Big data in crime statistics: Using Google Trends to measure victimization in designated market areas across the United States
| Autor: | Yu-Hsuan Liu, Kevin T Wolff, Tzu-Ying Lo |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Methodological Innovations, Vol 16 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2059-7991 20597991 |
| DOI: | 10.1177/20597991231183962 |
| Popis: | Google Trends (GT) data could potentially supplement traditional crime measurement strategies, allowing criminologists to better understand crime statistics on a macro level. This study assesses the validity of GT crime estimates. The findings indicate that GT data are reliable for estimating MVT, larceny, and rape. Additionally, we illustrate how to use GT to identify places with high rates of unreported offenses. The results of this study demonstrate the feasibility of leveraging open-source big data such as GT to supplement traditional sources of crime data, particularly for categories of crime with substantial underreporting rates. Results suggest the GT rape measure may be a more accurate estimate of the true incidence of rape than the measure drawn from the Uniform Crime Report (UCR). The limitations associated with the use of GT to generate estimates of crime are also discussed. |
| Databáze: | Directory of Open Access Journals |
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