Abstract
Automatic point-feature label placement (PFLP) is a fundamental task for map visualization. As the dominant solutions to the PFLP problem, fixed-position and slider models have been widely studied in previous research. However, the candidate labels generated with these models are set to certain fixed positions or a specified track line for sliding. Thus, the whole surrounding space of a point feature is not sufficiently used for labeling. Hence, this paper proposes a novel label model based on the region of movability, which comes from plane collision detection theory. The model defines a complete conflict-free search space for label placement. On the premise of no conflict with the point, line, and area features, the proposed model utilizes the surrounding zone of the point feature to generate candidate label positions. By combining with heuristic search method, the model achieves high-quality label placement. In addition, the flexibility of the proposed model enables placing arbitrarily shaped labels.
Highlights
Labeling graphical objects is an important issue in several fields, including cartography, geographic information systems, and information visualization
In this paper, according to the principle of the region of movability, the conflict-free potential space is obtained as the basis for the further point-feature label placement concerning various background features
More natural than the fixed-position model or slider model, the key point of our model is that the surrounding zone of a point feature is utilized for label placement
Summary
Labeling graphical objects is an important issue in several fields, including cartography, geographic information systems, and information visualization. To preserve the quality of the visual representation, a label should not overlap with other labels or features [1]. Two sub-problems, label number maximization and label size maximization, are defined [2]. Non-overlapping labeling is classified as NP-hard [3,4,5]. The label placement is usually divided into three tasks: labeling points, labeling lines, and labeling areas. Because labeling points is a more fundamental problem, point-feature label placement (PFLP) has been extensively studied in recent years [6,7,8,9]
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