Innovative DSS for intelligent monitoring and urban square design approaches: A case of study

In this paper we proposed a new platform based on data processing suitable for urban square design optimization. The re-design of square in cities is important to make the urban squares livable especially for cities with high population density. Innovative solutions about green space design, computer vision and environmental sensing have been implemented, observing experimentally the influence of the public space on people’s behaviour. Furthermore, the platform processes data about weather condition, objects detected by image processing algorithms, environmental pollution and occupancy patterns to perform correlation analysis. A Convolutional Neural Network (CNN) has been used to predict people occupancy over time, basing on a weather dataset. The platform implements a Decision Support System (DSS) aimed to read the data, and provide guidelines for urban square design optimization. The DSS is constructed by data modeling, data mining and multiple correspondence analysis (MCA) tools tailored on the specific case of study. Experimental observations provide insights for a self-adaptive urban square design. Multiple correspondence analysis best fits qualitative and quantitative data obtained from sensors, and highlights people’s behaviour in the urban square in correlation with different variables. After a complete overview of factors which can influence the livability of a square and economical/social models are presented different results about citizen behaviour interpretation in function of weather data and attitudes. The proposed framework can be potentially applied for a generic urban square.