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.
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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)