Introduction. The police and the investigation team has been manually doing behavioural analysis and connecting different crimes to an offender. With the help of computers technologies, databases, and automated system, the statistical analysis of the offender’s behaviour significantly improved. There we can transfer from a manual process to an automated one, and the investigator can allocate time and resources better by prioritising the offenders to investigate. In this study, we create and experiment with a proof of concept system that ranks and prioritise different offenders using the Random Choice method in combination with the state of the art Spatial-Temporal method. Objectives. In experimenting with the proof of concept system, we are aiming to understand the effect of different offender’s behaviour having on the offenders ranking and the effect of having multiple different numbers of reference crimes in the database. The objective is also to understand the role of consistency and distinctiveness in offenders ranking. Moreover, understanding the performances of our proof of concept system comparing to already existing methods such as Random Choice, Spatial-Temporal and a baseline method that based on pure randomness. Method. The method we chose for this study was an experimental study. With an experimental environment with independent and dependent variables, we presented and evaluated the system. We were using the experimenting approach because it has a stable presence and widely used in similar studies in this field. Results. After the experiments, we found that different Modus Operandi (MO)categories have a different effect on the ranking results and different distinctive combinations of MO categories also has different accuracy when ranking the offenders. Offenders that were consistent with more references crime in the database were often higher ranked and were linked more correctly. Our proof of concept system shows significant improvement over Random Choice method and the Spatial-Temporalmethod. Conclusion. From the results, we concluded that the proof of concept system displays a significant accuracy in ranking and prioritising offenders, there different MO categories and combinations of them has a different effect on the accuracy of the ranking. The ranking system was also affected by the number of reference cases that exist in the database. Future works can extend the study by trying to improve different aspects of the proof of concept systems, such as the Random Choice aspect or the Spatial-Temporal aspect.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-19689 |
| Date | January 2020 |
| Creators | Tran, Bao Khang |
| Publisher | Blekinge Tekniska Högskola, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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