A Real-time location based algorithm for notification of crime hot-spots using crowd sourcing
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Security of the people has always been the number one objective of many governments in the world today. Governments endeavour to achieve this objective has faced several challenges ranging from economic, social and political. Despite heavy investments by local and National Government in Kenya on security measures, crime continues to remain a serious problem in the society, as a result, there are loss of lives, loss of property and investors shying away. Gathering relevant and up to date operational information on crime intelligence across several sources has always been one of the challenging issues faced by national security practitioners and citizens. This therefore makes it difficult to identify crime hotspot areas in timely manner, and also improper allocation of Police resources in the right hotspot areas. The data collection exercise was done earnestly to ensure that there was ample understanding of the participants’ interaction with crowdsourcing platforms and their experience and willingness to use a crowd-based crime hotspot reporting network. The study thus found significant justification for the design of the criminal hotspot system to leverage data about crime incidents in the city in order to classify crime hotspots. The design of the system was made using unified modelling language and detailed in the fifth chapter of the thesis. The developed prototype was then tested against parameters to gauge its efficiency and effectiveness. The conclusions of the testing as well as the recommendations of the study are documented in the sixth and last chapter of the study respectively.