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In cartography, Geographic Information Systems (GIS), and visualization, the position of a label relative to its point feature is crucial for readability and user experience. Alongside other factors, the point-feature label placement (PFLP) is typically governed by the Position Priority Order (PPO), a systematic raking of potential label positions around a point feature according to predetermined priorities. While there is a broad consensus on factors such as avoiding label conflicts and ensuring clear label-to-feature associations, there is no agreement on PPO. Most PFLP techniques rely on traditional PPOs grounded in typographic and cartographic conventions established decades ago, which may no longer meet today's user expectations. In contrast, commercial products like Google Maps and Mapbox use non-traditional PPOs for unreported reasons. Our extensive user study introduces the Perceptual Position Priority Order (PerceptPPO), a user-validated PPO that significantly departs from traditional conventions. A key finding is that labels placed above point features are significantly preferred by users, contrary to the conventional top-right position. We also conducted a supplementary study on the preferred label density, an area scarcely explored in prior research. Finally, we performed a comparative user study assessing the perceived quality of PerceptPPO over existing PPOs, advocating its adoption in cartographic and GIS applications, as well as in other types of visualizations. Our research, supported by nearly 800 participants from 48 countries and over 45,500 pairwise comparisons, offers practical guidance for designers and application developers aiming to optimize user engagement and comprehension, paving the way for more intuitive and accessible visualizations.

Over the recent years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains, including robotics, self-driving cars, and finance. In this paper, we are introducing Reinforcement Learning (RL) to label placement, a complex task in data visualization that seeks optimal positioning for labels to avoid overlap and ensure legibility. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts. To facilitate RL learning, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualization. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than the compared methods. Nevertheless, our method is ideal for scenarios where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation by the participants.