Map labels play an important role in helping users interpret geographic information. Traditional label placement algorithms are typically designed for static or fixed-scale maps, where labels appear at discrete, predefined zoom levels. However, vario-scale maps change content continuously during zooming, requiring labels to also transition smoothly to maintain readability and visual coherence.
This research explores how to place and adjust labels on vario-scale maps, ensuring legibility and usability throughout scale transitions. Due to limited existing work in this area, a set of requirements, both hard and soft, are defined to guide the methodology and evaluate results.
Different strategies are applied based on geometry types. For elongated features such as roads and rivers, label anchor points and orientations are derived from the medial axis (skeleton). For compact geometries such as buildings, the centroid and pole of inaccessibility are used to determine anchor points, while the minimum rotated bounding rectangle guides label rotation.
Two polygonization methods are developed using the tGAP structure: slice-based method extracts geometry at fixed scales and computes label positions per feature type; event-based method reacts to changes in geometry, computing label anchors whenever a change occurs. Label anchor points could be represented as continuous trajectories, supporting smooth interpolation. During interactive visualization, label positions, orientations, and opacities are interpolated across scales. To prevent clutter, real-time collision detection is implemented.
The methodology was applied to the TOP10NL dataset in the Delft region. Results showed that the event-based method allows more precise responsiveness to geometry changes but can produce occasional abrupt label movements. In contrast, the slice-based method offers more stable transitions but may cause unnecessary label shifts even when geometries do not change. Event-based labeling also resulted in a higher number of anchor points. While hard requirements were generally satisfied, some soft requirements were not fully met due to intentional trade-offs or implementation limitations.
Overall, this research introduces a novel framework for integrating dynamic labeling into vario-scale maps using the tGAP structure and demonstrates its feasibility through an interactive, real-time prototype with smooth label transitions and collision handling.

By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.

To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.

The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.