Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

PLOS ONE, Dec 2019

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

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Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

Citation: Liu Y, Sui Z, Kang C, Gao Y ( Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data Yu Liu 0 Zhengwei Sui 0 Chaogui Kang 0 Yong Gao 0 Peter Csermely, Semmelweis University, Hungary 0 Institute of Remote Sensing and Geographical Information Systems, Peking University , Beijing , China The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatiallyembedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a checkin data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips. - A number of social media websites that support geo-tagged information submission and sharing have been recently introduced and achieved great commercial success. Various functions have been provided by these websites, such as social networking (Facebook), micro-blogging (Twitter), photo sharing (Flickr), and location based check-in (Gowalla and Foursquare). Each website has millions of registered members and their submissions form an important type of big data. Since much information is usergenerated and associated with particular locations, Goodchild coined the term volunteered geographical information (VGI) for it [1]. In this paper, we use check-in record to denote a piece of geo-tagged content posted by a user. A check-in record generally includes a short textual message, a photo, and the time and location indicating when and where the message was posted. With a check-in data set, we can extract the footprints of large volumes of individuals. Although the trajectory of one particular person is rather stochastic, we can find underlying patterns when the number of trajectories increases. An interesting example is a map depicting the last 500 million check-in points on Foursquare that clearly demonstrate the human activity distribution across the world (https://foursquare.com/infographics/500million). Much research has been conducted using check-in data, sometimes with additional data such as social ties between users, collected from various sources. Several strands of status quo work can be identified. At the individual level, human mobility patterns [2,3] and geographical impacts on social networks [4,5] are investigated. At the aggregate level, these data enables us to study spatial activity distributions and spatial interactions between regions [6]. Recently, human mobility patterns have drawn much attention in the areas of physics [7], geography [8,9], and computer science [10], with the availability of multi-sourced trajectory data [11]. However, these studies either do not distinguish motion patterns at different spatial scales or focus on intra-urban trip patterns. It is natural that inter-urban trips have different mechanisms from those of intra-urban trips. For example, one in general has two frequently revisited anchor points (i.e. home and workplace) and commutes occupy a large proportion in intra-urban trips. On the contrary, we can only find one anchor point, corresponding to his (or her) home town, from an individuals trajectory at the interurban scale. However, whether there exists different mechanisms account for different human mobility patterns at and across different scales remains a research question. Little comparison research on this point has been done due to the lack of individuals inter-urban trajectories. Clearly, a check-in data set makes an investigation of inter-urban mobility possible for its large spatiotemporal coverage. In this research, we use a social media check-in data set submitted by about half millions users to study the inter-urban trip patterns. At the collective level, these trips represent spatial interaction strengths between cities. Our research serves three purposes. First, we intend to reveal the underlying distance effect in the trips extracted from check-in records. Second, we try to link patterns at the collective level of spatial interactions versus the individual level of human movements, and to make a comparison with intra-urban patterns revealed from mobile phone or taxi data sets. Last, we investigate the implications of distance decay effect in regionalizing the study area based on spatia (...truncated)


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Yu Liu, Zhengwei Sui, Chaogui Kang, Yong Gao. Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data, PLOS ONE, 2014, 1, DOI: 10.1371/journal.pone.0086026