TY - JOUR
AU - Muhammad Faisal Buland Iqbal
AU - Aman Ullah
AU - Ahmed Alhomoud
AU - Tariq Hussain
AU - Razaz Waheeb Attar
AU - Jianquan Ouyang
AU - Mrim M. Alnfiai
AU - Wesam Atef Hatamleh
AB - This research introduces a method for predicting where crimes will occur based on clustering activity in the  area. Hotspots, or locations with a disproportionately high number of crimes, are located by a combination  of spatial and temporal grouping methods employed by this strategy. Crime forecasting models use these  hotspots to predict where crimes will occur. The approach's efficacy is tested using actual crime data, and  it successfully predicts future crimes in high-crime zones. Law enforcement agencies can use the proposed  method to protect the public better, and it shows promise as a tool for crime prediction. Academic research  into the topic of foreseeing criminal behavior is a newer development. Researchers in this discipline have  discovered that criminal behavior has universal patterns. These patterns may help law enforcement agencies  plan for criminal activities. Predictive policing, hotspot analysis, and geographical profiling are examples of  when crime forecasting has been useful. Several aspects of the census, such as the average yearly income and  literacy rate, are related to the prevalence of crime in a certain area. Indicators of potentially criminal behavior,  these characteristics may be seen as markers. This investigation aims to discover if any clues can be gleaned  from past criminal behavior that may be utilized to forecast future criminal behavior. Using machine learning  and 2-D Hotspot analysis, we propose a method for the spatiotemporal prediction of criminal activity within  the scope of this study. Clustering is a method used in 2-dimensional hotspot analysis. Methods of modern  categorization, both with and without hotspot analysis, are used to evaluate the suggested model's efficacy. It  is found that the model that incorporates hotspot analysis performs better than the one that does not. 
IS - In press
M1 - In press
N2 - This research introduces a method for predicting where crimes will occur based on clustering activity in the  area. Hotspots, or locations with a disproportionately high number of crimes, are located by a combination  of spatial and temporal grouping methods employed by this strategy. Crime forecasting models use these  hotspots to predict where crimes will occur. The approach's efficacy is tested using actual crime data, and  it successfully predicts future crimes in high-crime zones. Law enforcement agencies can use the proposed  method to protect the public better, and it shows promise as a tool for crime prediction. Academic research  into the topic of foreseeing criminal behavior is a newer development. Researchers in this discipline have  discovered that criminal behavior has universal patterns. These patterns may help law enforcement agencies  plan for criminal activities. Predictive policing, hotspot analysis, and geographical profiling are examples of  when crime forecasting has been useful. Several aspects of the census, such as the average yearly income and  literacy rate, are related to the prevalence of crime in a certain area. Indicators of potentially criminal behavior,  these characteristics may be seen as markers. This investigation aims to discover if any clues can be gleaned  from past criminal behavior that may be utilized to forecast future criminal behavior. Using machine learning  and 2-D Hotspot analysis, we propose a method for the spatiotemporal prediction of criminal activity within  the scope of this study. Clustering is a method used in 2-dimensional hotspot analysis. Methods of modern  categorization, both with and without hotspot analysis, are used to evaluate the suggested model's efficacy. It  is found that the model that incorporates hotspot analysis performs better than the one that does not. 
PY - 9998
SE - 1
SP - 1
EP - 11
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A 2D Clustering Based Hotspot Identification  Approach for Spatio-Temporal Crime Prediction
UR - https://www.ijimai.org/journal/bibcite/reference/3517
VL - In press
SN - 1989-1660
ER -