An Interactive Approach for Exploration of Flows Through Direction-Based Filtering

Vrotsou, Katerina; Fuchs, Georg; Andrienko, Natalia

doi:10.1007/s41651-017-0001-7

Cited by 17 publications

(12 citation statements)

References 38 publications

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“…A second group of techniques rely on interaction as an exploration tool, for example through filtering the data [VFAA17], or selecting subsets of the data, which are then displayed in separate, superimposed or juxtaposed visualizations [HSS11,LMYH11]. The Flowstrates technique offers rich interaction capabilities, for example letting the user interactively select which origin and destination locations should be displayed in the central heatmap [BBBL11].…”

Section: D4-interact: Requiredmentioning

confidence: 99%

Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

Schöttler

Yang

Pfister

et al. 2021

Computer Graphics Forum

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This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time and uncertainty. Our overview analyses existing techniques along four dimensions: (i) the representation of geographical information, (ii) the representation of network information, (iii) the visual integration of both and (iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade-offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. https:// geonetworks.github.io.

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Section: D4-interact: Requiredmentioning

confidence: 99%

Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

Schöttler

Yang

Pfister

et al. 2021

Computer Graphics Forum

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“…Understanding route choice behavior of taxi trajectories is essential to sustainable urban transport. Wardrop's transportation theory demonstrates that drivers minimize travel time for route choice behavior [33]. Following the shortest path is one of the major features of human route choice behavior, because the shortest path potentially exhibits the minimum travel time [34].…”

Section: Taxi Trip Patterns Analyticsmentioning

confidence: 99%

Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Shen

et al. 2020

IJGI

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Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.

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“…Transaction links are drawn as a "Flow map" [11], i.e., arcs that are bent towards the y-axis -resulting in reduced occlusion between bins of companies and the transaction edges between them. Additionally, the use of the vertical dimension intends to reduce edge crossings [26]. Flow maps have been proven to be more effective than straight links in conveying flow direction and volume [10], and precedent work investigated their use on a geographical context [20].…”

Section: Visualizationmentioning

confidence: 99%

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

Arleo

Sorger

Tsigkanos

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Figure 1: The different visualizations composing the Sabrina system. The three views (a, b, c) display the firm density and transaction data of an Austrian economy dataset [3]: (a) gradient encoding in blue/red indicates whether the firm density in a hexagonal area increased/decreased in respect to the previous time step; (b) inferred transactions between individual companies; (c) regionally clustered firm data. The interface allows the user to (d) navigate the temporal data dimension, (e) show aggregated details on selected transaction flows, and (f) adjust encoding and filtering options. Tooltips (g) show details on firms within a bin. ABSTRACTInvestment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

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An Interactive Approach for Exploration of Flows Through Direction-Based Filtering

Cited by 17 publications

References 38 publications

Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

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