Multi-layer data integration technique for combining heterogeneous crime data. Issue 3 (May 2022)
- Record Type:
- Journal Article
- Title:
- Multi-layer data integration technique for combining heterogeneous crime data. Issue 3 (May 2022)
- Main Title:
- Multi-layer data integration technique for combining heterogeneous crime data
- Authors:
- Ahmed, Sadaf
Gentili, Monica
Sierra-Sosa, Daniel
Elmaghraby, Adel S. - Abstract:
- Abstract: Analysis of publicly available human and drug trafficking crime data faces the challenge of finding a comprehensive dataset that includes a sufficiently large number of crime incidents. Our proposed methodology attempts to address this challenge by using entity resolution techniques to merge multiple state-wide crime datasets and a county-wide incident report dataset to get a clearer picture of a category of criminal activity in a geographical area. This methodology combines incident reports, crime reports, and court records to close any gaps that may be present in a single data source. We apply this methodology to create a dataset that includes drug and human trafficking related crimes and incidents from three distinct sources (from Louisville Open Data Crime Reports, Federal Bureau of Investigation Kentucky Crime Incidents, and the Kentucky Online Offender Lookup website) to provide researchers data to study the link between drug and human trafficking related crimes. In a case study performed with the new merged dataset, an XGBoost classifier was able to label a 7-day sliding time window, within any given county, as containing a human trafficking related incident or not with a Matthews correlation coefficient of 0.86. Highlights: Novel multi-layer data integration technique for combining crime datasets. Robust system addresses data fragmentation issues prevalent in human trafficking data. New dataset of crimes related to human and drug trafficking for furtherAbstract: Analysis of publicly available human and drug trafficking crime data faces the challenge of finding a comprehensive dataset that includes a sufficiently large number of crime incidents. Our proposed methodology attempts to address this challenge by using entity resolution techniques to merge multiple state-wide crime datasets and a county-wide incident report dataset to get a clearer picture of a category of criminal activity in a geographical area. This methodology combines incident reports, crime reports, and court records to close any gaps that may be present in a single data source. We apply this methodology to create a dataset that includes drug and human trafficking related crimes and incidents from three distinct sources (from Louisville Open Data Crime Reports, Federal Bureau of Investigation Kentucky Crime Incidents, and the Kentucky Online Offender Lookup website) to provide researchers data to study the link between drug and human trafficking related crimes. In a case study performed with the new merged dataset, an XGBoost classifier was able to label a 7-day sliding time window, within any given county, as containing a human trafficking related incident or not with a Matthews correlation coefficient of 0.86. Highlights: Novel multi-layer data integration technique for combining crime datasets. Robust system addresses data fragmentation issues prevalent in human trafficking data. New dataset of crimes related to human and drug trafficking for further research. Case study showing spatio-temporal link between drug and human trafficking crimes. … (more)
- Is Part Of:
- Information processing & management. Volume 59:Issue 3(2022)
- Journal:
- Information processing & management
- Issue:
- Volume 59:Issue 3(2022)
- Issue Display:
- Volume 59, Issue 3 (2022)
- Year:
- 2022
- Volume:
- 59
- Issue:
- 3
- Issue Sort Value:
- 2022-0059-0003-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-05
- Subjects:
- Entity resolution -- Data integration -- Trafficking crimes -- XGBoost -- Binary classification -- Natural language processing
Information storage and retrieval systems -- Periodicals
Information science -- Periodicals
Systèmes d'information -- Périodiques
Sciences de l'information -- Périodiques
Information science
Information storage and retrieval systems
Periodicals
658.4038 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064573 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ipm.2022.102879 ↗
- Languages:
- English
- ISSNs:
- 0306-4573
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4493.893000
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British Library HMNTS - ELD Digital store - Ingest File:
- 21548.xml