All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi
The evidence that burglaries cluster spatio-temporally is strong. However, research is unclear on whether clustered burglaries (repeats/near-repeats) should be treated as qualitatively different crimes compared to spatio-temporally unrelated burglaries (non-repeats). This study, therefore, investiga...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/3/160 |
| _version_ | 1827648663489347584 |
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| author | Anton Borg Martin Svensson |
| author_facet | Anton Borg Martin Svensson |
| author_sort | Anton Borg |
| collection | DOAJ |
| description | The evidence that burglaries cluster spatio-temporally is strong. However, research is unclear on whether clustered burglaries (repeats/near-repeats) should be treated as qualitatively different crimes compared to spatio-temporally unrelated burglaries (non-repeats). This study, therefore, investigated if there were differences in modus operandi-signatures (MOs, the habits and methods employed by criminals) between near-repeat and non-repeat burglaries across 10 Swedish cities, as well as whether MO-signatures can aid in predicting if a burglary is classified as a near-repeat or a non-repeat crime. Data consisted of 5744 residential burglaries, with 137 MO features characterizing each case. Descriptive data of repeats/non-repeats is provided together with Wilcoxon tests of MO-differences between crime pairs, while logistic regressions were used to train models to predict if a crime scene was classified as a near-repeat or a non-repeat crime. Near-repeat crimes were rather stylized, showing heterogeneity in MOs across cities, but showing homogeneity within cities at the same time, as there were significant differences between near-repeat and non-repeat burglaries, including subgroups of features, such as differences in mode of entering, target selection, types of goods stolen, as well the traces that were left at the crime scene. Furthermore, using logistic regression models, it was possible to predict near-repeat and non-repeat crimes with a mean F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>1</mn></msub></semantics></math></inline-formula>-score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.8155</mn></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.0866</mn></mrow></semantics></math></inline-formula>) based on the MO. Potential policy implications are discussed in terms of how data-driven procedures can facilitate analysis of spatio-temporal phenomena based on the MO-signatures of offenders, as well as how law enforcement agencies can provide differentiated advice and response when there is suspicion that a crime is part of a series as opposed to an isolated event. |
| first_indexed | 2024-03-09T19:44:17Z |
| format | Article |
| id | doaj.art-47d25ca21e164179835d7215f6916fef |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T19:44:17Z |
| publishDate | 2022-02-01 |
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| spelling | doaj.art-47d25ca21e164179835d7215f6916fef2023-11-24T01:28:07ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-02-0111316010.3390/ijgi11030160All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus OperandiAnton Borg0Martin Svensson1Blekinge Institute of Technology, 371 79 Karlskrona, SwedenBlekinge Institute of Technology, 371 79 Karlskrona, SwedenThe evidence that burglaries cluster spatio-temporally is strong. However, research is unclear on whether clustered burglaries (repeats/near-repeats) should be treated as qualitatively different crimes compared to spatio-temporally unrelated burglaries (non-repeats). This study, therefore, investigated if there were differences in modus operandi-signatures (MOs, the habits and methods employed by criminals) between near-repeat and non-repeat burglaries across 10 Swedish cities, as well as whether MO-signatures can aid in predicting if a burglary is classified as a near-repeat or a non-repeat crime. Data consisted of 5744 residential burglaries, with 137 MO features characterizing each case. Descriptive data of repeats/non-repeats is provided together with Wilcoxon tests of MO-differences between crime pairs, while logistic regressions were used to train models to predict if a crime scene was classified as a near-repeat or a non-repeat crime. Near-repeat crimes were rather stylized, showing heterogeneity in MOs across cities, but showing homogeneity within cities at the same time, as there were significant differences between near-repeat and non-repeat burglaries, including subgroups of features, such as differences in mode of entering, target selection, types of goods stolen, as well the traces that were left at the crime scene. Furthermore, using logistic regression models, it was possible to predict near-repeat and non-repeat crimes with a mean F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>1</mn></msub></semantics></math></inline-formula>-score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.8155</mn></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.0866</mn></mrow></semantics></math></inline-formula>) based on the MO. Potential policy implications are discussed in terms of how data-driven procedures can facilitate analysis of spatio-temporal phenomena based on the MO-signatures of offenders, as well as how law enforcement agencies can provide differentiated advice and response when there is suspicion that a crime is part of a series as opposed to an isolated event.https://www.mdpi.com/2220-9964/11/3/160residential burglariesrepeat and near-repeat victimizationcrime predictiongeographic crime analysis |
| spellingShingle | Anton Borg Martin Svensson All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi ISPRS International Journal of Geo-Information residential burglaries repeat and near-repeat victimization crime prediction geographic crime analysis |
| title | All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
| title_full | All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
| title_fullStr | All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
| title_full_unstemmed | All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
| title_short | All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
| title_sort | all burglaries are not the same predicting near repeat burglaries in cities using modus operandi |
| topic | residential burglaries repeat and near-repeat victimization crime prediction geographic crime analysis |
| url | https://www.mdpi.com/2220-9964/11/3/160 |
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