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http://dx.doi.org/10.9717/kmms.2017.20.8.1282

A Deep Learning-based Streetscapes Safety Score Prediction Model using Environmental Context from Big Data  

Lee, Gi-In (Dept. of Media Technology Contents., The Catholic University of Korea)
Kang, Hang-Bong (Dept. of Media Technology Contents., The Catholic University of Korea)
Publication Information
Journal of Korea Multimedia Society / v.20, no.8, 2017 , pp. 1282-1290 More about this Journal
Abstract
Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.
Keywords
Urban Safety; Crime Prediction; Stacked Autoencoder; Context;
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