A Layout Technique for Storyline-based Visualization of Consecutive Numerical Time-varying Data

Geo-Storylines: Integrating Maps Into Storyline Visualizations

“…Later research has mostly focused on their automatic generation [7,24,31,33,48,49] and on applying them to different domains. For example, Storylines have been used to visualize dynamic social networks [44,59], collaboration in groups [33,36], genealogical data [28], temperature changes over time [58], and even to analyze eye tracking data [9]. A more recent line of research considers how additional information can be integrated into Storyline visualizations including mixing automatic and human input [50,51], non-linear narratives [37,38] and multiple relationships at once by branching their lines [17].…”

“…Designs that reduce 2D space to the y-axis can show distance but distort the horizontal space making horizontal direction or adjacency hard to determine. One exception is the work of Yagi et al [58] that shows temperature variation in Japan. Their visualization combines a map of Japan with a Storyline representation of temperature, and relates the two through color.…”

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

“…For disease spreading, this encoding can be used to show contacts in the same ward and forms a key part of our approach. Research in storyline visualization has focused on optimizing the compactness of storyline visualizations (either automatic or users-assisted) [4,23,37,47,48,51,61,62,67,68], reducing crossings [25,32,70,79], plotting approaches [60], combining storylines with event-based methods [3], genealogical data [31], streaming and dynamic data [66,81], and contacts between living things or actors exhibiting similar behavior [52]. Reda et al [52] is the closest approach to ours, but it needs to consider all contacts in the storyline.…”

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

“…For disease spreading, this encoding can be used to show contacts in the same ward and forms a key part of our approach. Research in storyline visualization has focused on optimizing the compactness of storyline visualizations (either automatic or users-assisted) [4,24,38,48,49,52,62,63,68,69], reducing crossings [26,33,71,80], plotting approaches [61], combining storylines with event-based methods [3], genealogical data [32], streaming and dynamic data [67,82], and contacts between living things or actors exhibiting similar behavior [53]. Reda et al [53] is the closest approach to ours, but it needs to consider all contacts in the storyline.…”