Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

Abstract

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intraand inter-regional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the nonadministrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.

Revisiting the Role of Place in Geographic Information Science

“…Various recent studies using social media, particularly Twitter data, aim to reveal a sense of place. While many computer scientists and computer linguists, on the other hand, discover the power of spatial concepts [95,[97][98][99][100][101][102][103], and likewise, GIScientists discover the potential of semantic analyses [77,94,96,[104][105][106], the dividing line between spatial and non-spatial applications diminishes.…”

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

“…In addition, land use types at the city scale can be derived based on the dynamics of human spatial presence across the urban landscape (Pei et al, ). Moreover, according to movement between places and interactions via phone and social media, spatial networks can be generated and boundaries of socially coherent communities and their dynamics inferred (Yin, Soliman, Yin, & Wang, ).…”

Examining the Relationship between Urban Land Expansion and Economic Linkage Using Coupling Analysis: A Case Study of the Yangtze River Economic Belt, China

“…The economic mass, M i,j , of place i or j is an important index for the gravity model. In this expression, M i,j can be measured by principal component analysis, and we selected a compressive index system considering the availability of data and the actual situation in the study area, as well as other reference studies (Table 1) [30,34]. evaluation accuracy of the first-grade classification of land use reached 94.3%; the comprehensive accuracy of the second-grade classification reached 91.2% and met the mapping accuracy of 1:100,000 scale data.…”