@article{NestelSłomskaPrzechLedermannetal., author = {Nestel, Chelsea and Słomska-Przech, Katarzyna and Ledermann, Florian and Bogucka, Edyta P. and V{\"o}r{\"o}s, Fanni and Meier, Sebastian and Roth, Robert E.}, title = {Opportunities and Challenges for the Next Generation of Cartographers}, series = {The cartographic journal : The world of mapping}, volume = {61}, journal = {The cartographic journal : The world of mapping}, number = {3}, publisher = {Taylor and Francis}, address = {London}, issn = {0008-7041}, doi = {10.1080/00087041.2024.2439720}, pages = {218 -- 226}, abstract = {Here, we introduce Next Generation Cartographers, a Working Group of the International Cartographic Association (ICA) Students and early-career professionals today learn cartographic design alongside a diverse array of conceptual and technical competencies and, therefore, are working at the forefront of the discipline. However, they typically are not among the leading voices within the cartography community simply due to experience. To this end, we organized a Next Generation Cartographers (NGC) workshop at EuroCarto 2022 to identify opportunities and challenges for the next generation of cartographers. During our discussions, advocacy, peer support, mentorship, and outreach emerged as key needs of next generation cartographers and, ultimately, the need for NGC to have a voice in ICA planning. Inclusion became a core value of NGC to promote safety and diversity across the cartography community. NGC opened to global membership in August 2023 at the International Cartographic Conference in Cape Town, South Africa, and became a Working Group in autumn 2023.}, subject = {International Cartographic Association}, language = {en} } @article{CayNagelMeier, author = {{\c{C}}ay, Damla and Nagel, Till and Meier, Sebastian}, title = {The UDV Card Deck}, series = {IEEE Computer Graphics and Applications}, volume = {45}, journal = {IEEE Computer Graphics and Applications}, number = {4}, address = {New York}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {0272-1716}, doi = {10.1109/MCG.2025.3556573}, pages = {30 -- 44}, abstract = {This paper presents the Urban Data Visualization (UDV) card deck, a tool designed to facilitate reflective discussions and inform the collaborative design process of urban data visualizations. The UDV card deck was developed to bridge the gap between theoretical knowledge and practice in workshop settings, fostering inclusive and reflective approaches to visualization design. Drawing from urban visualization design literature and the results from a series of expert workshops, these cards summarize key considerations when designing urban data visualizations. The card deck guides different activities in an engaging, collaborative, and structured format, promoting inclusion of diverse urban actors. We introduce the card deck and its goals, demonstrate its use in four case studies, and discuss our findings. Feedback from workshop participants indicates that the UDV card deck can serve as a supportive and reflective tool for urban data visualization researchers, designers and practitioners.}, subject = {Fallstudie}, language = {en} } @article{SafariallahkheiliSchieweMeier, author = {Safariallahkheili, Qasem and Schiewe, Jochen and Meier, Sebastian}, title = {Post-Hoc Explanation of AI Predictions in Wildfire Risk Mapping Through an Interactive Web-Based GeoXAI System}, series = {KN - Journal of cartography and geographic information}, journal = {KN - Journal of cartography and geographic information}, publisher = {Springer International Publishing}, address = {Cham}, issn = {2524-4957}, doi = {10.1007/s42489-025-00194-0}, pages = {16}, abstract = {This case study presents an innovative approach for explaining wildfire susceptibility through a web-based Geospatial eXplainable Artificial Intelligence (GeoXAI) system. By addressing limitations in traditional GeoXAI tools, such as the lack of geographical context for model predictions and local explanation, this system integrates state-of-the-art XAI methods with open-source geospatial technologies. Applied to the wildfire-prone regions of Berlin and Brandenburg, Germany, the system combines environmental, topographic, and meteorological features derived from high-resolution geospatial data for training a Random Forest (RF) model. The web-based GeoXAI system enables interactive exploration of the model output and its features, allowing users to visualize wildfire susceptibility, examine feature contributions, and correlate predictions with spatial patterns through post-hoc interpretability. By employing post-hoc explanation methods like SHAP, the system offers clear insights into model predictions by analyzing feature contributions after training, which helps users better understand AI-driven outcomes. Designed with a user-centered approach, the platform promotes trust and usability through transparent predictions, interactive geovisualizations, and local explanations, allowing users to navigate spatial data intuitively by exploring overviews, focusing on specific regions, and accessing detailed insights on demand. This work highlights the potential of combining GeoXAI with machine learning to improve decision-making in wildfire prevention and management.