Worbel: Aggregating Point Labels into Word Clouds
Sujoy Bhore, Robert Ganian, Guangping Li, Martin N\"ollenburg, Jules, Wulms

TL;DR
This paper introduces Worbel, a hybrid visualization method combining word clouds and point labeling to represent categorized spatial data, addressing the NP-hard problem with heuristics and SAT models, and evaluating their effectiveness.
Contribution
It proposes a novel visualization approach for categorically grouped spatial points, formulates the problem's computational complexity, and develops efficient heuristics and SAT-based solutions.
Findings
Heuristics achieve comparable quality to SAT models.
Heuristics run significantly faster than SAT models.
The approach effectively visualizes categorical spatial data.
Abstract
Point feature labeling is a classical problem in cartography and GIS that has been extensively studied for geospatial point data. At the same time, word clouds are a popular visualization tool to show the most important words in text data which has also been extended to visualize geospatial data (Buchin et al. PacificVis 2016). In this paper, we study a hybrid visualization, which combines aspects of word clouds and point labeling. In the considered setting, the input data consists of a set of points grouped into categories and our aim is to place multiple disjoint and axis-aligned rectangles, each representing a category, such that they cover points of (mostly) the same category under some natural quality constraints. In our visualization, we then place category names inside the computed rectangles to produce a labeling of the covered points which summarizes the predominant…
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