A proliferation of web-based maps have appeared depicting many different aspects of the spread of the novel coronavirus (SARS-CoV-2). In this commentary, we consider the usage of web-based mapping during the COVID-19 pandemic and argue that web maps have been widely misused for delivering public information on this fast moving, epidemiologically complex, and geographically unbounded process.

This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT's performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT's responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges of LLMs in spatially-oriented fields. We conduct our evaluation with two models, GPT-3.5 and GPT-4, to understand whether general improvements of an LLM translate to improvements in answering questions related to the spatial domain. We find that both GPT variants can pass a balanced, introductory GIS exam, scoring 63.3% (GPT-3.5) and 88.3% (GPT-4), which correspond to grades D and B+ respectively in standard US letter grading scale. In addition, we also identify specific questions and topics where the LLMs struggle to grasp spatial concepts, highlighting the challenges in teaching such topics to these models. Finally, we assess ChatGPT's performance in specific aspects of GIS, including spatial analysis, basic concepts of mapping, and data management. This granular analysis provides further insights into the strengths and weaknesses of ChatGPT's GIS literacy. This research contributes to the ongoing dialogue on the integration of AI models in education and can provide guidance for educators, researchers, and practitioners seeking to leverage LLMs in GIS. By focusing on specific questions or concepts that pose difficulties for the LLM, this study addresses the nuances of teaching spatial concepts to AI models and offers potential avenues for improvement in spatial literacy within future iterations of LLMs.

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approach.