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Recreation of 3D crime scenes is critical for law enforcement in the investigation of serious crimes for criminal justice responses. This work presents a premier systematic literature review (SLR) that offers a structured, methodical, and rigorous approach to understanding the trend of research in 3D crime scene reconstruction as well as tools, technologies, methods, and techniques employed thereof in the last 17 years. Major credible scholarly database sources, Scopus, and Google Scholar, which index journals and conferences that are promoted by entities such as IEEE, ACM, Elsevier, and SpringerLink were explored as data sources. Of the initial 17, 912 papers that resulted from the first search string, 258 were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. To summarize the existing efforts, we compared and analysed various classical 3D reconstruction approaches. This study presents the first comprehensive review of key milestones in the development of methods for 3D crime scene reconstruction, gaps for improvement and where immersive technology has been used to enhance crime scene findings. This study found that the implementation of light detection and ranging (LiDAR) scanners and immersive technologies, alongside traditional methods, has been beneficial in the recreation of crime scenes. The SLR is limited to existing applications with peer-reviewed papers published between 2005 and 2021. Results based on the analysed published data indicated that 20.2% of the articles implemented immersive technologies in crime scene reconstruction, of which Augmented Reality (AR) accounted for 15.3%, Virtual Reality (VR) accounted for 75%, Mixed reality (MR) accounted for 5.9% and VR and AR mixture accounted for 3.8%. Finally, we summarize the development trend of design and key technology prospects of crime scene recreation using immersive technology and provide insights into potential future research. To the best of the researchers' knowledge, this is the first survey that accomplishes such goals.

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Identifying related offences in a criminal investigation is an important goal for crime analysts. This can deliver evidence that can assist in apprehension of suspects and better attribution of past crimes. The use of pattern based approaches has the potential to assist crime experts in discovering new patterns of criminal activity. Hence, research in this area continues. This paper revisits frequent pattern growth models for crime pattern mining. Frequent pattern (FP) based approaches, such as the FP-Growth model, have been identified to be more effective than techniques proposed in the past, such as Apriori. Therefore, this research proposes a descriptive statistical approach, based on a quartile (floor-ceil) function, for the minimum support threshold (MST) choice selection, which is a major decision step in the pruning phase of the Traditional FP-Growth (TFPG) model. Our revised frequent pattern growth (RFPG) model further proposes a Pattern-pattern (P p ) paradigm to identify tuples of subtle crime pattern(s) sequences or recurring trends in criminal activity. We present empirical results in order to guide intended audience about future decisions or research regarding this model. Results indicate that RFPG is more promising than TFPG and will always ensure the utilisation of a reasonable percentage of the crime dataset, in order to produce more reliable and sufficiently informative patterns or trends. c 2015 Isafiade et al. Published by Elsevier B.V. Selection and/or peer-review under responsibility of ITQM2015.