TIN-based Tag Map Layout

ABSTRACT

With the widespread use of tag clouds, multiple map-based variations have been proposed. Like standard tag clouds (also called word clouds), these ‘tag maps’ all share the basic strategy of displaying words within a ‘geographic space’ and scaling the word size to depict frequency (or importance) of those words within some dataset. While some tag maps simply plot a standard tag cloud on top of a map, the subset of tag maps we focus on here are those in which the collection of words are displayed within bounded geographic regions (often of irregular shape) that the words are relevant for. For this form of tag map, map scale and polygon shape add constraints to word size and position that have not been considered in most prior approaches to tag map word layout. In this paper, we present a layout strategy for tag map generation that includes consideration of the shape and size of the geographical regions acting as containers for the tags. The method introduced here uses a triangulated irregular network (TIN) to subdivide the geographical region into many triangle subareas, with the centroid of each triangle being a potential location to centre a tag on. All the triangles are sorted by their area and all the tags are sorted by their weight value (e.g. frequency, importance or popularity). Positioning of tags is undertaken sequentially from most important (or frequent or popular) with potential locations being the TIN triangle centroids (tried from largest to smallest triangle). After each tag placement, the TIN is recalculated to integrate the tag centroid and bounding corners into the TIN creation. The limited whitespace in the geographical region, at any specific scale, is used fully by dynamically adjusting the font size along with the number and the direction of tags. The method can be applied to add tags within geographic polygons that are convex, concave and other more complex regions containing holes or islands.

KEYWORDS:

• Tag cloud
• text layout
• triangulated irregular network
• map visualization
• cartography

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributor

Nai Yang is an Associate Professor in the School of Information Engineering, China University of Geosciences (Wuhan). He is also a visiting faculty in the GeoVISTA Center, Department of Geography, Pennsylvania State University. His research focuses on geovisualization, spatial analysis, and GIS applications.

ORCID

Nai Yang http://orcid.org/0000-0001-5306-1163

Alan M. MacEachren http://orcid.org/0000-0002-0356-7323

Additional information

Funding

This work was supported by the State Scholarship Fund from the China Scholarship Council (CSC) [201706415027]; the Fundamental Research Founds for National University, China University of Geosciences(Wuhan)[G1323541712]; and the Geomatics Technology and Application key Laboratory of Qinghai Province [QDXS-2017-02].