}, subject = {Entscheidungsunterst{\"u}tzungssystem}, language = {en} } @article{SafariallahkheiliSchieweMeier, author = {Safariallahkheili, Qasem and Schiewe, Jochen and Meier, Sebastian}, title = {Interactive web-based Geospatial eXplainable Artificial Intelligence for AI model output exploration}, series = {AGILE : GIScience series}, volume = {6}, journal = {AGILE : GIScience series}, number = {44}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {2700-8150}, doi = {10.5194/agile-giss-6-44-2025}, pages = {7}, abstract = {This case study presents a web-based Geospatial eXplainable Artificial Intelligence (GeoXAI) system demonstrated through a case study for wildfire susceptibility assessment. Addressing limitations in traditional GeoXAI tools, the system integrates XAI methods with open-source geospatial technologies. Using a Random Forest model, the system combines environmental, topographic, and meteorological features to provide global and local insights. SHAP values offer feature-level explanations, while the interactive platform enables users to visualize wildfire susceptibility, examine feature contributions, and correlate predictions with spatial patterns and distribution of feature values. This approach tries to enhance transparency in AI-driven environmental decision support systems, with a specific focus on the interpretability of model output.}, subject = {Erkl{\"a}rbare k{\"u}nstliche Intelligenz}, language = {en} } @article{Meier, author = {Meier, Sebastian}, title = {Offene Daten und Interoperabilit{\"a}t}, series = {Vermessung Brandenburg}, volume = {29}, journal = {Vermessung Brandenburg}, number = {1.2024}, address = {Potsdam}, organization = {Ministerium des Innern und f{\"u}r Kommunales des Landes Brandenburg}, issn = {1430-7650}, pages = {22 -- 29}, abstract = {Das Thema „Offene Daten" (Open Data) gewinnt in immer mehr Anwendungsgebieten an Bedeutung. Auch in Beh{\"o}rden und der {\"o}ffentlichen Verwaltung etabliert sich zunehmend eine Open-Data-Kultur. Offene Daten aus der Verwaltung, im englischen „Open Government Data" (OGD), bergen viele Potentiale - ob es die Erleichterung der bereichs{\"u}bergreifenden Zusammenarbeit innerhalb der Verwaltung selbst ist oder das Schaffen von Transparenz gegen{\"u}ber B{\"u}rgerinnen und B{\"u}rgern. Auch auf wirtschaftlicher Ebene bieten sich neue M{\"o}glichkeiten f{\"u}r datengest{\"u}tzte Gesch{\"a}ftsfelder. Doch bei der Entwicklung neuer zivilgesellschaftlicher oder wirtschaftlicher Anwendungen basierend auf beh{\"o}rdlichen offenen Daten ergeben sich auch Herausforderungen.}, subject = {Cloud Computing}, language = {de} } @article{MeierBoguckaLedermannetal., author = {Meier, Sebastian and Bogucka, Edyta P. and Ledermann, Florian and Roth, Robert E. and Słomska-Przech, Katarzyna and V{\"o}r{\"o}s, Fanni}, title = {Opportunities and Challenges for the Next Generation of Cartographers}, series = {Abstracts of the ICA}, journal = {Abstracts of the ICA}, number = {6}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {2570-2106}, doi = {10.5194/ica-abs-6-162-2023}, pages = {1 -- 3}, abstract = {Developing the next generation of cartographers is essential to sustaining and expanding the mission of the International Cartographic Association. Students and early-career professionals today learn cartographic design alongside a diverse array of conceptual and technical competencies, such as data science, human-computer interaction, information visualization, and machine learning. Accordingly, the next generation of cartographers often are working at the forefront of the discipline, actively reshaping what cartography is and can be.