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Choropleth map animation is widely used to show the development of spatial processes over time. Although animation congruently depicts change, the rapid succession of complex map scenes easily exceeds the human cognitive capacity, causing map users to miss important information. Hence, a reduction of the visual complexity of map animations is desirable. This article builds on research related to complexity reduction of static choropleth maps. It proposes value generalization of choropleth time-series data in space and time, by using a method that adapts to the degree of global spatiotemporal autocorrelation within the dataset. A combination with upstream algorithms for local outlier detection results in less complex map animations focusing on large-scale patterns while still preserving significant local deviations in space and time. An according software application allows for in-depth exploration of the spatial and temporal autocorrelation structures in time-series data and provides control over the whole process of generalization.

The Body of Knowledge for Geographic Information Science and Technology (GIS&T BoK) has been the main reference document for curriculum design in the geospatial domain. Today, the BoK is supposed to have fallen short in adequately covering the domain due to significant conceptual and technological advances in the field. Thus, several initiatives around the globe work towards an update of the GIS&T BoK. This research assesses the demands of today's GIS&T workforce across Europe to contribute to the effort of a demand‐oriented update. We assessed the workforce demand by means of a Europe‐wide distributed online questionnaire and complementary expert interviews. The results show that the BoK still is a comprehensive reference base for the geospatial domain that is generally deemed relevant by the European workforce. However, workforce demands point to three main topics that need to be addressed by an update of the BoK: (1) the shift from primary data acquisition to the handling of highly abundant spatial data; (2) a lack of competences in programming and application development; and (3) a poor coverage of web‐related aspects. Future research should complement workforce demands with a review of the scientific literature to identify additional shortcomings related to conceptual advances.

Natural language processing systems like ChatGPT have recently attracted enormous attention in the field of higher education. We aim to contribute to this discussion by scrutinizing the suitability of current testing methods and potentially necessary shifts in learning objectives in GIScience. This paper presents an anecdotal approach to the impact of ChatGPT on teaching and learning based on a real-world use case. It focuses on the results of a fictional student who used ChatGPT for the completion of applicationdevelopment assignments, including coding. The solutions were submitted to the instructor, who assessed the results in a single-blind experiment. The instructor's feedback and grading as well as the AI-plagiarism results were part of our evaluation of the testing methods applied. This triggered a discussion on the adequacy of current learning objectives in the development of GIS applications and the integration of AI into the learning process.