Multi-scale analysis of linear data in a two-dimensional space

Qiang, Yi; Chavoshi, Seyed Hossein; Logghe, Steven; Maeyer, Philippe De; Weghe, Nico Van de

doi:10.1177/1473871613477853

Cited by 14 publications

(21 citation statements)

References 32 publications

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“…Qiang et al [57] propose an original technique for simultaneously representing movement characteristics in full detail and at different levels of aggregation. In a 2D display, the horizontal dimension represents time or the street extent, and the vertical dimension corresponds to different levels of aggregation, from maximal detail at the bottom to maximal aggregation (i.e., a single value) at the top.…”

Section: Movement Dynamics Along a Routementioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

118

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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Section: Movement Dynamics Along a Routementioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

118

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“…Continuous temporal model A conceptually appealing model to represent the multi-temporal nature of geographical information is the CTM (Qiang et al 2013). 2 This model is an extension of a 2D representation of intervals, the triangular model (TM) (Qiang et al 2010(Qiang et al , 2012a(Qiang et al , 2012b, which was originally introduced by Ligozat (1994Ligozat ( , 1997 and Kulpa (1997Kulpa ( , 2006.…”

Section: Multi-scale Temporal Analysis Versus Multi-scale Spatial Anamentioning

confidence: 99%

“…In this way, the horizontal position of the point in the triangle represents the midpoint mid(I) of the interval and the vertical position h represents its duration dur(I) (Figure 2b). Qiang et al (2013) extended TM to a continuous space, leading to CTM. CTM can be used effectively for multi-scale temporal analysis.…”

Section: Multi-scale Temporal Analysis Versus Multi-scale Spatial Anamentioning

confidence: 99%

“…Ways to present and analyse temporal variations in geographical data have been presented by, among others, Van Wijk and Selow (1999), Weber et al (2001), and Li and Kraak (2008). More recently, Qiang et al (2010, 2012a, 2012b, 2013, Qiang 2012 have demonstrated the advantages of projecting linear temporal data onto a 2D space in various case studies. Their approach can compactly represent temporal data at multiple temporal scales in a single view, enabling the discovery of interesting patterns at whatever level of temporal detail.…”

Section: Introductionmentioning

confidence: 99%

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The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis

Weghe

Roo

Qiang

et al. 2014

International Journal of Geographical Information Science

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When studying geographical phenomena, different levels of spatial and temporal granularity often have to be considered. While various approaches have been proposed to analyse geographical data in a multi-scale perspective, they have all focused on either spatial or temporal attributes rather than on the integration of space and time over multiple scales. This study introduces the continuous spatio-temporal model (CSTM), a conceptual model that seeks to address this shortcoming. The presented model is based on (1) the continuous temporal model (CTM), a multi-scale model for temporal information, and (2) the continuous spatial model (CSM), an extension of CTM for multi-scale spatial raster data. At the core of the presented conceptual model is a spatio-temporal evolution element or, in short, stevel, which is described by four variables: (1) pixel location, (2) spatial resolution, (3) temporal interval, and (4) temporal resolution. By varying one or more of these variables, a CSTM-tree consisting of (sets of) stevel arrays is created, forming the basis of an exhaustive CSTM-typology. These arrays can then be used to systematically cluster spatio-temporal information. The value of our approach is illustrated by means of a simplified example of mean temperature evolution. Various suggestions are made for modifications to be developed in future research

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“…He argues that this approach is advantageous in various applications and theoretical and has comprehensively elaborated its use in qualitative interval reasoning and interval arithmetic. Van de Weghe et al (2007) (Qiang, et al, 2010) and interval analysis (Qiang, et al, 2012a;2012b, 2013. Since the TM displays a set of intervals within a stable point structure, it offers special insights into interval distributions, particularly when a large amount of intervals are represented in it.…”

Section: Introductionmentioning

confidence: 99%

Representing time intervals in a two-dimensional space: An empirical study

Qiang

Valcke

Maeyer

et al. 2014

Journal of Visual Languages & Computing

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ObjectiveInstead of the linear model (LM), time intervals can be represented by a two-dimensional (2D) model, which is called the Triangular Model (TM). Although the TM has been introduced for decades and applied in some areas, there still a lack of empirical studies on its usability. To fill this gap, this study aims to evaluate how people perform when using the TM to answer questions on time intervals, in comparison with using the traditional LM. MethodAround 250 novice participants took part in the experiment, which consisted of a video training, a pretest and posttest. The video training introduced the basic knowledge of temporal relations and the two representations. The pretest allowed participants to practice the knowledge they have learned and receive feedbacks of the answers. In the posttest, participants' accuracy and speed when answering the questions were recorded for analysis.The results of using the TM and the LM were compared in pairs. The null hypothesis is that the participants produce equal results with the two models. ResultThe results showed that the participants scored better and spent less time when answering questions with the TM, which rejected the null hypothesis. Moreover, the score and speed when they used the TM did decline in the questions containing a larger number of intervals.In contrast, the score and accuracy when they used the LM declined when questions containing a larger number of intervals. Conclusion-The TM is easy to learn. After a 20-minute training, novice participants can use it to solve questions and produce satisfactory result.-The TM is easy and efficient for visual queries of time intervals.-The TM is easy to use for handling a large number of intervals. Implication-The TM can be widely applied in analysing time intervals and linear data.-Tools implementing the TM can be learned and used by novice users.3

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Multi-scale analysis of linear data in a two-dimensional space

Cited by 14 publications

References 32 publications

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis

Representing time intervals in a two-dimensional space: An empirical study

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