Cities are essential for economic, social, and cultural life, driving innovation but also creating inequalities. Challenges like rapid growth and inadequate infrastructure lead to vulnerabilities in urban areas. This phenomenon of social vulnerability extends beyond individual sociodemographic characteristics; it is also closely tied to the quality of services provided by the living environment, spatial organization, and accessibility. Within this framework, the phenomenon of social vulnerability is not limited to the sociodemographic characteristics of individuals; it is also directly related to the services offered by the living environment, spatial arrangements, and, in particular, accessibility. This study explores social vulnerability through the lens of accessibility and socioeconomic status, using the 15-Minute City model. The Mean Shift clustering algorithm was utilized to cluster buildings and a new classification of points of interest (POIs) with 12 categories was proposed. A theoretical accessibility analysis for these POIs were conducted for each building cluster, and an accessibility index was computed. Additionally, land cover analysis was performed for each neighborhood, followed by correlation analyses between the accessibility index, land cover, and socioeconomic scores. The findings show no significant link between land cover and accessibility, but there is a strong correlation with 0.57 between access to sports facilities and socioeconomic status. Lower socioeconomic groups have limited access to fragmented areas, while higher groups reside in well-planned regions with more services. The study shows that spatial inequalities are tied to income, living standards, density, and transportation, emphasising the need for future research on social dimensions.

Building fires are considered major disasters because of their significant effects on people, property, and the environment. This understanding has led to increased attention on developing preventive measures, particularly through the creation of effective methods for assessing fire risk. However, the effectiveness of these methods relies heavily on detailed physical and functional information of the building and data-driven decision-making. Building Information Modeling (BIM) has proven effective in representing structures, even in three dimensions. When integrated with Geographic Information Systems (GIS), it enhances spatial intelligence, leading to improved decision-making through robust multi-criteria approaches. Hence, this study develops a framework to assess fire risk in an indoor environment that deploys a BIM-based GIS and Multi-Criteria Decision-Making; this is specifically known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The developed framework consists of four steps: identifying fire risk parameters, calculating weights, conducting spatial fire risk assessments, and visualizing the results, where the developed concepts are tested and validated. According to the significant findings, the developed framework estimates that 18% of building rooms are at moderate risk, while the compared model identifies only 1%. This considerable difference could potentially arise from the detailed data structure of BIM and the spatial insights gained from GIS. By implementing the designed framework, key fire risk factors can be identified in three dimensions, accompanied by a comprehensive quantitative evaluation platform for fire risks within indoor environments.

The formulation of effective evacuation strategies in indoor spaces during fire incidents is paramount to minimizing loss of life. Complex structures, such as multi-storey buildings, university campuses, shopping malls, and hospitals, present unique challenges for evacuation due to their architectural designs and high occupancy levels. To address these challenges, conducting fire simulations is essential for both expediting evacuation processes and identifying the safest evacuation scenarios. However, several fragmented tools/solutions need to be integrated to model the infrastructure, simulate fire dynamics, and human behavior. Hence, this study aims to integrate these fragmented solutions into a data-driven Building Information Modeling (BIM) based platform that includes Fire Dynamics Simulator (FDS) and rule-based evacuation simulations for various human profiles. The framework analyzes various dynamic factors that can assist decision-makers in implementing strategies to mitigate and redesign structures, ultimately reducing fire risk in complex buildings. The analysis of these simulations, including congestion, accessibility, and density, enables the identification of critical factors and hazardous areas that contribute to extended evacuation times and increased smoke spread, thereby heightening the risk of loss of life.

Background The Positive Energy District (PED) concept aims to transform urban areas into carbon-neutral and zero-energy communities by reducing energy consumption and increasing renewable energy production. However, challenges remain in making effective decisions, including inconsistent data sources, difficulties in collaboration among stakeholders, and the need to merge interdisciplinary technical and scientific knowledge. Hence, GIS-based frameworks offer mature solutions to address such issues. Methods This study introduces a three-dimensional (3D) GIS-based framework designed to improve decision-making in PEDs, with a particular focus on spatiotemporal analyses of solar energy potential. The rationale behind selecting solar energy is the high potential of urban areas for suitability, as well as the legal and technical constraints associated with wind and hydro energy production within densely populated areas and built-up areas. The literature review showed that the combined use of 3D GIS and PED concepts has not yet been widely explored, despite the significant potential that exists. The developed framework were deployed and tested in the case study area – the Caferağa neighborhood of Kadıköy, Türkiye. In order to test and validate the achieved results, various platforms are utilized and compared. Results The 3D GIS-based framework demonstrated several advantages over traditional systems. It utilized spatial data, including high-resolution 3D building models, to assess solar potential, employing and comparing various spatial tools. Comprehensive analyses were performed to assess the exposure of buildings to sunlight during different seasons and to evaluate the shading effects encountered throughout the day. The analysis of sun hour revealed that only 5% of the entire work area received more than nine hours of sunlight during the winter. Additionally, when evaluating the solar potential, a 22.29% discrepancy was found within utilized platforms. Conclusion The achieved results are promising, where co-creation, co-design- and co-implementation could be performed collaboratively via designed platform that seamlessly integrate data, functions, necessary tools and expertise. The results indicate that 3D spatial analyses promote the potential for solar energy and aid decision-makers in the complex processes of designing and expanding PEDs.

Background The Positive Energy District (PED) concept aims to transform urban areas into carbon-neutral and zero-energy communities by reducing energy consumption and increasing renewable energy production. However, challenges persist in facilitating effective decision-making. GIS-based frameworks offer mature solutions to help overcome these issues. Methods This study describes a GIS-based framework to integrate databases, conduct spatial analyses, and support decision-making through spatiotemporal analyses of solar energy potential to support decision-making in PEDs. Furthermore, the developed concepts are deployed in the case study area – the Caferağa neighborhood of Kadıköy, Türkiye. Results According to the results of spatial analyses including sunlight exposure and shading effects, there is significant potential for solar energy within the district, especially in spring and summer seasons. The total solar energy was 629 million kWh, with an average solar energy of 1,410 kWh/m². Three-dimensional (3D) spatial analyses indicate that the solar energy potential is high on both roofs and various facades of the building. Conclusion The achieved results are promising, where co-creation, co-design- and co-implementation could be performed collaboratively via designed platform that seamlessly integrate data, functions, necessary tools and expertise.