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.
Abstract. Over the past two decades, Portugal has been severely affected by large-scale forest fires. In response, the Portuguese government launched a programme aimed at transforming the landscape to make it more resilient to wildfires and economically sustainable. The process of creating new land-use proposals, however, remains highly complex, involving multiple stakeholders and error-prone workflows reliant on disconnected geographic information systems (GIS) and spreadsheets. This paper presents LAND IT, a web-based spatial decision support system (SDSS) designed to optimise OIGP (Integrated Landscape Management Operations) creation process. An OIGP is a management model that define a new landscape proposal. Unlike conventional GIS software, LAND IT provides a structured framework that streamlines version control, scenario comparison, and stakeholder collaboration. The system enables users to create and manage multiple proposals iteratively, preserving decision-making knowledge and minimising costly errors. It also integrates essential geospatial datasets, automates dependency management, and enhances data-driven decision-making. Mação, a wildfire-prone municipality, was selected as the pilot case study due to its strategic role in implementing nine OIGP. The results were approved by Mação’s stakeholders as they demonstrate significant improvements in efficiency, transparency, and the ability to compare alternative landscape transformation strategies. While LAND IT is currently tailored to Mação, its scalable architecture allows for future expansion to other municipalities. LAND IT represents a significant step towards more resilient and sustainable territorial management, ultimately contributing to wildfire mitigation and long-term environmental planning.
Abstract. This study addresses the challenge of evaluating Singapore’s long-term urban strategy by quantifying the impact of planning regulations, a task often hampered by fragmented data and siloed tools. To overcome these limitations, we developed a data-driven workflow using Semantic Web Technologies (SWT). Central to this workflow are two ontologies: OntoPlanningRegulations, which captures a subset of Singapore’s planning rules, and OntoBuildableSpace, which defines measurable 3D spaces within urban plots. These ontologies integrate diverse regulatory data into a structured Knowledge Graph (KG), connecting regulations to 3D urban models. This approach bridges document-based urban policies and advanced urban analytics, offering an automated methodology to generate 3D master plans. In doing so, it provides valuable information on the cumulative impacts of regulations on the future urban form of the city.
Abstract. Already more than half of the global population is living in urban areas today. Rapid urbanisation during the past decades has brought about a significant loss of green space in the built environment across the globe. To mitigate the loss of horizontal green space, vertical façade greenery has been promoted as one promising way especially in increasingly densifying and expanding urban areas. Façade greening has also been promoted to strengthen (mental) health and wellbeing of the ever-increasing urban population in the expanding built environments. To investigate the contribution of façade greening to the wellbeing of urban citizens, we set out to empirically study the influence of green building façades, experienced in virtual reality, on human emotion using self-reports and psycho-physiological measurements. As hypothesized by prior work, we indeed find that participants feel less aroused and thus more relaxed in virtual urban environments experienced with green building façades compared to regular building façades without any greenery. With these promising empirically validated results we shed new light on mental health benefits of vertical greenery in urban environments.
Abstract. This paper describes a two stage approach for identifying neighbourhood areas that may be undergoing gentrification related changes. It summarises classic hedonic house price data over time (2014–2023) for each neighbourhood, and compares neighbourhood average price with those of local nearby areas. This enables neighbourhoods experiencing high relative increases in price to be identified as potentially gentrifying areas. Social media data for these areas were extracted and analysed using a large language model which scored each individual social media post by the degree to which their content indicated that the neighbourhood is experiencing change, potentially providing confirmatory evidence or not of gentrification. A number of areas of further work are identified.
Abstract. In view of climate change driving extreme events such as floods, assessing urban infrastructure resilience is critical for disaster response and urban planning. We investigated how flooding in Rio Grande do Sul affected road network connectivity and urban resilience in terms of lack of redundancy to healthcare facilities. We performed centrality analysis using the edge betweenness indicator to identify urban arteries critical for connectivity at metropolitan and intracity scales and compared alternative routes to assess healthcare facilities resilience. Understanding how floods disrupt road connectivity and mobility is critical for identifying vulnerable areas and improving disaster response planning. The results revealed that just 71 km—or 2% of the total analysed network—accounted for 12% of core-metropolitan connectivity prior to the floods. These high-centrality urban arteries, including the BR-290 Freeway, were disproportionately affected: they lost approximately 92% of their total centrality after the flooding. Overall, the road network experienced a 59% reduction in betweenness centrality at the core metropolitan scale. At the municipal level, impacts varied. For example, Canoas experienced a 59% loss in intracity connectivity, while Nova Santa lost only 14%, despite a larger flooded area (113 km2 versus 65 km2). Regarding the analysis of urban resilience to access healthcare facilities, the results revealed higher deficits in peripheral hospitals, such as Hospital Restinga e Extremo Sul, indicating a lower resilience. These results indicate the importance of multi-scale analyses to reveal spatial disparities and inform disaster risk management. This study provides actionable insights to support decision-makers in improving emergency responses and strengthening infrastructure resilience to future climate-related disasters.
