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Abstract
Background
Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects.

Methods
We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics.

Results
The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon.

Limitations
Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems.

Conclusions
There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction.

Implications
Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

Predictive policing is the use of analytical techniques to identify targets for police intervention with the goal of preventing crime, solving past crimes, or identifying potential offenders and victims. These tools are not a substitute for integrated approaches to policing, nor are they a crystal ball. This guide assesses some of the most promising technical tools and tactical approaches for acting on predictions in an effective way.

Hot-spot maps regularly aid many policing resource allocation decisions in today's data-driven age. However, it is unclear what forecasting algorithm(s) should be used to create these maps. To address this gap, we must be able to assess how "good" a generated hot-spot map is. Currently, four main metrics are used for evaluation: the prediction accuracy index (PAI), the recapture rate index (RRI), the prediction efficiency index (PEI), and the prediction efficiency index* (PEI*). This article discusses PAI, RRI, and PEI's strengths and weaknesses, articulates and justifies PEI*, and demonstrates the differences in calculations and interpretations of each metric. We argue that PEI* measures the efficiency of a crime forecasting algorithm while being operationally realistic and should be used in conjunction with other appropriate measures.

Based on the study of conceptual provisions, the most substantiated, from the author's point of view, approach to formulating the concept of the doctrine of forensic prognostication and its relationship with forensic forecasting is identified and presented. It is noted that these concepts are correlated as "teaching – teaching method", and the forecasting method, as the main one in forensic prognosis, combines a set of other methods used to form a forecast. The range of objects, subjects, types and methods of forensic forecasting were considered. New approaches to the practice of forensic forecasting were studied.

"As the metaverse rapidly evolves, a comprehensive examination of the emerging threats and challenges is imperative. In the groundbreaking exploration within Forecasting Cyber Crimes in the Age of the Metaverse, the intersection of technology, crime, and law enforcement is investigated, and it provides valuable insights into the potential risks and strategies for combating cybercrimes in the metaverse.Drawing upon research and scientific methodologies, this book employs a forward-thinking approach to anticipate the types of crimes that may arise in the metaverse. It addresses various aspects of cybercrime, including crimes against children, financial fraud, ransomware attacks, and attacks on critical infrastructure. The analysis extends to the protection of intellectual property rights and the criminal methods employed against metaverse assets. By forecasting the future of cybercrimes and cyber warfare in the metaverse, this book equips law enforcement agencies, policymakers, and companies with essential knowledge to develop effective strategies and countermeasures. It explores the potential impact of cybercrime on police capabilities and provides valuable insights into the planning and preparedness required to mitigate these threats."--