“GI_Forum” publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain. “GI_Forum” implements the policy of open access publication (CC-BY-ND-License) after a double-blind peer review process through a highly international team of established scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. The 2023-1 Issue comprises work of researchers from different disciplines, most of whom presented their work at the GI_Salzburg 2023 conference (https://gi-salzburg.org/en/). The articles address diverse collections of spatiotemporal data such as multispectral LIDAR, SAR or biology-related datasets, and advanced concepts, methods and tools applied for their analysis like OBIA or Google Earth Engine. Applications range from natural resources management, natural phenomena and hazards to green spaces, urban place perception, mobility, and infrastructure. The impact of ChatGPT on teaching and learning GIScience based on an anecdotal approach contributes to the overall discourse in the scientific and educational community respectively.

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure is pivotal for the promotion of walking and, subsequently, for achieving the benefits derived from its numerous positive effects. However, efficiently measuring the walkability at the street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The model incorporates eleven indicators describing the street segments and their close surroundings that are relevant for pedestrians, such as the presence and type of pedestrian infrastructure, road category, noise levels, and exposure to green and blue space. A weighted average calculation results in walkability index values for each street segment within a road network graph. The model’s generic approach and the ability to be used with open data ensure its reproducibility, adaptability, and scalability. The feasibility of the walkability model was shown using a case study for Salzburg, Austria. The model’s validity was evaluated through a large-scale study involving 660 full responses to an online survey. Participants provided ratings on the walkability of randomly selected street segments in Salzburg, which were compared with the calculated index, revealing a strong correlation (Spearman’s rank correlation = 0.82).