}, subject = {Kartograf}, language = {en} } @inproceedings{MeierGlinka, author = {Meier, Sebastian and Glinka, Katrin}, title = {To Classify is to Interpret}, series = {Proceedings of Mensch und Computer 2023}, booktitle = {Proceedings of Mensch und Computer 2023}, editor = {Stolze, Markus and Loch, Frieder and Baldauf, Matthias and Alt, Florian and Schneegass, Christina and Kosch, Thomas and Hirzle, Teresa and Sadeghian, Shadan and Draxler, Fiona and Bektas, Kenan and Lohan, Katrin and Knierim, Pascal}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {979-8-4007-0771-1}, doi = {10.1145/3603555.3608532}, pages = {395 -- 401}, abstract = {Taxonomy building is a task that requires interpreting and classifying data within a given frame of reference, which comes to play in many areas of application that deal with knowledge and information organization. In this paper, we explore how taxonomy building can be supported with systems that integrate machine learning (ML). However, relying only on black-boxed ML-based systems to automate taxonomy building would sideline the users' expertise. We propose an approach that allows the user to iteratively take into account multiple model's outputs as part of their sensemaking process. We implemented our approach in two real-world use cases. The work is positioned in the context of HCI research that investigates the design of ML-based systems with an emphasis on enabling human-AI collaboration.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{BruhnMeierLawrence, author = {Bruhn, Thomas and Meier, Sebastian and Lawrence, Mark G.}, title = {Interactive network visualization on the integration of mindsets and sustainability : creating conditions for emergence through a relational narrative}, series = {Innovation : The European Journal of Social Science Research}, volume = {36}, journal = {Innovation : The European Journal of Social Science Research}, number = {1}, publisher = {Routledge, Taylor \& Francis Group}, address = {London}, issn = {1351-1610}, doi = {10.1080/13511610.2022.2101987}, pages = {71 -- 84}, abstract = {Transdisciplinary research processes often involve the integration of knowledge and stakeholders from various backgrounds. Here, we introduce the story of the research project AMA (A Mindset for the Anthropocene) on the role of mindsets in the context of sustainability and present an interactive visualization tool that we developed for stakeholder mapping and research communication. Through this platform, we provide access and navigation to everyone interested in this field of research and we have simultaneously created a channel for all stakeholders to co-create content. Here, we describe the design and functionalities of the platform and the participatory way it was developed as part of our stakeholder engagement. We discuss upon how such a design allows for reflection of potential biases in transdisciplinary research processes and simultaneously catalyzing self-organization in stakeholder networks.}, subject = {Stakeholder}, language = {en} } @inproceedings{Meier, author = {Meier, Sebastian}, title = {Semi-automatic spatial classification of heterogeneous spatial open government data : use case of Germany}, series = {Abstracts of the International Cartographic Association}, booktitle = {Abstracts of the International Cartographic Association}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {2570-2106}, doi = {10.5194/ica-abs-5-59-2022}, pages = {2}, abstract = {As part of the research project Open Data Cloud Services (ODCS), we have been trying to overcome some of the limitations introduced through the heterogeneity of spatial open government data (sOGD). In this paper we describe some of the challenges of sODG and one of the tools we built to spatially organize heterogenous sOGD, to make it easier for users to find data and automatically integrate it into existing data structures and in the future allow for cross-dataset spatial analysis.}, subject = {Klassifikation}, language = {en} } @inproceedings{GoodwinMeierBartrametal., author = {Goodwin, Sarah and Meier, Sebastian and Bartram, Lyn and Godwin, Alex and Nagel, Till and D{\"o}rk, Marian}, title = {Unravelling the Human Perspective and Considerations for Urban Data Visualization}, series = {2021 IEEE 14th Pacific Visualization Symposium (PacificVis)}, booktitle = {2021 IEEE 14th Pacific Visualization Symposium (PacificVis)}, publisher = {IEEE}, address = {New York}, organization = {The Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {978-1-6654-3931-2}, doi = {10.1109/PacificVis52677.2021.00024}, pages = {126 -- 130}, abstract = {Effective use of data is an essential asset to modern cities. Visualization as a tool for analysis, exploration, and communication has become a driving force in the task of unravelling our complex urban fabrics. This paper outlines the findings from a series of three workshops from 2018-2020 bringing together experts in urban data visualization with the aim of exploring multidisciplinary perspectives from the human-centric lens. Based on the rich and detailed workshop discussions identifying challenges and opportunities for urban data visualization research, we outline major human-centric themes and considerations fundamental for CityVis design and introduce a framework for an urban visualization design space.}, subject = {Individuelle Datenverarbeitung}, language = {en} }