Two-point Equidistant Projection and Degree-of-interest Filtering for Smooth Exploration of Geo-referenced Networks

The visualization and interactive exploration of geo-referenced networks poses challenges if the network’s nodes are not evenly distributed. Our approach proposes new ways of realizing animated transitions for exploring such networks from an ego-perspective. We aim to reduce the required screen estate while maintaining the viewers’ mental map of distances and directions. A preliminary study provides first insights of the comprehensiveness of animated geographic transitions regarding directional relationships between start and end point in different projections. Two use cases showcase how ego-perspective graph exploration can be supported using less screen space than previous approaches.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/vis55277.2024.00023
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
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article

Two-point Equidistant Projection and Degree-of-interest Filtering for Smooth Exploration of Geo-referenced Networks

Steffen Koch, Samuel J. Beck, Max Franke
Target Tracking and Data Fusion in Sensor Networks
article

Two-point Equidistant Projection and Degree-of-interest Filtering for Smooth Exploration of Geo-referenced Networks

Steffen Koch, Samuel J. Beck, Max Franke
article en

Abstract

The visualization and interactive exploration of geo-referenced networks poses challenges if the network’s nodes are not evenly distributed. Our approach proposes new ways of realizing animated transitions for exploring such networks from an ego-perspective. We aim to reduce the required screen estate while maintaining the viewers’ mental map of distances and directions. A preliminary study provides first insights of the comprehensiveness of animated geographic transitions regarding directional relationships between start and end point in different projections. Two use cases showcase how ego-perspective graph exploration can be supported using less screen space than previous approaches.

University of Stuttgart (DE)
Climate action
Openalex Percentile: Top 80%
Target Tracking and Data Fusion in Sensor Networks
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