Interactive Generation of 1D Embeddings from 2D Multi-dimensional Data Projections

Ngo, Quynh Quang; Linsen, Lars

doi:10.2312/vmv.20201190

Cited by 1 publication

(1 citation statement)

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“…Zhou et al [ZJW21] extend this concept by traversing on a Hamiltonian path based on a space-filling curve. Ngo and Linsen [NL20] combine dimensionality reduction to 2D with interactive 2D to 1D mapping. Guo et al [GPG02;GG06;Guo07] suggested that complete-linkage hierarchical clustering to derive spatial orderings from geographical data provided better results than space-filling curves.…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of Spatio‐temporal Phenomena with 1D Projections

Franke

Henry

Koch

et al. 2021

Computer Graphics Forum

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It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio‐temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio‐temporal data, based on a set of neighborhood‐preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real‐world application scenarios and discuss the feedback we received from domain and visualization experts.

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