Geo word clouds

Buchin, Kevin; Creemers, Daan H. M.; Lazzarotto, Andrea; Speckmann, Bettina; Wulms, Jules

doi:10.1109/pacificvis.2016.7465262

Cited by 22 publications

(5 citation statements)

References 15 publications

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“…Two kinds of spatial information have been integrated into word clouds: geo-spatial position of each word within a cloud and artistic shape creation. Buchin et al [9] design geo word clouds that place words to respect not only the word frequency but also the relative positions between words. Since the spatial relations between words are preserved, such clouds can form shapes of geographic regions.…”

Section: Related Work 21 Word Cloud Visualizationmentioning

confidence: 99%

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

Wang

Lee

Chu

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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We present a new technique to enable the creation of shape-bounded Wordles, we call ShapeWordle, in which we fit words to form a given shape. To guide word placement within a shape, we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape. To handle non-convex shapes, we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements. In addition, we offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles. Lastly, we present three evaluations: a comprehensive comparison of our results against the state-of-the-art technique (WordArt), case studies with 14 users, and a gallery to showcase the coverage of our technique.

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Section: Related Work 21 Word Cloud Visualizationmentioning

confidence: 99%

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

Wang

Lee

Chu

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…Large geo-referenced data sets often contain different tags for the same location, and tag map layout algorithms decide on aggregations and omissions. Tag-cloud-driven tag maps [53] collect information for entire polygonal regions and place the tags regardless of actual geographical locations within that polygon applying a tag placement strategy adopted from tag cloud algorithms [54], [55], [56]. Location-driven tag maps place tags dependent on the actual geographical distribution of corresponding data items.…”

Section: Tag Mapsmentioning

confidence: 99%

Modeling How Humans Judge Dot-Label Relations in Point Cloud Visualizations

Reckziegel

Pfeiffer

Heine

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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When point clouds are labeled in information visualization applications, sophisticated guidelines as in cartography do not yet exist. Existing naive strategies may mislead as to which points belong to which label. To inform improved strategies, we studied factors influencing this phenomenon. We derived a class of labeled point cloud representations from existing applications and we defined different models predicting how humans interpret such complex representations, focusing on their geometric properties. We conducted an empirical study, in which participants had to relate dots to labels in order to evaluate how well our models predict. Our results indicate that presence of point clusters, label size, and angle to the label have an effect on participants' judgment as well as that the distance measure types considered perform differently discouraging the use of label centers as reference points.

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“…Classic word clouds are often generated using forced-based approaches, alongside with a spiral placement heuristic [14][15][16] that allows for a very compact final layout. This method is powerful even when the rough position of a word is dictated by an underlying map [4,12]. Semantic word clouds on the other hand have been approached with many different techniques, e.g., force directed [6], seam-carving [17], and multidimensional scaling [2].…”

Section: Introductionmentioning

confidence: 99%

Layered Area-Proportional Rectangle Contact Representations

Nöllenburg¹,

Villedieu²,

Wulms³

2021

Preprint

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We investigate two optimization problems on area-proportional rectangle contact representations for layered, embedded planar graphs. The vertices are represented as interior-disjoint unit-height rectangles of prescribed widths, grouped in one row per layer, and each edge is ideally realized as a rectangle contact of positive length. Such rectangle contact representations find applications in semantic word or tag cloud visualizations, where a collection of words is displayed such that pairs of semantically related words are close to each other. In this paper, we want to maximize the number of realized rectangle contacts or minimize the overall area of the rectangle contact representation, while avoiding any false adjacencies. We present a network flow model for area minimization, a linear-time algorithm for contact maximization of two-layer graphs, and an ILP model for maximizing contacts of k-layer graphs.

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Geo word clouds

Cited by 22 publications

References 15 publications

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

Modeling How Humans Judge Dot-Label Relations in Point Cloud Visualizations

Layered Area-Proportional Rectangle Contact Representations

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