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A Demographic Analysis of Urban Nature Utilization

机译:城市自然利用的人口统计学分析

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The ease of deployment of digital technologies and the Internet of Things, enables the collection of vast amounts of data from our cities that are now becoming increasingly connected and smart. This study aims to investigate the interaction of citizens with the urban green areas, with a view to improve wellbeing through targeted, financial-efficient interventions. A field experiment has been carried out in Sheffield, UK involving 1,870 participants. We collected both objective and subjective data, using a tailor-made smartphone app. Location tracking was activated as soon as people entered any of the publicly accessible green areas, to automatically determine type of activity and interaction. This was complemented by textual and photographic information that users could insert spontaneously or when prompted. We could thus establish interaction patterns and map these to demographics, using data science methods to find out the particular features that affected wellbeing, for different categories. Using text and image analysis, we found a distinctive clustering around age and gender, showing that different categories are interested in specific features. The younger age group (18-35 years) prioritized the presence of urban features in parks, whereas the older group (54-72 years) paid more attention to nature. Pinpointing the particular elements that improve human interaction with the urban green spaces, is the starting point for improving city management and planning using technology and science.
机译:数字技术和物联网的易于部署,使我们能够从我们的城市中收集大量数据,而这些城市现在正变得越来越互联和更加智能。这项研究旨在调查公民与城市绿地之间的互动,以期通过有针对性的,经济有效的干预措施来改善人们的福祉。在英国谢菲尔德进行了一项现场试验,共有1,870名参与者参加。我们使用量身定制的智能手机应用程序收集了客观和主观数据。人们进入任何可公开访问的绿色区域后,就会立即激活位置跟踪功能,以自动确定活动和互动的类型。用户可以自发或在提示时插入的文字和图片信息对此进行了补充。因此,我们可以使用数据科学方法建立交互模式并将其映射到人口统计数据,以找出影响不同类别幸福感的特定特征。使用文本和图像分析,我们发现了一个围绕年龄和性别的独特聚类,表明不同类别对特定功能感兴趣。年龄较小的年龄段(18-35岁)优先考虑公园中城市风貌的存在,而年龄较大的年龄段(54-72岁)则更注重自然。指出改善人类与城市绿色空间互动的特定元素,是使用技术和科学改善城市管理和规划的起点。

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