Obtaining insights about the dynamics of urban structure is crucial to the framing of the context within the smart city. This paper focuses on urban areas of interest (UAOI), a concept that provides functional definitions of a city’s spatial structure. Traditional sources of social data can rarely capture these aspects at scale while spatial information on the city alone does not capture how the population values different parts of the city and in different ways. Hence, we leverage volunteered geographic information (VGI) to overcome some of the limits of traditional sources in providing urban structural and functional insights. We use a special type of VGI—metadata from geotagged Flickr images—to identify UAOIs and exploit their temporal and spatial attributes. To do this, we propose a methodological strategy that combines hierarchical density-based spatial clustering for applications with noise and the ‘α-shape’ algorithm to quantify the dynamics of UAOIs in Inner London for a period 2013–2015 and develop an innovative visualisation of UAOI profiles from which UAOI dynamics can be explored. Our results expand and improve upon the previous literature on this topic and provide a useful reference for urban practitioners who might wish to include more timely information when making decisions.

中文翻译：

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通过自愿的地理信息了解感兴趣的城市区域的动态

获得有关城市结构动态的见解对于在智慧城市中构建环境至关重要。本文关注的是感兴趣的城市区域（UAOI），该概念提供了城市空间结构的功能定义。传统的社会数据源很少能大规模地捕捉到这些方面，而仅城市的空间信息无法捕捉到人口如何评价城市的不同部分以及如何以不同的方式。因此，我们利用志愿地理信息（VGI）来克服传统资源在提供城市结构和功能洞察方面的某些限制。我们使用一种特殊类型的VGI（来自带有地理标签的Flickr图像的元数据）来识别UAOI，并利用其时空属性。去做这个，我们提出了一种方法论策略，将基于层次密度的空间聚类与噪声和“α形”算法相结合，以量化2013-2015年内伦敦UAOI的动态，并开发出一种新颖的UAOI轮廓可视化方法，可以探索UAOI动力学。我们的研究结果在以前有关该主题的文献的基础上进行了扩充和改进，为可能希望在决策时包括更多及时信息的城市从业者提供了有用的参考。