As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

This research has been conducted to find answers to the questions of how projects that support local businesses work in areas undergoing gentrification and what are the achievements and limitations of these projects, based on the case studies of Brixton Pound and Pop Brixton supporting local businesses in Brixton in the London borough of Lambeth. To this end, this study collected data through interviews with local businesses belonging to each project and analysed them based on prior research on the projects, evaluation reports, and articles in the media. It began by investigating the supporting mechanisms of projects that support local businesses in the area, and then analysed and evaluated the projects' contribution to local businesses and the impact on the local community according to these mechanisms. The two projects in the case study showed contrasts in their support mechanisms, governance, and the role of Lambeth Council, and the local businesses' evaluations of the projects' support differed depending not only on the support mechanism of each project but also on business type and perceptions of the impact of gentrification.