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Updated: Oct 8, 2025
07:36
An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
Published on: May 3, 2016
8.7K
Uncovering dynamic textual topics that explain crime.
1School of Mathematics, University of Leeds, Leeds LS2 9JT, UK.
Royal Society Open Science
|December 30, 2021
Summary
This study introduces a new method using crime news articles to predict crime trends. By analyzing temporal patterns in news, it enhances crime analysis beyond traditional spatial methods.
Area of Science:
- Criminology
- Data Science
- Computational Social Science
Background:
- Traditional crime analysis relies heavily on spatial covariates and historical crime data.
- The role of temporal covariates in understanding and predicting crime dynamics remains underexplored.
- Existing methods often overlook the rich information present in unstructured text data related to crime.
Purpose of the Study:
- To explore the utility of temporal covariates derived from crime news articles for crime analysis.
- To develop and present a novel joint model integrating text-based crime topics with historical crime counts.
- To assess the effectiveness of this new approach for predicting violent crime in London.
Main Methods:
- Collected and analyzed time-stamped crime-related news articles.
- Inferred latent crime topics from news article data.
- Developed a joint statistical model to associate inferred topics with historical crime counts.
- Focused on temporal (dynamic) covariates instead of traditional spatial covariates.
Main Results:
- Demonstrated a proof-of-concept for using news-derived temporal covariates in crime analysis.
- Successfully inferred crime topics and linked them to crime counts.
- Applied the novel joint model to analyze violent crime data in London.
Conclusions:
- Temporal covariates, particularly those inferred from news articles, offer valuable insights for crime analysis.
- The developed joint model provides a new framework for integrating diverse data sources in criminology.
- This approach has the potential to improve crime prediction and prevention strategies by capturing dynamic temporal factors.
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