Statistical Approach of Identifying Crime Hotspots for GIS Mapping in Sri Lanka

Abstract

Currently, crimes are well organized and difficult to investigate, and hence, the situation is more complex due to technological advancements. Therefore, there is a timely need to plan strategies to reduce crime using historical crime data. The objectives of the study are extraction of crime patterns using trend analysis, identifying the relationship between crime and geographic environment, and recognizing the district where crime is most prevalent. Five types of crimes that have a high impact on society from 2010 to 2019 in Sri Lanka were identified using the high mean cluster. There were House Breaking (HB), Hurt by Knife (HK), Robbery (RB), Rape (RP), and Cheating (CH). The result of Shapiro-Wilk test for normality wasn’t normal therefore Kruskal-Wallis multiple comparisons were used. There was a significant difference between the mean of HB, HK, RB, RP, and CH. Using Spearman’s rank correlation coefficient test, a very strong positive correlation was found between RP and HB (0.9472), RB and HB (0.9003) and HK and HB (0.9277). The type of crimes scattered throughout Sri Lanka was analyzed using Geographic Information System (GIS). Thematic maps were generated to identify hotspot areas. The risk of all five types of crime appeared to be high in Kurunegala and Gampaha districts. Prediction Accuracy Index (PAI) values were calculated to compare the predictive accuracy of crime types and the highest PAI value (3.98) was for RB crime. According to the findings of this study, a new security strategy can be developed to eradicate these trends from society.