Abstract. The evaluation of bike networks is important for improving cycling infrastructure and supporting sustainable travel behaviors. While bikeability indexes are widely used, current methods typically rely on straightforward metric aggregation, often overlooking nuanced interactions between evaluation elements. In this study, we introduce a novel approach to integrate a Knowledge Graph (KG) of bikeability evaluation studies with the Analytic Network Process (ANP), a decision modeling technique. The KG, which comprises more than 270 bikeability metrics and 41 qualitative criteria, provides a structured foundation for index development, reflecting the trends in existing evaluation approaches. ANP enhances this framework by capturing the interdependencies in the use of qualitative criteria and quantitative metrics, ensuring a more rigorous and transparent aggregation process. As a case study, we apply this methodology to Zurich’s road network and evaluate network bikeability at the segment level. Further, we conduct a sensitivity analysis of how changes in the KG structure impact the network evaluation results. By treating bikeability index development as a decision-making task, our study strengthens the methodological foundation of bikeability index design. Future research will scale this framework to larger networks, extend the sensitivity analysis, and benchmark our approach against established bikeability indexes.
Abstract. The evaluation of rainfall-induced soil erosion risk is fundamental for territorial planning and takes into account parameters such as rainfall erosivity, soil erodibility and the topographic factor. The Triangular Irregular Network (TIN) is the most frequently used interpolator in the production of digital elevation models (DEM) but is considered unsuitable by several authors for the calculation of soil erosion. Therefore, the DEM created for the city of Torres Novas, Portugal, using interpolation methods such as Inverse Distance Weighting, Ordinary Kriging, and Empirical Bayesian Kriging (EBK) were evaluated to determine which one was the most accurate. The best interpolator was EBK, from which a rainfall-induced soil erosion map was created. A map was also produced from TIN and both were compared with historical cartography. The EBK method was found to be the most effective interpolator for rainfall-induced soil erosion as well. Therefore, the authors recommend its use in future studies in the municipality of Torres Novas.
Abstract. Information Retrieval is a set of techniques related to identifying and selecting documents from a very large collection of candidate documents based on their content. Traditionally, information retrieval is based on text documents and terms and various techniques for ranking the relevance of terms in documents. As an extension and to simplify the interaction of a user, however, techniques have been added enabling facet search. In this case, a search based on keywords or phrases is conducted. While doing this step, statistics on very specific low-rank properties of the documents are collected, e.g., price range, user ratings, color, manufacturer. This is then presented to the user together with search results in order to allow the user to filter or refine the search with respect to these queries. In this paper, we ask the question how meaningful facets can be computed for spatial databases and how this can be used to explore spatio-textual datasets exploiting such facets as an intuitive yet powerful information discovery mechanism beyond semantic categories. We show the feasibility of this approach on synthetic datasets, OpenStreetMap data, Wikipedia data, and social media data.
Abstract. This study investigated the geospatial patterns and determinants of violence against children and young people in Namibia using data from 5,191 individuals aged 13–24 years, interviewed across 79 constituencies in the 2019 Violence Against Children and Youth Survey. We employed Global Moran’s I and Local Indicators of Spatial Autocorrelation to identify global spatial autocorrelation and hotspots. Ordinary least squares regression was used to identify significant area-level risk factors, whereas Geographically Weighted Regression was used to model local variations. We found significant regional variations in the prevalence of past-year sexual(SV), physical (PV), and emotional violence (EV), with the highest rates observed in constituencies in central and northern Namibia. Global spatial autocorrelation was evident for SV and EV but not for PV, suggesting distinct spatial patterns for each form of violence in the study area.Our findings also revealed notable spatial variations in risk factors, with area-level factors showing strong influences in certain areas, while being less impactful or negligible in others. These findings emphasise the need for geographically targeted interventions and policies to address localised risk factors and reduce violence against children and young people in Namibia.
Abstract. Accessibility studies often focus on the general population, overlooking individual differences in mobility capacities and the role of external factors. Microscale street elements such as stairs, high kerbs, and sidewalk cracks can significantly impact urban mobility for individuals with restricted movement capacities. This study introduces a workflow to integrate different detailed accessibility information, such as barriers and facilitators for pedestrian mobility, with sidewalk data to create an enriched pedestrian network. Using this network, we evaluate each segment by computing an impedance score to quantify accessibility. Furthermore, we demonstrate how the network can be tailored to individual mobility needs and highlight its potential as a decision-making tool for urban planners and civil engineers to identify and prioritise targeted interventions for vulnerable populations.
