Understanding predictability and exploration in human mobility

Cuttone, Andrea; Lehmann, Sune; González, Marta C.

doi:10.1140/epjds/s13688-017-0129-1

Cited by 110 publications

(165 citation statements)

References 37 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…They base their conclusion on a logistic regression model using information on calls, SMS, proximity via Bluetooth and mobility from GPS. Using the same dataset Cuttone et al (2018), discussed the contrasting ideas of predicting a visit to an already visited location against predicting exploration of newer locations. In addition, they discussed the effect of spatial and temporal resolutions on predictive power.…”

Section: Human Mobility Patternsmentioning

confidence: 99%

Social physics: uncovering human behaviour from communication

Bhattacharya

Kaski

2018

Advances in Physics: X

View full text Add to dashboard Cite

In the post year 2000 era the technologies that facilitate human communication have rapidly multiplied. While the adoption of these technologies has hugely impacted the behaviour and sociality of people, specifically in urban but also in rural environments, their "digital footprints" on different data bases have become an active area of research. The existence and accessibility of such large population-level datasets, has allowed scientists to study and model innate human tendencies and social patterns in an unprecedented way that complements traditional research approaches like questionnaire studies. In this review we focus on data analytics and modelling research -we call Social Physics -as it has been carried out using the mobile phone data sets to get insight into the various aspects of human sociality, burstiness in communication, mobility patterns, and daily rhythms.

show abstract

Section: Human Mobility Patternsmentioning

confidence: 99%

Social physics: uncovering human behaviour from communication

Bhattacharya

Kaski

2018

Advances in Physics: X

View full text Add to dashboard Cite

show abstract

“…Song and Barabási [39] and Gallotti [40] used entropy to predict individual mobility patterns. Moreover, Cuttone et al [41] found out that there are also some relationships between the spatial and temporal resolution of the mobile phone data and the accuracy of predicting human mobility. The effectiveness of the sparse location data in the characterization of individual human mobility should be paid more attention.…”

Section: Mobile Phone Location Data For Human Mobility Researchmentioning

confidence: 99%

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

Fang

Zhang

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual's records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2-to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.

show abstract

“…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: 93%

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

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

Understanding predictability and exploration in human mobility

Cited by 110 publications

References 37 publications

Social physics: uncovering human behaviour from communication

Social physics: uncovering human behaviour from communication

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

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

Contact Info

Product

Resources

About