Time-Geography in Four Dimensions: Potential Path Volumes around 3D Trajectories

Demšar, Urška; Long, Jed A.

doi:10.21433/b3117gc866qs

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of the aforementioned 4M contexts, as illustrated in Figure 1, are constrained and are subjected to one of these spaces, and thus, the contexts are modeled differently. The movement spaces can be in any form of 1-D, 2-D, 2.5-D, 3-D, 76 and 4-D. 77 All of the above contexts are interrelated and affect one another or other entities in various manners (the solid arrows in Figure 1). We further present the relational and interactional additives of context to the introduced taxonomy.…”

mentioning

confidence: 99%

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

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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...

show abstract

mentioning

confidence: 99%

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

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Therefore, the exact path and the potential range of movement can be analysed using a space time prism (e.g. Kwan & Lee 2003; Winter & Yin 2011; Demsar & Long 2016; Liao 2019). In this study, the analysed timeframe includes multiple years.…”

Section: Methodsmentioning

confidence: 99%

Analysing Long Term Spatial Mobility Patterns of Individuals and Large Groups Using 3D‐GIS: A Sport Geographic Approach

Rauch

2022

Tijd voor Econ & Soc Geog

View full text Add to dashboard Cite

Individual mobility and human patterns analyses is receiving increasing attention in numerous interdisciplinary studies and publications using the concept of time‐geography but is largely unknown to the subdiscipline of sports geography. Meanwhile the visualization and evaluation of large data of individual patterns are still a major challenge. While a qualitative, microscale view on spatial‐temporal topics is more common in today's pattern research using mostly 24h time intervals, this work examines a quantitative approach focusing on an extended period of life. This paper presents a combination of time‐geographic approaches with 3D‐geoinformation systems and demonstrates their value for analysing individual mobility by implementing a path‐homogeneity factor (HPA). Using the example of professional athletes, it is shown which groups display greater similarities in their career paths. While a high homogeneity suggests that groups make similar decisions through socially influenced processes, low values allow the assumption that external processes provide stronger, independent individual structures.

show abstract

“…This poses a particularly difficult problem for visualisation, not only from the perspective of having to show the third dimension on a two-dimensional display, but also from having this dimension significantly smaller than the other two dimensions, resulting in a ‘pancake’ display, a typical example of which are 3D kernel density volumes. 21 This can to some extent be alleviated through geometric methods 22 or by an alternative hexagonal representation of the 3D space. 23 Other 3D aspects of movement are captured by accelerometers (3D motion) and magnetometers (3D orientation) and this information can be used to augment other movement data.…”

Section: Visual Analytics For Movementmentioning

confidence: 99%