作者
Enrico Ferrara, Antonio Liotta, Laura Erhan, Maryleen Ndubuaku, Daniele Giusto, Miles Richardson, David Sheffield, Kirsten McEwan
发表日期
2018/8/12
研讨会论文
2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)
页码范围
836-841
出版商
IEEE
简介
The capabilities offered by smart sensing (the Internet of Things) and data science, create new opportunities to carry out large-scale studies involving social science and human factors. We report here our findings on a pilot study aimed at better understanding how citizens interact with urban green areas, identify relevant features, spot interaction patterns and, ultimately, recommend interventions to increase well-being. Our study was carried out in Sheffield (UK), where we tracked 1,870 subjects for two different periods (7 and 30 days), covering 760 digitally geo-fenced green areas. Through a smartphone App, we collected both subjective data (personal feelings, type of social interactions, type of activity, and perception of space) and objective data (sensor data, location, time, and photos). We employed data science methods to filter, correlate, cluster, and visualize the data, doing text analysis to extract semantic …
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E Ferrara, A Liotta, L Erhan, M Ndubuaku, D Giusto… - 2018 IEEE 16th Intl Conf on Dependable, Autonomic …, 2018