  This survey paper presents a comprehensive analysis of crime prediction
methodologies, exploring the various techniques and technologies utilized in
this area. The paper covers the statistical methods, machine learning
algorithms, and deep learning techniques employed to analyze crime data, while
also examining their effectiveness and limitations. We propose a methodological
taxonomy that classifies crime prediction algorithms into specific techniques.
This taxonomy is structured into four tiers, including methodology category,
methodology sub-category, methodology techniques, and methodology
sub-techniques. Empirical and experimental evaluations are provided to rank the
different techniques. The empirical evaluation assesses the crime prediction
techniques based on four criteria, while the experimental evaluation ranks the
algorithms that employ the same sub-technique, the different sub-techniques
that employ the same technique, the different techniques that employ the same
methodology sub-category, the different methodology sub-categories within the
same category, and the different methodology categories. The combination of
methodological taxonomy, empirical evaluations, and experimental comparisons
allows for a nuanced and comprehensive understanding of crime prediction
algorithms, aiding researchers in making informed decisions. Finally, the paper
provides a glimpse into the future of crime prediction techniques, highlighting
potential advancements and opportunities for further research in this field
