Rapid urbanisation in Global South cities has led to a progressive decline in accessible green and recreational spaces, resulting in increased environmental stress, lifestyle disorders, and deteriorating mental well-being. In metropolitan cities such as Chennai, rising temperatures, high-density development, and uneven distribution of public open spaces have intensified health vulnerabilities at the neighbourhood level. This study conceptualises urban green–recreational spaces as Urban Breathing Systems and examines their role as micro-scale health acupuncture interventions in promoting physical and mental well-being. Using Chennai City as a representative case of Global South urbanisation, the study adopts a mixed-methods and spatial analytical approach by integrating high-resolution satellite data, GIS-based accessibility measures, neighbourhood surveys, and secondary health records to construct composite indicators of green exposure, thermal comfort, recreational accessibility, and perceived well-being. Structural Equation Modelling (SEM) was employed to analyse the direct and indirect relationships between green–recreational infrastructure, environmental mediators, and health outcomes, supported by spatial regression and hotspot analyses to identify health-risk and green-deficit zones. The results reveal a statistically significant positive association between green recreational accessibility and both physical activity levels and psychological well-being, with thermal regulation and environmental comfort emerging as key mediating variables. Neighbourhoods with limited access to urban breathing systems exhibited higher stress levels and greater heat-related vulnerability. The study demonstrates that strategically distributed micro green–recreational interventions can function as effective urban health acupuncture in dense metropolitan contexts and provides an evidence-based framework for integrating health-sensitive green infrastructure into urban planning policies to support sustainable and resilient urban development in the Global South.

This research presents a comprehensive framework for the spatial modelling of Indian cities through the algorithmic translation of statutory planning regulations into three-dimensional urban form. With a specific focus on Navi Mumbai as a prototypical case study, the work develops a regulation-sensitive, procedural digital shadow that simulates both present and future urban morphology. By parameterising the Unified Development Control and Promotion Regulations (UDCPR), the model integrates plot-level geometry, building control norms, and development plan data to generate regulation-compliant, rule-based predicted 3D built form. An architectural typology classification system is formulated based on plot size—monoblock, perimeter block, and colony structure—and these forms are encoded into Computer Generated Architecture scripts. A heterogeneous dataset is compiled to construct a baseline 2018 model and the model generates a hypothetical redevelopment scenario for 2038 and 2058 horizon years. Model evaluation is undertaken through quantitative comparison with development plan population projections, visual validation against the actual built environment, and procedural compliance with the UDCPR. The model encompasses various analytical applications, such as redevelopment potential mapping, shadow and daylight simulation, and urban density analysis. A demonstrative application shows how the model integrates with environmental simulation tools to assess performance-based planning outcomes like annual daylight hours. The future scenario reveals a significant morphological shift from mid-rise to high-rise typologies, with an increase in built-up area from 0.54 to 1.85 million sqm, and a projected population escalation from 0.34 to 1.08 million. The paper also explores the implications of such modelling tools for sustainability, resilience and infrastructure forecasting while identifying the limitations of deterministic procedural models. This research aims to bridge the current gap between static regulatory documents and the dynamic realities of urban transformation.

Women’s experiences of safety and comfort in urban parks are shaped by interrelated design, environmental, and social factors that are rarely captured in a single evaluative framework. Focusing on neighbourhood parks in Amman, Jordan, this study develops the Women’s Safety Priorities Index (WSPI), a composite indicator designed to assess how women perceive the safety, accessibility, and inclusivity of these everyday public spaces. Survey data were collected from female park users and grouped into key dimensions including lighting, signage, visibility, isolation, accessibility, facilities, environmental quality, and motivation to visit. Item scores were normalised to a common scale, with isolation reversed so that higher values consistently reflected more women-friendly conditions, and then aggregated into dimension-level sub-indices. These sub-indices were combined into an overall WSPI using data reduction techniques to derive empirical weights, and internal consistency checks were conducted to ensure reliability. A priority improvement matrix was constructed by jointly considering central tendency and variability for each dimension, and further analysis examined differences across age, marital status, and employment groups. The findings indicate that accessibility, signage, and lighting are perceived as comparatively strong aspects of neighbourhood park design, while environmental quality, the availability and condition of facilities, motivation to visit, and feelings of isolation are weaker and require more urgent attention. Priority analysis highlights facilities and environmental quality as primary targets for intervention, with visibility, motivation, and isolation forming a second tier of concern. Demographic patterns show that younger women place particular emphasis on lighting and visibility, married women are especially sensitive to accessibility and reduced isolation, and employed women place greater value on facilities and environmental comfort. Overall, the WSPI translates complex perception data into actionable insights, supporting planners and policymakers in Amman and similar cities in prioritising gender-responsive improvements in neighbourhood park design and management.

