| Literature DB >> 35813090 |
Eric Halford1, Anthony Dixon2, Graham Farrell2.
Abstract
Anti-social behaviour recorded by police more than doubled early in the coronavirus pandemic in England and Wales. This was a stark contrast to the steep falls in most types of recorded crime. Why was ASB so different? Was it changes in 'traditional' ASB such as noisy neighbours, or was it ASB records of breaches of COVID-19 regulations? Further, why did police-recorded ASB find much larger early-pandemic increases than the Telephone Crime Survey for England and Wales? This study uses two approaches to address the issues. The first is a survey of police forces, via Freedom of Information requests, to determine whether COVID-regulation breaches were recorded as ASB. The second is natural language processing (NLP) used to interrogate the text details of police ASB records. We find police recording practice varied greatly between areas. We conclude that the early-pandemic increases in recorded ASB were primarily due to breaches of COVID regulations but around half of these also involved traditional forms of ASB. We also suggest that the study offers proof of concept that NLP may have significant general potential to exploit untapped police text records in ways that inform policing and crime policy.Entities:
Keywords: Anti-social behaviour; Antisocial behavior; Artificial intelligence; COVID-19; Natural language processing; Policing
Year: 2022 PMID: 35813090 PMCID: PMC9251022 DOI: 10.1186/s40163-022-00168-x
Source DB: PubMed Journal: Crime Sci ISSN: 2193-7680
Fig. 1Monthly police-recorded ASB in England and Wales
Fig. 2Percent difference between observed and expected recorded ASB (shaded = 95% confidence intervals
Questions included in FOI request
| Number | Question |
|---|---|
| 1 | Generally, during the pandemic how have you recorded within the control room incoming reports relating to COVID-19 breaches? |
| 2 | How did this recording practice manifest, either directly or indirectly, to data.police.uk reporting? |
| 3 | Did your force record reports of COVID-19 rule infringements as ASB? |
| 4 | Did your Force use an existing category for COVID-19 rule infringement other than ASB? |
| 5 | Did your force record reports of COVID-19 rule infringements as another category that is reported to data.police.uk? |
| 6 | If you did use an alternate incident class such as ASB, did you apply any ‘tagging’ system to capture the COVID infringements within recorded incidents? |
| 7 | If you did use a tag what tag labels did you use? |
| 8 | Did your force create a new category for recording reports of COVID-19 infringements? |
| 9 | If so is this new category reportable to data.police.uk? |
Metrics for final NLP models
| Metric | ‘Specific COVID Complaint’ model | ‘Traditional ASB’ model |
|---|---|---|
| Accuracy | 90% | 92% |
| F1 | 0.80 | 0.96 |
| Matthews correlation coefficient | 0.74 | 0.68 |
F1 and Matthews correlation coefficients used because they are better measure when the datasets are imbalanced between classes
Fig. 3Change in ASB by police force and lockdown
Fig. 4NLP categorisation of ASB
Model confusion matrices generated from test data with final models
| Labelled as traditional ASB | Not labelled as traditional ASB | Labelled as breach of COVID regulation | Not labelled as breach | ||
|---|---|---|---|---|---|
| Traditional ASB | 166 | 7 | Breach of COVID regulation | 36 | 6 |
| Not traditional ASB | 7 | 18 | Not breach | 12 | 144 |