Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland

Abstract

High-Speed Rail (HSR) has increasingly become an important mode of inter-city transportation between large cities. Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development. Quantifying the impact of HSR's interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making. We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact. This paper adopts two types of spatial information: toponyms from social media posts, and the geographical location information embedded in social media posts. The framework highlights the asymmetric nature of social interaction among cities, and proposes a series of metrics to quantify such impact from multiple perspectives -including interaction strength, spatial decay, and channel effect. The results show that HSRs not only greatly expand the uneven distribution of inter-city connections, but also significantly reshape the interactions that occur along HSR routes through the channel effect.

Mapping cognitive place associations within the United Kingdom through online discussion on Reddit

“…Our gravity model gives a β coefficient of 1.00 (Pearson's R 2 0.772), reflecting a distance decay from co‐occurrences in Reddit comments that is stronger than decay observed in other studies that explored news articles (0.23; Hu et al., 2017), or general web queries (0.2; Liu et al., 2014). Confirming the existence of a general distance decay effect for Reddit derived places demonstrates that distance typically contributes to lower co‐occurrences in locations that are further apart, a similarity that is shared with past work that examines decay from the perspective of true population movements (Gong et al., 2021; Yang et al., 2019), and the social relationships of regions examined through social media (Bailey et al., 2018; Li et al., 2021). Our place associations generated from users on Reddit therefore appear to more appropriately incorporate a geographic component compared with city mentions in news articles or general web pages.…”

“…Within computational geography, most research has explored direct connections between places by investigating physical movements of individuals, using population movement data from both traditional data sources such as Census or surveys (Rae, 2009; Titheridge et al., 2009), or through alternative forms of data like transport records (Allard & Moura, 2016; Farber & Li, 2013; Gong et al., 2021; Yang et al., 2019), mobile phone data (Lin et al., 2019; Rowe, Lovelace, et al., 2022; SafeGraph, 2022), and geotagged social media (Arthur & Williams, 2019; Li et al., 2021; Ostermann et al., 2015; Steiger et al., 2015). However, focusing only on connections built through population movement conceals associations that persist through individuals or community subconsciously, regardless of any physical movement.…”

Investigating Metropolitan Hierarchies through a Spatially Explicit (Local) Approach

“…While the estimation and comparison of the global regression coefficients of the citysize rule (using linear, logarithmic, or generalized specifications) still feed the debate (both empirical and methodological) in regional science [108][109][110], (i) sources of heterogeneity in real-world data [111], (ii) spatially anisotropic effects [48], and (ii) the importance of changing time structures [57], require additional investigation. In this perspective, our study provides a first application to some of these research questions confirming, on the one hand, the topicality of the rank-size rule in an explicit analysis of metropolitan evolution and, on the other hand, the need of theoretical generalizations faced with latent (e.g., spacetime) statistical structures that may affect the model's precision [105]. Being aware of the descriptive and exploratory aim of our study, further research should clarify the possible impact of the spatial support used in this paper (namely, municipalities) with respect to other candidate solutions (e.g., regular grids) [56].…”

“…Metropolitan hierarchies, however, have an eminent spatial dimension. In fact, population distribution and the morphological structure of settlements follow non-automatic dynamics (i.e., dependent on their intrinsic demographic dimension or economic strength) derived from spatial interaction [45], accessibility [103], and the networks in which urban centers participate [104][105][106]. Space, therefore, remains a substantial dimension underlying the rank-size relationship, thus needing an explicit impact analysis [59].…”

Envisaging the Intrinsic Departure from Zipf’s Law as an Indicator of Economic Concentration along Urban–Rural Gradients

“…First, we focused on a complete population gradient (from the largest cities to the smaller villages), not censored at a specific demographic threshold, in line with Calderín-Ojeda [33]. This practical choice means managing data heterogeneity because of the presence of a relatively long tie of (medium-small) settlements, more or less sparse across the country, that contribute to the formation and consolidation of the metropolitan hierarchy in both accessible locations and more peripheral places [43]. In other words, with this choice, we are not exclusively interested in the goodness-of-fit of the rank-size relationship estimated on real data, but also in the inherent departure from the fit along the whole density gradient [44].…”