The Housing Rehabilitation of the Asia Minor Refugees (1922–1980s) produced some of the most distinctive layered urban landscapes in the Attica basin and other Greek cities. This paper—based on literature review, archival research, and extensive fieldwork including photographic documentation and spatial mapping—examines the evolution of these settlements and the present condition of their built environment. Emphasis is placed on their architectural and urban-design qualities, which constitute a vital yet undervalued part of Greece’s tangible cultural heritage. These settlements feature a rich range of architectural morphologies: vernacular influences and Asia Minor construction traditions coexist with early examples of Modernism and Bauhaus-inspired social housing, characterized by clarity of form, human-scaled layouts, and distinctive architectural details rarely found in contemporary buildings. These characteristics form a unique, yet fragile, heritage corpus, contributing significantly to the cultural identity of the urban landscape. Despite their importance, most Asia Minor Refugee districts are not listed as monuments; only a limited number of buildings or ensembles are protected under formal protection. Consequently, they face deterioration, piecemeal alterations, and redevelopment pressures that threaten their integrity.

The findings underscore the need to integrate them into a cultural sustainability framework—one that recognizes their architectural and urban-design value and ensures that these historic layers remain active components of the evolving city.

Due to rapid urbanization, cities in developing countries are increasingly affected by climate change, including rising urban temperatures. Understanding the spatiotemporal relationship between Land Use/Land Cover (LULC) change and Land Surface Temperature (LST) is crucial for sustainable, affordable housing development and for ensuring urban resilience by monitoring the Urban Heat Island (UHI) effect. This study employed remote sensing data to explore the spatiotemporal relationship between LULC and LST in Addis Ababa, Ethiopia. In addition, the study analyzed the spatial variabilities of LST values and the relationship between LST and different spectral Indices. Random Forest (RF) and geostatistical methods were employed for LULC classification and for determining the spatial variabilities of LST, respectively.

The findings highlight the increases in built-up areas between 2015 and 2020 and between 2020 and 2025. In comparison, vegetation and bare land LULC have decreased from 2015 to 2020 and from 2020 to 2025. The LULC also correlated with the LST. The highest mean LST value was found in the built-up areas. Bare land had the second-highest mean LST value after built-up areas. Vegetation land cover had the lowest mean LST value. The correlation analysis indicated a negative relationship between LST and the NDVI, SAVI, and MNDWI. In contrast, positive correlations were found between LST and the BSI and the NDBI. The results from the hotspot analysis revealed significant heat islands (hotspots) in the city center in 2015. However, heat islands have expanded rapidly to the outskirts of cities, implying gaps in the climate resilience of affordable housing units. The study's insights highlight the need for preventive measures in hotspot areas by integrating resilient design into affordable housing through actionable strategies to enhance its resilience and sustainability amid rapid urbanization and climate change. Increasing vegetation cover, using light-colored buildings, and incorporating green infrastructure have helped mitigate the rising heat effect in affordable housing areas.

Cities experiences higher temperatures compared to surrounding rural regions. The research utilized remote sensing data to analyze spatio-temporal changes in land use and land cover, land surface temperature , Normalized Difference Vegetation Index , and normalized difference built-up index (NDBI) across Jamshedpur Urban Agglomeration over 35 years from 1990 to 2025. The findings revealed a significant increase in built-up areas (237%) and a decrease in vegetation (21%), resulting in rise in average land surface temperature from 22.5°C in 1990 to 30.1°C in 2025. Industrial cores, commercial hubs, and densely populated residential areas were identified as severe UHI zones, with temperatures up to 5°C higher. Commercial areas like Sakchi and Bistupur emerged as localized UHIs, with temperatures up to 3°C higher than surrounding residential areas.

Heating patterns in residential area was influenced by urban morphology, building materials, and green cover. Slum clusters experiences high temperatures (35.5°C) due to high-density, low-rise dwellings with heat-trapping roofing materials, and sparse vegetation. In contrast, planned low-density housing areas like Sakchi recorded relatively lower temperatures (30-31.3°C) due to higher vegetation cover and more permeable surfaces. The research emphasizes the urgent need for interventions to mitigate UHI effects. These include implementing cool roof technologies, expanding green spaces, enhancing ventilation through climate-sensitive urban design, and developing hierarchical networks of green areas at various scales.

The study underscores the importance of incorporating UHI adaptation into urban planning policies to create more livable and climate-resilient cities. The research highlights the necessity of proactively mapping UHIs, conducting granular assessments of localized drivers, and adopting heat-mitigating measures synergistically across building, neighborhood, and city scales. As India's urbanization continues to accelerate, this research exemplifies the necessity of incorporating urban heat island adaptation as a policy priority for fostering livable, climate-resilient cities.

India's rapid urbanisation has precipitated a severe air quality crisis, yet 47% of the populationresides in unmonitored "blind spots". This data deficit hinders the application of GIS and AIessential for health-supportive urban planning. Because of this spatial sparsity, traditionalinterpolation methods, such as Kriging or Inverse Distance Weighting (IDW) are not effective dueto their inability to capture the non-linear, dynamic phenomena. This study addresses the criticalchallenge of monitoring air quality in unmonitored urban sectors of Ahmedabad, India. It presentsa hybrid Spatio-Temporal Graph Neural Network (ST-GNN) framework that fuses Sentinel-5Psatellite data with urban spatial features and meteorological variables. By integrating GraphAttention Networks (GAT) for spatial dependencies with an optimized XGBoost regressor, themodel achieves high predictive precision. The results demonstrate a R2 of 0.727 for PM2.s and aR2 of 0.809 for NO2. This framework provides a scalable solution for high-resolution, citywidepollution mapping to support data-driven urban policy. It can estimate pollution levels inunmonitored "blind spots". This study describes the framework's architecture, data processing,and its validation, establishing a new benchmark for scalable, cost-effective air qualitymanagement systems. These systems are crucial for meeting the goals of the National Clean AirProgramme (NCAP).

