A major challenge facing all law-enforcement organizations is to accurately and efficiently analyze the growing volumes of crime data in order to extract useful knowledge for decision makers. This is an increasingly important task, considering the fast growth of urban populations in most countries. In particular, to reconcile urban growth with the need for security, a fundamental goal is to optimize the allocation of law enforcement resources. Moreover, optimal allocation can only be achieved if we can predict the incidence of crime within different urban areas. To answer this call, in this paper we propose an artificial intelligence system for predicting per capita violent crimes in urban areas starting from socio-economic data, law-enforcement data and other crime-related data obtained from different sources. The proposed framework blends a recently developed version of genetic programming that uses the concept of semantics during the search process with a local search method. To analyze the appropriateness of the proposed computational method for crime prediction, different urban areas of the United States have been considered. Experimental results confirm the suitability of the proposed method for addressing the problem at hand. In particular, the proposed method produces a lower error with respect to the existing state-of-the art techniques and it is particularly suitable for analyzing large amounts of data. This is an extremely important feature in a world that is currently moving towards the development of smart cities.
|Number of pages||8|
|Journal||Journal of Ambient Intelligence and Humanized Computing|
|Publication status||Published - 1 Feb 2017|
- Crime Prediction
- Evolutionary Computation
- Local Search
- Urban Security