Analysis of movement data

Dodge, Somayeh; Weibel, Robert; Ahearn, Sean C.; Buchin, Maike; Miller, Jennifer A.

doi:10.1080/13658816.2015.1132424

Cited by 65 publications

(43 citation statements)

References 25 publications

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“…Context-awareness has recently received attention in many movement research directions in the areas of geographic information science (GIScience), [12][13][14][15] geocomputation (i.e., a wide array of spatial analytical methods and tools 16 ), visual analytics 17,18 (i.e., analytical reasoning facilitated by interactive visual interfaces 19 ), remote sensing 20 and tracking, 21 spatial data mining and knowledge discovery, or a combination thereof. The majority of this research has thus far merely used context as ancillary information to better understand mobilities, such as event-based movement analysis, 22 similarity measurement of trajectories, 23,24 uncertainty reduction and ranking in road networks, 25 uncertainty modeling associated with moving objects, 26 modeling spatial relevancy in context-aware systems, 27 determining significant places from mobility data, 28 visual analysis of movement behavior, 29 simulation models for movement, 30 analysis of human mobility patterns 31 and predictions, 32 and location prediction, 33 among others.…”

Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…As demonstrated by the rich content of this issue -and comparing with previous special issues edited by some of us (Purves et al 2014, Dodge et al 2016) -computational movement analysis continues to be a strongly developing research domain. At the workshop leading up to this special issue, a panel session was thus devoted to discussing future research trends.…”

Section: Computational Movement Analysis: a Possible Futurementioning

confidence: 99%

“…A number of previous special issues within IJGIS complement the suite of papers we present here. Specifically, Andrienko et al (2010) focusses on visualization of spatialtemporal data where movement data are emphasized, Zook et al (2015) looks at human mobility and mobile applications, Dodge et al (2016) explores the breadth of approaches encountered in the analysis of movement data, and Shaw et al (2016) looks at human dynamics in the big-data era. Here, in this issue, we focus on the development of computational methods and computational thinking in movement analysis owing to the rapid growth of movement datasets and new computational paradigms.…”

Section: Introductionmentioning

confidence: 99%

Moving ahead with computational movement analysis

Long

Weibel

Dodge

et al. 2018

International Journal of Geographical Information Science

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“…Tracked movement of objects is nowadays widely available and used for various applications in our society (Dodge et al 2016). Detailed vehicle movement for example can benefit the prediction of short-term and long-term traffic situations.…”

Section: Introductionmentioning

confidence: 99%

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Keler

Krisp

Ding

2017

Geo-spatial Information Science

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Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.

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Analysis of movement data

Cited by 65 publications

References 25 publications

Context‐aware movement analytics: implications, taxonomy, and design framework

Context‐aware movement analytics: implications, taxonomy, and design framework

Moving ahead with computational movement analysis

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

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