Anita Graser

Recent research in the field of geographic information science shows that reproducibility and replicability of publications have substantial room for improvement and proposes various actions to improve the situation. However, the impact of these actions remains unclear. This study investigates the combined effect of novel author guidelines and workflow review process, which award badges for successful reproductions, on the potential reproducibility of articles published in the AGILE conference series proceedings over the past decade. While replicating the approach of previous studies, this work expands the scope of prior reproducibility assessments and systematically compares the findings for the AGILE conference with those of the GIScience conference series proceedings, which has not undergone similar changes to guidelines or procedures. Results indicate that the reproducibility guidelines and the review process measurably improved the potential reproducibility of AGILE publications. The comparison with GIScience papers further suggests that clear and enforced guidance is a key driver for change. Our findings demonstrate the value of institutional policies and community norms in fostering reproducible research in the GIScience field and identify pathways for its ongoing improvement.

Movement research1 is a multidisciplinary field bridging geography, computer science, data science, human mobility, and movement ecology (Miller et al. 2019; Mokbel et al. 2024). It aims to underst...

Spatiotemporal machine learning models are increasingly central to geographic information science. Still, despite their growing impact, existing model documentation practices remain insufficient for capturing the spatial, temporal, and contextual dependencies that critically affect model validity, transferability, and responsible use. In this vision paper, we propose Spatiotemporal Model Cards (STMCs), a domain-agnostic extension of the Model Cards paradigm tailored to the unique characteristics of spatiotemporal models. The STMC framework integrates geographic and temporal metadata, autocorrelation-aware evaluation protocols, performance variability across space and time, transferability considerations, and ethical and sustainability aspects. Beyond model documentation, we outline how STMCs can serve as actionable interfaces within future-proof GeoAI systems, supporting model discovery, assessment, and reuse in both catalog-based and agentic (LLM-driven) settings. Using illustrative examples from the mobility domain, we demonstrate how STMCs help surface uncertainty, bias, and contextual limitations that are often obscured by aggregate performance metrics. We conclude by identifying key research challenges and directions for the GIScience community. STMCs provide a foundation for more transparent, reproducible, and responsible GeoAI practices.

Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,(iv) managing communities and fostering diversity is a necessary, on-going effort, and(v) tools for cross-language development need more attention and support.

This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed.

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)–equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-the-art, and describe open challenges for the research community in the coming years.

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.