Exploring Semi-Automatic Map Labeling

Klute, Fabian; Li, Guangping; Löffler, Raphael; Nöllenburg, Martin; Schmidt, Manuela

doi:10.1145/3347146.3359359

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…From a practical perspective, this identification would save much labor time since cartographers would not be required to manually inspect all labels from the automated solution (see, e.g., Klute et al (2019) for a practical implementation of semi-automated map labeling). Analytical evaluation of map labeling was studied by van Dijk (2002) who quantified several map labeling rules to form a label quality function used for evaluation (for a practical use of a similar framework, see Kern and Brewer (2008)).…”

Section: Rule-based Techniquesmentioning

confidence: 99%

Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions

Harrie

Oucheikh

Nilsson³

et al. 2022

J geovis spat anal

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Map label placement is an important task in map production, which needs to be automated since it is tedious and requires a significant amount of manual work. In this paper, we identify five cartographic labeling situations that present challenges by causing intensive manual work in map production of city wayfinding maps, e.g., label placement in high density areas, utilizing true label geometries in automated methods, and creating a good relationship between text labels and icons. We evaluate these challenges in an open source map labeling tool (QGIS), provide results from a preliminary study, and discuss if there are other techniques that could be applicable to solving these challenges. These techniques are based on quantified cartographic rules or on machine learning. We focus on deep learning for which we provide several examples of techniques from other application domains that might have a potential in map label placement. The aim of the paper is to explore those techniques and to recommend future practical studies for each of the identified five challenges in map production. We believe that targeting the revealed challenges using the proposed solutions will significantly raise the automation level for producing city wayfinding maps, thus, having a real, measurable impact on production time and costs.

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Section: Rule-based Techniquesmentioning

confidence: 99%

Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions

Harrie

Oucheikh

Nilsson³

et al. 2022

J geovis spat anal

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“…Recently, Klute et al [24] proposed an exact MaxSAT model for the point feature labeling problem. They achieved reasonable running time for real-world cartographic applications.…”

Section: Exact Solver Based On Maxsatmentioning

confidence: 99%

Worbel: Aggregating Point Labels into Word Clouds

Bhore,

Ganian,

et al. 2021

Preprint

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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 categories globally (in a word-cloud-like fashion) while locally avoiding excessive misrepresentation of points (i.e., retaining the precision of point labeling).We show that computing a minimum set of such rectangles is NP-hard. Hence, we turn our attention to developing heuristics and exact SAT models to compute our visualizations. We evaluate our algorithms quantitatively, measuring running time and quality of the produced solutions, on several artificial and real-world data sets. Our experiments show that the heuristics produce solutions of comparable quality to the SAT models while running much faster. ACM Subject ClassificationHuman-centered computing → Geographic visualization; Theory of computation → Computational geometry; Human-centered computing → Empirical studies in visualization Keywords and phrases labeling, word clouds, categorical point data Supplementary Material Source code, benchmark data generator and real-world data extractor at https://dyna-mis.github.io/geoWordle/.

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Visual preferences in map label placement

Scheuerman,

Harman,

Goldstein

et al. 2023

Discov Psychol

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Digital maps are important for many decision-making tasks that require situational awareness, navigation, or location-specific data. Often, digital mapping tools must generate a map that displays labels near associated features in a visually appealing manner, without occluding important information. Automated label placement systems generally accomplish this nontrivial task through a combination of heuristic algorithms and cartography rules, but the resulting maps often do not reflect the preferences and needs of the map user. To achieve higher quality map views, research is needed to identify cognitive and computational approaches for generating high-quality maps that meet user needs and expectations. In this paper, we present a study that explores the visual preferences of map users and supports the development of a preference model for digital map displays. In particular, we found that participants demonstrated consistent preferences for how labels are placed near their point of interest, and that they were more likely to choose positions that prioritized alignment over distance when ranking labels that made trade-offs between them.

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Exploring Semi-Automatic Map Labeling

Cited by 7 publications

References 26 publications

Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions

Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions

Worbel: Aggregating Point Labels into Word Clouds

Visual preferences in map label placement

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