| Title | How Data Analysis Supports Crime Prediction in Smart Cities |
|---|---|
| ID_Doc | 41247 |
| Authors | Altomare, A; Catlett, C; Cesario, E; Talia, D |
| Title | How Data Analysis Supports Crime Prediction in Smart Cities |
| Year | 2017 |
| Published | |
| Abstract | Today, about 55 per cent of the world's population lives in urban areas, a proportion that is expected to increase to 66 per cent by 2050. Such a steadily increasing urbanization is already bringing huge social, economic and environmental transformations and, at the same time, poses big challenges in city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. To face such challenges, the exploitation of information coming from urban environments and the development of Smart City applications to enhance quality, improve performance and safety of urban services, are key elements. This chapter discusses how the analysis of urban data may be exploited for forecasting crimes and presents an approach, based on seasonal auto-regressive models, for reliably forecasting crime trends in urban areas. In particular, the main goal of this work is to discuss the impact of data mining on urban crime analysis and design a predictive model to forecast the number of crimes that will happen in rolling time horizons. As a case study, we present the analysis performed on an area of Chicago. Experimental evaluation results show that the proposed methodology can achieve high predictive accuracy for long term crime forecasting, thus can be successfully exploited to predict the time evolution of the number of crimes in urban environments. |
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