Towards Semantic Interpretation of Movement Behavior

Baglioni, Miriam; Macêdo, José Antônio Fernandes de; Renso, Chiara; Trasarti, Roberto; Wachowicz, Mónica

doi:10.1007/978-3-642-00318-9_14

Cited by 48 publications

(47 citation statements)

References 8 publications

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“…Scientists are developing data-mining algorithms to detect and extract movement patterns (Gudmundsson et al, 2007;Laube, 2009), and applying visual analytics approaches to explore and understand those movement patterns (Andrienko and Andrienko, 2008;Thomas and Cook, 2006). Other researchers are interested in extracting significant locations from movement data (Thomason et al, 2015), exploring the spatial properties of movement data such as spatial autocorrelation (Orellana and Wachowicz, 2011), or enriching movement data with semantics (Alvares et al, 2007;Baglioni et al, 2009;Bogorny et al, 2010).…”

Section: Methodological Componentmentioning

confidence: 99%

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

Orellana¹,

Hermida²,

Osorio³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by specialized research groups with relatively narrow approaches from different disciplines. Consequently, broader questions remain less explored, mainly those relating to spatial behaviour of individuals and populations with their geographic environment and the motivations and perceptions shaping such behaviour. Understanding sustainable mobility and exploring new research paths require an interdisciplinary approach given the complex nature of mobility systems and their social, economic and environmental impacts. Here, we introduce the elements for a multidisciplinary analytical framework for studying active mobility patterns comprised of three components: a) Methodological, b) Behavioural, and c) Perceptual. We demonstrate the applicability of the framework by analysing mobility patterns of cyclists and pedestrians in an intermediate city integrating a range of techniques, including: GPS tracking, spatial analysis, auto-ethnography, and perceptual mapping. The results demonstrated the existence of non-evident spatial behaviours and how perceptual features affect mobility. This knowledge is useful for developing policies and practices for sustainable mobility planning.

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Section: Methodological Componentmentioning

confidence: 99%

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

Orellana¹,

Hermida²,

Osorio³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The DMQL incorporates a reasoning component, which allows one to specify domain-driven ontologies using the Web Ontology Language (OWL); the typical use of this feature is to specify different types of trajectories and patterns [22]. The main objective of the semantic component is to enrich both trajectories and mined patterns with domain knowledge, thus making an explicit representation of semantic concepts.…”

Section: Mastering the Geopkdd Processmentioning

confidence: 99%

Mobility, Data Mining and Privacy: The GeoPKDD Paradigm

Giannotti¹,

Trasarti²

2010

Proceedings of the 2009 SIAM Conference on “Mathematics for Industry”

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The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data. Miniaturization, wearability, pervasiveness of mobile devices are producing traces of our mobile activity, with increasing positioning accuracy and semantic richness: location data from mobile phones (Global System for Mobile Communications: GSM cell positions), Geographic Positioning System (GPS) tracks from mobile devices receiving geo-positions from satellites, etc. The objective of the GeoPKDD (Geographic Privacy-aware Knowledge Discovery and Delivery), a project funded by European Commission under the Future and emerging technologies (FET) program of the 6th Framework(FP6), has been to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. Pursuing this ambitious objective, the GeoPKDD project has started a new exciting multidisciplinary research area, at the crossroads of mobility, data mining, and privacy. This paper gives a short overview of the envisaged research challenges and the project achievements.

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“…Several studies on mobility data have considered static context elements such as static objects existing in the geographic space [3,4] and other properties of the geographic space [5]. However, dynamic context elements have been rarely considered despite being very common in real life applications of mobility data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…If it occurred, (2) to what extent can we estimate its start and end times? (3) What is the temporal pattern of arrival and departure of event attendees: (a) did they arrive progressively or immediately before the start of the event; (b) did they depart immediately after the end of the event or did they depart progressively, possibly taking some additional time to enjoy the location and/or experience before leaving the site?…”

Section: Introductionmentioning

confidence: 99%

How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern

Mazimpaka

Timpf

2017

IJGI

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Abstract:The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management.

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Towards Semantic Interpretation of Movement Behavior

Cited by 48 publications

References 8 publications

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

Mobility, Data Mining and Privacy: The GeoPKDD Paradigm

How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern

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