Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography and geovisualization. Generalization is not new to these areas of cartography, and has in fact always been involved in thematic geographic visualization, despite rarely being acknowledged. We illustrate this involvement with several examples of famous, public-audience thematic maps, noting the generalization procedures involved in drawing each, both across their basemap and thematic layers. We also consider, for each map example we note, which generalization operators were crucial to the formation of the map’s thematic message. The many incremental gains made by the cartographic generalization research community while treating reference data can be brought to bear on thematic cartography in the same way they were used implicitly on the well-known thematic maps we highlight here as examples.

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques.

Variable-scale maps have been advocated by several authors in the context of mobile cartography. In the literature on real-time map generalisation, however, corresponding methods that resolve cartographic conflicts by deformation of the underlying map space together with the map foreground, are underrepresented. This paper demonstrates how the concept of a malleable space can be applied as a part of the generalisation process and incorporated into the overall methodology of point generalisation. Two different algorithms are used, a density-equalising cartogram algorithm and Laplacian smoothing. Both methods work in real-time and are datadriven. In addition, they allow for a parameterisation in combi-nation with a quadtree data structure, as well as a combination with 'classic' generalisation operators (e.g. selection, aggregation, displacement) based on the quadtree. The quadtree serves both as a spatial index for fast retrieval and search of points, and as a density estimator to inform generalisation operators. The use of the quadtree as a common spatial index provides a tool to combine variable-scale maps with classic generalisation. A combination of the two allows, at small map scales, the maintenance of detail in dense areas and data reduction in sparse areas. Additionally, it facilitates building a modular workflow for real-time map generalisation.

When they make map at a given scale, the readers of the International Journal of Cartography know that they need to adjust and often simplify the level of detail of their geographic data, in order ...

The increasing volume and complexity of multimodal spatio-temporal data requires advanced approach for data exploration, integration and interpretation. This paper presents a flexible and extensible dashboard framework designed to support data-driven decision-making through interactive visualisation, exploration and analysis. The dashboard allows users to interpret structured data such as GNSS, IMU or temperature measurements as well as unstructured data types such as image files and contextual metadata. The dashboard follows the principle of combining data with reference data and display the process from raw to processed data. The concept is developed both for analysts and developers who need to identify suitable methods for analysing data and for subsequent debugging during the project. It supports analytical tasks such as pattern recognition, the integration of data sets and the validation of analysis results. By using InfluxDB for time series data management and Grafana for dynamic visualisation, the architecture ensures high scalability and responsiveness even with complex data sets. A use case from rail infrastructure monitoring shows how the dashboard facilitates data-driven insights and improves interpretability by linking multiple data perspectives. The integration of raw data, processed results and planned information enables users to align analysis results with operational objectives. This paper presents a reusable dashboard approach that improves decision-making and visual understanding of multimodal spatio-temporal data in applied cartographic and monitoring contexts.

Switzerland uses a total of 50.2 TWh to heat buildings and produces warm water in 2019, corresponding to around 80% of the energy consumption in Swiss households. Fossil heat generators account for 65% of heating energy in buildings and 53% of warm water generation (Kemmler 2020). This corresponds to 31.4 TWh per year and causes 7.5 million tons per year of CO2 emissions, with a share of 61% oil heating and 39% gas heating. With the "Energy Strategy 2050+" Switzerland commits to becoming climate neutral by 2050 (Bundesrat 2020) with the goal to no emit no more greenhouse gas, than are compensated (net zero target). One target to reach this goal is to replace fossil-based heat generators in existing buildings with heat generators based on renewable energy. In urban areas, gas networks are unlikely to be renewed and likely be shut down. However, this means that buildings previously supplied with gas will have to switch to another heat generator. If the gas network is not replaced by district heating, heat must be generated locally at the buildings. As a result, each building will have to look for its own new heat generation. Since larger heat generators are more efficient and economical than individual small heat generators, it makes sense to combine adjacent buildings into a thermal micro-grid, e.g. semi-detached houses are supplied by one heat generator. Thermal micro-grids are therefore more sustainable than individual solutions.