Crime Mapping Model based on Cloud and Spatial Data: A Case Study of Zambia Police Service

Crime mapping is a strategy used to detect and prevent crime in the police service. The technique involves the use of geographical maps to help crime analysts identify and profile crimes committed in different residential areas, as well as determining best methods of responding. The development of geographic information system (GIS) technologies and spatial analysis applications coupled with cloud computing have significantly improved the ability of crime analysts to perform this crime mapping function. The aim of this research is to automate the processes involved in crime mapping using spatial data. A baseline study was conducted to identify the challenges in the current crime mapping system used by the Zambia Police Service. The results show that 85.2% of the stations conduct crime mapping using physical geographical maps and pins placed on the map while 14.8% indicated that they don’t use any form of crime mapping technique. In addition, the study revealed that all stations that participated in the study collect and process the crime reports and statistics manually and keep the results in books and papers. The results of the baseline study were used to develop the business processes and a crime mapping model, this was implemented successfully. The proposed model includes a spatial data visualization of crime data based on Google map. The proposed model is based on the Cloud Architecture, Android Mobile Application, Web Application, Google Map API and Java programming language. A prototype was successfully developed and the test results of the proposed system show improved data visualization and reporting of crime data with reduced dependency on manual transactions. It also proved to be more effective than the current system.

@article{Phiri2020,
  title     = {Crime Mapping Model based on Cloud and Spatial Data: A Case Study of Zambia Police Service},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {1},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Jonathan Phiri and Jackson Phiri and Charles S. Lubobya},
  doi       = {10.14569/IJACSA.2020.0110132},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110132}
}