Abstract. Urban vitality captures the dynamic and interactive nature of city environments by highlighting how residents engage with public spaces, making it essential for differentiating neighborhoods. Traditional indicators focused on static measures, such as density, land-use diversity, and built environment design. Most of these measures fail to capture the dynamic nature of vitality. This paper introduces the concept of Mobility Vitality, a novel measure that captures the dynamic and vibrant nature of human activities through the analysis of active and micro-mobility modes, including biking, e-scootering, and recreational running. Taking Washington, D.C. as a case study, we analyze the spatiotemporal patterns of mobility across different modes and time periods, revealing significant variations in mobility patterns between the downtown core and peripheral areas. The results also indicate that the most unique time series of the three micro-mobility modes are weekend mornings and weekday nights, and fluctuations are more pronounced within a day than between weekdays and weekends. The proposed analysis framework may guide infrastructure investments, optimize urban transport networks, and advance more equitable and sustainable cities.
Abstract. Since its launch in 2014, openSenseMap has become a widely used platform for citizen science and environmental sensing. Over the past decade, it has enabled real-time data collection, visualization, and analysis through an open sensor network. This paper reflects on its evolution, technical advancements, and impact on geospatial research. We discuss challenges in managing an open geospatial infrastructure at scale and introduce the next-generation openSenseMap, which enhances data quality, interoperability, and usability. By sharing lessons learned and future directions, we underscore its role in participatory environmental monitoring and geographic information science.
Abstract. This paper introduces MAP-VERSE (MAP Usability - Validated Empirical Research by Systematic Evaluation), an open metadata repository designed to address the lack of accessible and consistently reported datasets in map usability research. Currently, researchers face challenges in discovering and reusing relevant data due to the absence of centralized platforms and standardized reporting practices. This hinders reproducibility, comparative analyses, and the development of predictive models for map usability. MAP-VERSE tackles these challenges by establishing core metadata dimensions for describing map usability datasets; defining validity criteria for best practice datasets to ensure methodological rigor and transparency; developing a user-friendly search interface for efficient data discovery; and proposing strategies to encourage standardized metadata reporting. The repository currently features curated metadata for nine existing open-access datasets encompassing various map types, tasks, and methods. Future work includes expanding the repository, implementing automated validation, and promoting standardized reporting practices through templates and guidelines. MAP-VERSE aims to foster open data sharing and collaboration, ultimately advancing our understanding of human-map interaction and improving map design.
Abstract. On-street parking significantly impacts urban traffic flow. Existing research has primarily focused on pricing policies as a method to mitigate the congestion caused by parking. However, the specific influence of spatial parking configurations on through traffic remains underexplored. This study investigates how the placement of on-street parking spaces affects through traffic, using microscopic simulation to analyse travel time disruptions across a range of scenarios. A simulated urban road network serves as the test environment, allowing for systematic variations in traffic and parking demand at all the potential parking locations. Results indicate that certain parking locations cause significant disruptions to through traffic, with increased delays correlating with higher through and local traffic volumes. Notably, spillover effects from cruising for parking cause delays even at locations away from primary through traffic routes. In contrast, some parking locations near throughways had minimal impact due to available alternative routes. These findings highlight that parking placement impacts are shaped by the interaction of traffic flow and parking demand rather than proximity alone. The results underscore the need for strategic parking placement in urban areas to minimize disruptions to through traffic, suggesting that parking management strategies could reduce adverse impacts on urban mobility.
Abstract. Proximity plays an important role in Geographic Information Sciences. It underpins our understanding of spatial dependence and spatial structure, and is a key component of many commonly used analytical techniques. Despite this, it remains a difficult concept to rigorously define. Describing one geospatial object as "near" another implies much more than a simple geometric relationship - with factors such as accessibility, utility and function also playing an important role. Previous work has shed light on these relationships through the application of sophisticated mathematical models which attempt to encapsulate both spatial and non-spatial aspects of proximity. In this paper, we present a novel method that uses Large Language Models (LLMs) to extract perceived proximity relationships from natural language. Using 20000 AirBnB listings in London, we identify locations which are described as "near" to each property and analyse their spatial distribution. Our results reveal complex patterns linking perceived proximity to accessibility, utilisation, and administrative prominence. We show that locations with a broader area of influence often correspond to higher transit connectivity or higher place-level categories. While the Airbnb dataset reflects a specific, tourism-focused demographic, the approach is generalisable to other sources of user-generated text. This work demonstrates how LLMs can support data-driven spatial analysis by surfacing nuanced, context-sensitive geospatial relationships embedded in everyday language.