The rapid growth of e-commerce has transformed urban freight systems, making last-mile delivery (LMD) a critical component of urban mobility with important implications for congestion, emissions, and operational efficiency. In response, several innovative solutions have been proposed, including urban consolidation centres, alternative delivery vehicles, etc. Among these, parcel lockers (PLs) have emerged as a promising option because they enable delivery consolidation, reduce failed delivery attempts, and improve service reliability.

Despite the growing literature on LMD innovations, limited attention has been devoted to the economic and investment dimensions of parcel locker deployment, especially across different territorial contexts. This gap is particularly relevant in the current policy framework, where European and national strategies increasingly promote transport decarbonisation and smart urban logistics. Understanding under which conditions parcel lockers represent a viable investment is therefore essential for evidence-based decision-making.

This study investigates PLs as an investment strategy in urban LMD systems, adopting a broader perspective that conceptualises them as decentralised micro-hubs or micro-warehouses for freight consolidation and temporary storage. Building on a structured literature review, the paper addresses two research questions: how freight micro-hubs affect the cost structure of urban LMD systems, and to what extent investments in parcel lockers improve delivery efficiency and reduce operational costs.

The analysis develops a cost-based evaluation framework using key parameters identified in the literature, including delivery costs, failed delivery rates, consolidation effects, and distance reduction. Focusing on Italy, the study compares metropolitan, medium-sized, and peripheral cities, and provides practical recommendations for logistics operators considering parcel locker investments.

Daily and weekly variations shape how people perceive streetscapes. These environments typically include vegetation, built form, and water features, which provide a basis for classifying high streets into grey, green, and blue typologies. Street typologies were classified based on observed environmental characteristics using a structured framework. This study aims to investigate how day-of-week and time-of-day variations shape soundscape perception — specifically dimensions of eventfulness, pleasantness, vibrancy, and calmness — across grey, green, and blue high-street typologies in London, to inform temporally responsive design strategies. To address this, data were collected at sites in Chelsea and Camden through 10 autonomous soundwalk groups across nine specified locations. The acoustic and perceptual data were analysed using the Kruskal–Wallis and Mann–Whitney U nonparametric tests, which are appropriate for non-normally distributed perceptual data.

Overall, the results indicate that temporal differences—particularly day-of-week and time-of-day—affect how grey, green, and blue high streets are perceived. In grey locations, weekend sound atmospheres are perceived as more vibrant. In green locations, weekends are perceived as calmer and quieter than weekdays, while weekday soundscapes are perceived as more chaotic. From an hourly perspective, grey locations are perceived differently throughout the day, with early hours perceived as more pleasant and vibrant. Mann–Whitney U test results also suggest that, in green spaces, early hours are perceived as more uneventful, calm, and monotonous, whereas later in the day increased people and traffic sounds are associated with higher perceived eventfulness and chaos. These findings suggest that high-street design should incorporate temporally responsive soundscape and environmental design approaches, rather than relying solely on static interventions.

This paper addresses the problem of detecting and classifying power quality disturbances in modern urban networks, an essential aspect for the reliable operation of critical infrastructures. In the context of smart cities, where energy systems support public transport, digital infrastructure and urban services, power quality monitoring becomes a key factor for operational continuity and performance. The main objective of this work was to develop and evaluate an artificial intelligence-based framework for the automatic identification of the main types of disturbances: voltage sags, voltage swells, harmonics and transients. To this end, simulated signals with varying levels of Gaussian noise and overlap between classes were generated to reproduce realistic operating conditions. Three classification methods were compared: (i) a one-dimensional convolutional neural network (1D-CNN) trained directly on the raw signal, (ii) a multilayer perceptron based on extracted features such as RMS value and total harmonic distortion, and (iii) a Support Vector Machine classifier. The results show that the 1D-CNN model offers superior performance and high robustness to noise, maintaining high accuracy even under conditions of significant signal degradation. In addition, it allows for efficient detection of short-duration transients without requiring manual feature extraction. The main contributions of the paper include (1) the direct use of the raw signal for classification, eliminating intermediate processing steps; (2) the systematic evaluation of performance based on the signal-to-noise ratio; (3) the integration of a “2-of-2” decision logic to stabilize the results in real-time monitoring scenarios; and (4) the analysis of the model interpretability by visualizing the internal activations. The paper is addressed to electricity grid operators, smart infrastructure solution providers, local authorities, and researchers in the field of signal processing and machine learning. The results demonstrate that the proposed approach can contribute to the development of scalable and reliable systems for power quality monitoring, with direct applications in the management of modern urban infrastructures.