Comparison of Different Spatial Interpolation Techniques to Thematic Mapping of Socio-Economic Causes of Crime Against Women

The increase in the crime rate numbers and a rise in the need to find better solutions to handle information about criminality is affected by the ever-changing socio-economical order of the world. Despite the number of solutions implemented for reducing crime (against women), cities continue to have an unsafe environment. The prime drawback lies in the inability to provide a prompt response in real-time when in danger. Thus, the effective utilization of technology in public safety management is important. The present state of the art solutions focus on technological innovations with limited human intervention and are insufficient in ensuring the safety of the women as and when required. To dig deeper into the root cause of preventing a crime from occurring in a particular place, it is vital to analyze the parameters and factors contributing to the crime in a community. This research applies the Information Communication Technologies (ICT) along with harnessing big data tools to identify crime hotspots and patterns. After a comprehensive literature review, it has been noted that there are different social-economic factors affecting the crime in an area. The proposed work aims to integrate the socio-economic attributes leading to increasing crime against women. Interpolation strategies used for thematic maps generation also play a major role in predicting and studying the area affected by a crime. This research initially identifies the various social-economical parameters that affect crime against women. Some of them to mention include unemployment, illiteracy, population, sex ratio, traffic, age, no. of schools, and location of liquor shops. Subsequently, a comparison of major interpolation methods used in crime mapping: Inverse Distance Weighted (IDW), Kriging, and Spline are formulated to understand the overall contribution of socio-economic factors on the crime thematic map to further ascertain if one parameter poses substantially more important than the other. The comparison of different Interpolation techniques used in pixel by pixel error analysis on high definition satellite images of the crime site, of resolution as high as 2.5m x 2.5m, is created using visualization libraries like Matplotlib and Seaborn. Finally, the thematic maps are created using the best Interpolation technique chosen and help in predicting the pattern of the crime. The proposed framework developed using Geographic Information System (GIS) based visualization and big data tools for crime mapping can then be applied in the development of user interactive platforms and designing safety strategies to help the needy in real-time. To validate the methodology, a case study is performed with real data, in the Jhunjhunu district of Rajasthan, India.