guidance for the planning of security measures. To the authors’ knowledge, this paper is the first exploratory study of its kind that uses a combination of official statistics and crowdsourced data.</p>},
  author       = {Sternberg, Henrik and Lantz, Björn},
  issn         = {1367-5567},
  keyword      = {criminology,crowdsourcing data,social sustainability,statistical analysis,Transport crime},
  language     = {eng},
  month        = {01},
  pages        = {15},
  publisher    = {Taylor & Francis},
  series       = {International Journal of Logistics Research and Applications},
  title        = {Using crowdsourced data to analyze transport crime},
  url          = {http://dx.doi.org/10.1080/13675567.2018.1431211},
  year         = {2018},
}