Cartographic visualization of human trajectory data: overview and analysis

Gonçalves, Tiago; Afonso, Ana Paula; Martins, Bruno

doi:10.1080/17489725.2015.1074736

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…Taking into account that the presence of clutter is a deciding factor in the usability of the STC, with some reports that more than 10 trajectories result in excessive clutter [13,17], we decided to investigate two Order of events Who was the first to arrive at his/her lunch appointment on the 7th? U,S K,P U,P different data scenarios: a Simple dataset with only 3 different trajectories (see Fig.…”

Section: Data Scenariosmentioning

confidence: 99%

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

Filho

Stuerzlinger

Nedel

2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Our immersive space-time cube (STC) aims to lower the known steep learning curve of this three-dimensional spatio-temporal representation. The base map is coupled to a virtual reproduction of the analyst's own real desk and all data manipulations and queries are implemented through intuitive gestures and tangible controls, increasing usability and comfort, while decreasing mental workload.Abstract-A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.

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Section: Data Scenariosmentioning

confidence: 99%

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

Filho

Stuerzlinger

Nedel

2020

IEEE Trans. Visual. Comput. Graphics

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“…The addition of time lines to tools such as Google Earth has begun the necessary move from 2D to 4D mapping, and this trend is unlikely to end soon. Analysis tools to identify group behavior in trajectories are now beginning to be developed, and pattern-and movement-based syntaxes and semantics are now undergoing research (Gonçalves, Afonso, & Martins, 2015). Such analysis and its cartography have generated interest in the analysis of human mobility data, often revealed through social media and high resolution imagery (González, Hidalgo, & Barabási, 2008).…”

Section: Theme 3: Spatial Analysis and Applications In Cartographymentioning

confidence: 99%

“…Computational needs, for example in partitioning in support of parallel and high performance computing, have led to GIScience research that expands upon what has been called cyberGIS (Wang, 2010;Wang & Goodchild, 2018). Tracking data have presented its own new sets of challenges and applications (Gonçalves et al, 2015;González et al, 2008); yet, human movement data have been shown to have a high degree of both spatial autocorrelation and predictability.…”

Section: Theme 5: Gisciencementioning

confidence: 99%

Contemporary American cartographic research: a review and prospective

Clarke

Johnson

Trainor

2019

Cartography and Geographic Information Science

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We review recent developments in cartographic research in North America, in the context of informing the 29th International Cartographic Conference, and 18th General Assembly in 2019. The titles of papers published since 2015 in four leading cartographic journals yielded a corpus of 245 documents containing 1109 unique terms. These terms were analyzed using Latent Dirichlet Allocation and by visual analytics to produce 14 topic groups that mapped onto five classes. These classes were named as information visualization, cartographic data, spatial analysis and applications, methods and models, and GIScience. The classes were then used as themes to discuss the recent cartographic literature more broadly, first, to review recent trends in the research and to identify research gaps, and second, to examine prospects for new research over the next 20 years. A conclusion draws some broad findings from the review, suggesting that cartographic research in the future will be aimed less at dealing with data, and more at generating insight and knowledge to better inform society about global challenges.

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“…Nowadays, there are plenty of exploratory studies on how best to model and visualize 2D/3D geospatial information (such as maps, images, and 3D models) on virtual globes [64][65][66]. Visualizing and analyzing geospatial objects in four dimensions (x, y, z, and time), however, is still a relatively new field [4][5][6][7][8][9][10][11][12][13][14][15][67][68][69][70]. With the increased attention being paid to the fourth dimension of the Earth space, and the significant improvements to acquire spatio-temporal data, more and more people are getting interested in the representation and analysis of time-dynamic geospatial objects on virtual globes [71,72].…”

Section: The Role Of Czml In Geoscientific Researchmentioning

confidence: 99%

“…In recent years, the importance of time-dynamic geospatial objects and their virtual globe-based applications has been rapidly increasing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. At present, the most favorite schema for describing custom geospatial objects is the Keyhole Markup Language (KML) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part II: Impact, Comparison, and Future Developments

Zhu

Wang

2018

IJGI

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This is the second and final part of our Cesium Markup Language (CZML) study. Here, we describe the relevant applications, academic influence, and future developments of CZML. Since its emergence in 2011, CZML has become widely used in the geoscientific environment. It is also having a positive impact on geoscience. Numerous applications use CZML for generating time-dynamic geovisualization, facilitating data interoperability, and promoting spatial data infrastructures. In this paper, we give an overview of the available tools and services, representative applications, as well as the role that CZML plays for geoscientific research. Furthermore, we also discuss key similarities and differences between CZML and KML (Keyhole Markup Language), and outline some of the future improvements for CZML's research and development.

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Cartographic visualization of human trajectory data: overview and analysis

Cited by 15 publications

References 36 publications

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

Contemporary American cartographic research: a review and prospective

Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part II: Impact, Comparison, and Future Developments

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