Understanding the dynamics of urban areas of interest through volunteered geographic information

Journal of Geographical Systems https://doi.org/10.1007/s10109-018-0284-3 ORIGINAL ARTICLE Understanding the dynamics of urban areas of interest through volunteered geographic information Meixu Chen1 · Dani Arribas‑Bel1 · Alex Singleton1 Received: 10 October 2017 / Accepted: 14 November 2018 © The Author(s) 2018 Abstract 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. Keywords Urban dynamics · Urban areas of interest · Quantitative analysis · Volunteered geographic information · Social media data JEL Classification R0 * Meixu Chen 1 Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Roxby Building, 74 Bedford St S, Liverpool L69 7ZT, UK 13 Vol.:(0123456789) M. Chen et al. 1 Introduction The rapid growth of urban populations across the globe is resulting in new kinds of technical, physical, material and social challenges and constraints (Chourabi et al. 2012). With the aim of tackling such issues, how to make a city ‘smart’ has become a significant strategy in many developed and developing regions of the world. Although there is no standard definition of the smart city, common characteristics can be summarised as an integrated system connecting digital technologies, critical infrastructures and citizens, to plan, govern and manage a city in order to improve its sustainability, optimise processes and maximise the provision of collective public and private services (Harrison et al. 2010; Washburn et al. 2010; Batty 2017). Integral to the operationalisation of a smart city, it is often of relevance to obtain timely insights into the dynamics of urban population at a temporal granularity finer than that of traditional surveys, which can be enhanced by or provided through digital technologies. It is within this context that the present paper engages with the concept of urban areas of interest (UAOI), which refers to parts of the urban built environment that can be delineated in their extent through the clustering of human activity. Such areas may contain business zones, tourist attractions, iconic landmarks, recreational zones or other attractors (Hu et al. 2015). The notion of a UAOI is, therefore, a combination of morphological features including buildings and streets, and ‘points of interest’, as defined by the relevance the population concedes specific parts of cities. As such, a UAOI can be viewed as a perceptual space, which is captured by the social morphology of the city, albeit rooted in physical space (Crooks et al. 2016). Accordingly, a UAOI should emerge from the activities of a large collective of different people to avoid very individual conceptions. Furthermore, such definitions are complex, as unlike well-defined geographic divisions or administrative districts, the delineation of a UAOI may vary between people in different contexts, ages and cultures. Identifying and understanding UAOIs has applications in multiple fields. For spatial planning, they may assist in identifying areas with greater public priority in the context of limited resource availability (Gandy 2006). For retailing, they can help identify areas where people cluster, and how these have evolved over time, which might aid in store location or for targeting advertisements more effectively. For transport planning, they may help prioritise traffic flows or the provision of public transport; for statistical agencies, they may provide useful reference distributions in comparison with official geographical divisions. The challenge of defining UAOIs over time resides in the need for granular spatiotemporal data recorded within cities. Although traditional data sources used in urban studies, such as remotely sensed data, have a lengthy history of application and can be used to characterise urban morphology, they do not capture human dynamics beyond expansion or contraction of the built form. Alternatively, survey or census data might be utilised to inform the discovery of UAOIs, but these are usually costly to administer and may be of limited temporal granularity (Shi et al. 2014; Tasse and Hong 2014). A third alternative has emerged in the last few years. Several new forms of digital data 13 Understanding the dynamics of urban areas of interest through… derived from urban activity through passive or active forms of data collection capture urban form and/or social functional geography (Arribas-Bel 2014; Crooks et al. 2016). Such data are referred to as volunteered geographic information (VGI; Goodchild 2007), which includes the use of digital devices by communities or individuals to create, accumulate, upload and communicate geographic information, typically through contemporary web technology. Commonly designated as VGI is a variety of content from social media networks, which often support geolocation of assets and include networks such as Twitter, Facebook, Flickr and Instagram. Data derived from these networks have been used in a variety of contexts to explore spatial, temporal and even semantic information about human activities (Jiang et al. 2015; Lansley and Longley 2016; Lloyd and Cheshire 2017; Gao et al. 2017). In this paper, we examine the potential of data derived from the online photograph management and sharing website Flickr to extract and understand urban areas of interest. Although there are inherent biases associated with geotagged Flickr data, a number of studies have utilised these data effectively to explore various issues within urban contexts (Hollenstein and Purves 2010; Lee et al. 2014; Hu et al. 2015; Gao et al. 2017). Flickr offers an attractive proposition as a data source for a number of reasons. The scale of the Flickr network is extensive and, as of 2016, Flickr had 122 mi (...truncated)