Table 1. Related work summary comparison.

Author	Year	Country	Dataset	Features	Method	Accuracy	Advantage
Chandrasekar et al.	2015	USA	General Crime in San Francisco	Date, Day of the week, Name of the Police Department District, Address, Latitude, Longitude, Category, Description, Resolution	RF, SVM, Gboost	Md1 SVM 96% Md1 Gboosted 75.02%	Including time series modeling to understand temporal correlations of blue/white collar and Violent/Nonviolent crime classification data and predicting fluctuations in different crime categories
Feng et al.	2018	USA	General Crime in San Francisco, Chicago, Philadelphia	Date, Category, Descript, Dayof Week, P. D. District, Resolution, Address, x, y, Dome, Arrest	NB, KNN, RF, XGBoost	XGboost 70%	Predicting overall crime in the coming years using time series
Kim et al.	2018	Canada	General Crime in Vancouver	Crime Type, Month, Day, Hour, Weekday, Address, Neighborhood, x, y	KNN, XGBoost	Md1 Gboost 41.9% Md2 Gboost 43.2%	Predicting crime location
Bharati et al.	2018	USA	General Crime in Chicago	Block, Location, District, Community Area, Latitude, X, Y, Longitude, Hour, Month,	KNN, LR, DT, RF, SVM, NB	KNN 78.9%	Detecting, predicting, and solving crimes much faster, thereby lowering the crime rate
Alves et al.	2018	Brazil	Murder Crimes	Child labor, Elderly -Female- Male Population, GDP, Homicides, Income, Literacy, Sanitation, Suicides, Traffic Accidents, Unemployment	RF	RF 97%	The prediction revealed that unemployment and ignorance were the most important variables in defining murders in Brazilian cities, and the order of importance of urban indicators in predicting crimes was determined.
Verma et al.	2019	India	Terrorist Incidents	Incident Location, Time, Type, Weapon, Intensity of Attack, Capital, Perpetrator	DT, RF, IG	RF 84%	Predicting the occurrence of terrorist incidents on demographic and geographic data
Jha et al.	2019	India	Murder Crimes on the Internet	Crime Type, Year, Address, Place, Territory	NB	NB 51.2%	Analyzing crimes in minimum time and predicting the location and type of future crimes
Arora et al.	2019	Public	Cybercrime Data Publicly Available on Social Media	Synonyms, Age, Location, Gender, Hashtags, and Sarcasm	RF	RF 80%	Detecting cyber threats automatically
Ghankutkar et al.	2019	Public	Real-time Crime Data from HuffPost News Site	Category, Headline, Authors, Link, Short Description, Date	SVM, MNB, RF	RF 85.83%	Providing analysis to current news after being classified as crime and non-crime data
Kumar et al.	2020	India	Murder, Kidnapping, Violence, Robbery, Gambling, Accident, Indore, Crimes	Hour, Longitude, Latitude, Day of the Week, Week, Month	KNN	Prev 93.23% Proposed 99.51%	Predicting the type of crime, and where and when it may occur
Ch et al.	2020	India	Cybercrime	Incident, Harm, Access, Year, Violation, Victim, Offender	SVML, LR MNB, RF	LR 99.3%	Finding and analyzing cyber-attacks that exploit vulnerabilities.