Abstract. Research in environmental psychology and safety science suggests that time of day largely impacts pedestrian spatial decisions in cities, influencing where and how people move. The cognition of the surrounding environment is altered by darkness, perception of safety, and the appeal of different routes. In this work, we present an Agent-Based Model (ABM) of pedestrian movement in cities that incorporates time-sensitive elements into the agents’ route choice behavioural mechanisms by considering urban form, nighttime potential vulnerability, and the impact of street lighting. Our preliminary findings indicate that nighttime pedestrian movements differ from daily patterns. Volumes of pedestrian agents moving after sunset tend to converge along the well-known minor roads in the historical city centre. Conversely, high concentrations along rivers and in proximity to parks and green areas, typical of daily movement flows, do not surface during the night. The ABM, although at an initial development stage and built on the basis of a few behavioural mechanisms, constitutes a promising tool to advance knowledge on route choice behaviour and, after adequate calibration and validation, may emerge as a potential tool to support policy making.
Abstract. This study investigates the key factors influencing public transport mode shares in Uyo Urban area, Akwa Ibom State, Nigeria, with a focus on understanding the role of the built environment factors on public transport mode shares. The study employed a mixed-method approach, utilizing a structured survey for data collection, capturing key variables such as population density, transit supply, road network density, and commuter preferences. Data were analyzed using Principal Component Analysis (PCA), Multiple Linear Regression, and Geographically Weighted Regression (GWR) to explore spatial and non-spatial relationships between the identified factors and public transport usage. The PCA revealed three key components influencing public transport mode share: accessibility and infrastructure quality (39.36%), environmental constraints (15.15%), and mobility and Travel behaviour (13.15%). Regression analysis indicated that environmental constraints were the most significant predictor, followed by mobility and travel behaviour and accessibility and infrastructure quality. The GWR analysis highlighted spatial heterogeneity, showing that the impact of these factors varied across different neighbourhoods within Uyo urban. The findings suggest that improving infrastructure, addressing environmental constraints, and aligning public transport systems with commuter behaviours are crucial for increasing public transport usage. Policy recommendations include enhancing transit supply, improving road connectivity, mitigating environmental challenges, and tailoring services to commuter needs. The study underscores the importance of a multi-faceted approach to public transport planning that considers both infrastructure and socio-environmental factors to improve public transport adoption and sustainability in Uyo Urban and similar urban contexts.
Abstract. The rapid emergence of shared electric scooter (e-scooter) services has posed new challenges to road safety over the last few years as a serious worldwide public concern. Previous studies have investigated e-scooter accidents from multiple perspectives. However, research gaps still exist in understanding the role of infrastructure-related factors in e-scooter accidents. This study aims to investigate and model the relationship between the characteristics of traffic infrastructure and the presence of e-scooter accidents, especially the presence of curbs and the complexity of street views. Curb extraction was first achieved by applying the Segment Anything Model to Google Street View images as a supplement to existing traffic infrastructure data. Variables related to curbs, the complexity of street views, and traffic transport were then constructed. With pseudo-absence points being generated, the influence of traffic infrastructure on the presence of accidents was analysed by applying logistic regression and Random Forest classification. Results show that bicycle traffic count, distance to road, and object complexity of the whole scene are the most important variables in the classification model, while besides these three, the presence of curbs, distance to pedestrian crossing, speed limit value, urban district, and road width are significantly correlated with the presence of e-scooter accidents.
Abstract. Hexagonal Discrete Global Grid Systems (DGGS) offer significant advantages for spatial analysis due to their uniform cell shapes and efficient indexing. Among the three central place apertures (3, 4, and 7), aperture 7 subdivisions exhibit very desirable properties, including the preservation of hexagonal symmetry and the formation of unambiguous indexing hierarchies. Interest in hierarchically indexed aperture 7 hexagonal DGGS has recently increased due to the popularity of the H3 DGGS. But there are currently no open-source equal-area aperture 7 hexagonal DGGS available, that provide similar indexing capabilities like H3. We present IGEO7, a novel pure aperture 7 hexagonal DGGS, and Z7, its associated hierarchical integer indexing system. In contrast to H3, where cell sizes vary by up to ±50% across the globe, IGEO7 uses cells of equal area, making it a true equal-area DGGS. IGEO7 and Z7 are implemented in the open-source software DGGRID. We also present a use case for on-demand suitability modeling to demonstrate a practical application of this new DGGS.
Abstract. 3D evaluation of indoor environments is important for first responders and their commanders. Both are unfamiliar with the situation that they will find. While doing their work they can scan the environment they are working in and process that data in real time. To be able to do this efficiently both the scanned areas and the unscanned parts need to be assessed giving indications where to go to next. In this research an approach to identify, classify and visualize unscanned areas within the building is proposed. Using RGB and Depth (RGB-D) cameras and Collaborative Visual SLAM a pointcloud of the interior of the building is generated. By combining the pointcloud with the building’s outer boundary it is possible to evaluate which areas have been visited by the responders, which have not and identify potential hidden spaces. The proposed solution implements that by employing voxels. The result can be seen on the online 3D web-viewer. The research shows promising results but also aspects that will have to be improved like the alignment of indoor and outdoor parts. Also the narrow field of view of the used Li- DAR device (iPhone) brought limitations.
