On the regularity of human mobility

Oliveira, Eduardo Mucelli Rezende; Viana, Aline Carneiro; Sarraute, Carlos; Brea, Jorge; Alvarez-Hamelin, José Ignacio

doi:10.1016/j.pmcj.2016.04.005

Cited by 43 publications

(14 citation statements)

References 26 publications

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“…This is partly due to the fact that actual routes between destinations had not bee collected in VERITAS in this study, but more convincingly, it questions the regularity in participants' spatiotemporal behaviours. Previous studies, mostly based on cellphone location data, have demonstrated a high level of regularity in peoples' spatio-temporal behavior [35][36][37], both outside but even inside homes [38]. Yet, whereas most people have highly routinized activities, schedules, and destinations, some have less regular roaming spaces.…”

Section: Discussionmentioning

confidence: 99%

Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

Kestens

Thierry

Shareck

et al. 2018

Spatial and Spatio-temporal Epidemiology

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Section: Discussionmentioning

confidence: 99%

Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

Kestens

Thierry

Shareck

et al. 2018

Spatial and Spatio-temporal Epidemiology

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“…Human mobility is strongly regular, from multiple perspectives. People frequently return to a same, limited set of locations [3,24], which results in repetitive sequences of visits to a few places [7,24], and infrequent trips to other locations in a (typically large) geographical region [24]. Regularity also occurs in time, as the locations above are visited in highly periodic patterns [4].…”

Section: Design Rationalementioning

confidence: 99%

Complete trajectory reconstruction from sparse mobile phone data

et al. 2019

Self Cite

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Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

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“…The uncertain and fragmented network topology is a major obstacle for telling exactly how and where to get in touch with a given node when necessary in a distributed environment. Fortunately, implicit in the movement of people is the periodicity and regularity to a certain degree, both in social [62] and geographical [63] aspects. In other words, people have the propensity to meet a group of persons and go to a collection of places more often.…”

Section: Map-based Region Partitioning For Content Disseminationmentioning

confidence: 99%

Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval

Xiang

et al. 2019

IJGI

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Geospatial information is gaining immense interest and importance as we enter the era of highly developed transportation and communication. Despite the proliferation of cellular network and WiFi, on some occasions, users still face barriers to accessing geospatial data. In this paper, we design and implement a distributed prototype system with a delay/disruption tolerant network (DTN), named Geo-DMP, for cooperatively and opportunistically sharing and exchanging named geospatial contents in a device-to-device fashion. First of all, we construct a lightweight “content agent” module to bridge the gap between the application layer and the underlying DTN protocol stack. Afterwards, to profile the mobility history of users in practical geospatial environments, we present a map segmentation scheme based on road network and administrative subdivision information. Subsequently, we associate the regional movement history information with the content retrieval process to devise a hierarchical and region-oriented DTN routing scheme for both requests and responses. Finally, we conduct extensive experiments with real-world trajectories and complete implementations on the emulation platform composed of virtual machines. The experiments corroborate that Geo-DMP has the capability of successfully retrieving geospatial contents for users for most of the time under mobile circumstances with episodic connectivity. Moreover, en-route caches can be efficiently exploited to provision contents from multiple sources with less network resource consumption and shorter user-perceived latencies.

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On the regularity of human mobility

Cited by 43 publications

References 26 publications

Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

Complete trajectory reconstruction from sparse mobile phone data

Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval

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