Understanding urban human activity and mobility patterns using large-scale location-based data from online social media

Hasan, Samiul; Zhan, Xianyuan; Ukkusuri, Satish V.

doi:10.1145/2505821.2505823

Cited by 264 publications

(191 citation statements)

References 21 publications

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“…Reference [35], when analysing the temporal evolution of the Brazilian air network, states that "a reasonable fit is obtained by using a stretched exponential", although no statistical analysis is provided. Finally, [36] correctly recognises that, even though there is a "suggestive scaling behavior" in the distribution of node degrees in maritime networks, "simple models for generating scale-free statistics are not sufficient to describe these empirical networks"; similar careful observations have been made for travel demand networks at the urban scale [37][38][39][40] and locationbased analysis of data from social media [41].…”

Section: Common Pitfalls and Misleadingmentioning

confidence: 90%

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

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The introduction of complex network concepts in the study of transportation systems has supposed a paradigm shift and has allowed understanding different transport phenomena as the emergent result of the interactions between the elements composing them. In spite of several notable achievements, lurking pitfalls are undermining our understanding of the topological characteristics of transportation systems. In this study, we analyse four of the most common ones, specifically related to the assessment of the scale-freeness of networks, the interpretation and comparison of topological metrics, the definition of a node ranking, and the analysis of the resilience against random failures and targeted attacks. For each topic we present the problem from both a theoretical and operational perspective, for then reviewing how it has been tackled in the literature and finally proposing a set of solutions. We further use six real-world transportation networks as case studies and discuss the implications of these four pitfalls in their analysis. We present some future lines of work that are stemming from these pitfalls and that will allow a deeper understanding of transportation systems from a complex network perspective.

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Section: Common Pitfalls and Misleadingmentioning

confidence: 90%

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

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“…Many studies show that humans mobility follows simple reproducible patterns. The mobility patterns show a high degree of temporal and spatial regularity and can be considered as mobility intentions [14,25]. For example, commuting which is a basic mobility pattern in many spatiotemporal dataset can be used to explain why a worker arrived at the work place around 9 a.m. on work days.…”

Section: Mobility Intention Extractionmentioning

confidence: 99%

Why You Go Reveals Who You Know: Disclosing Social Relationship by Cooccurrence

Feng

Wang

et al. 2017

Wireless Communications and Mobile Computing

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The popularity of location-based services (LBS) and the ubiquity of sensor device have resulted in rich spatiotemporal data. A large number of human behaviors had been recorded including cooccurrence which refers to the phenomenon that two people have been to the same places at the same time. These data enable attackers to infer people's social relationship based on their cooccurrences and many attack models were proposed. However, current attack models still cannot effectively address the following two challenges: How to distinguish cooccurrences between acquaintances and strangers? What kind of cooccurrence contributes to strong social strength? In this paper, we present a novel social relationship attack model-the Mobility Intention-based Relationship Inference (MIRI) model-which can solve the above two issues. Firstly, we extract mobility intentions and adopt them to characterize cooccurrences. A classification model is trained for attacking social relationship. The experimental results on two real-world datasets demonstrate that the proposed MIRI model can properly differentiate cooccurrences by simultaneously considering spatial and temporal features. The comparison results also indicate that MIRI model significantly outperforms state-of-the-art social relationship attack models.

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“…In the literature, we find that some researchers use these devices for land-use identification-for instance, the demonstration of GPS data for discovering a region and sensing human activity [12], urban Wi-Fi characterization [13], land-use and landscape identification using cell-phone data [14][15][16]. However, these models concentrate on a particular region in a specific area, the lack of information from this data [17] and difficult to identify the user's footprint.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Land-Use Map Based on Twitter Data

2017

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Location-based social media allows people to communicate and share information on a popular landmark. With millions of data records generated, it provides new knowledge about a city. The identification of land use intends to uncover accurate positions for future urban development planning. The purpose of this research is to investigate the use of social networking check-in data as a source of information to characterize dynamic urban land use. The data from this study were obtained from the social media application i.e., Twitter. Three kinds of data that are prioritized in this research are check-ins (specific location), timestamps, and a user's status text or post activities. In this study, we propose a grid-based aggregation method to divide the urban area. Two different approaches are compared-rank and clustering methods to group the place's activities. Then we utilize time distribution frequency to attain the land-use function. In this case, Makassar City, Indonesia, has been selected as the case study. An analysis shows that the check-in activity and the method we proposed can be used to group the actual land-use types.

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Understanding urban human activity and mobility patterns using large-scale location-based data from online social media

Cited by 264 publications

References 21 publications

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Why You Go Reveals Who You Know: Disclosing Social Relationship by Cooccurrence

Dynamic Land-Use Map Based on Twitter Data

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