Cycling plays an increasingly important role in sustainable urban mobility, offering benefits such as reduced carbon emissions, traffic decongestion, recreational opportunities and improved public health. This is aligned with Singapore’s city-in-nature concept and our commitment to car-lite options in the Singapore Green Plan with an expansion to 1300km park connectors, fulfilling Sustainability Development Goals 11 and 13. Despite expansive measures, less than 2% of residents cycle to work, signalling a need to move beyond isolated infrastructural interventions. This research aims to synthesise evidence of fragmented cycling infrastructure, highlighting a spatial mismatch, and explore how park connector design and accessibility influence cycling habits, encouraging active mobility opportunities in daily commutes.

Utilising Geographical Information Systems (GIS) data to spatially map cycling infrastructure across 12,877 HDB residential flats in 55 planning areas, I aim to integrate various access-to-cycling-infrastructure indices for each flat and aggregate the flat-specific metrics to the town level to assess residential access to cycling infrastructure. This highlights that cycling facilities are clustered in the Northeast and Northwest regions, leaving residential towns with inconsistent links to recreational and commuting routes. Additionally, fragmented connections, inconsistent end-of-trip facilities, and bottlenecks at intersections increase collision risks, reducing safety and providing inconvenient cycling experiences, discouraging active mobility. This shows a failure to close the mobility loop, as inconvenient commutes deter residents towards car-lite options. As such, I plan to identify systematic spatial gaps in connectivity, particularly in HDB towns with complex road layouts or missing cycling links and highlight the need to move beyond green corridor expansion, prioritising the connectivity of cycling pathways.

Rapid urbanization in coastal cities intensifies the pressure on transport infrastructure, while fragmented active mobility networks limit equitable access to key destinations. Shared pathway infrastructure plays a significant role in enabling sustainable transport and social inclusion; however, limited public resources necessitate evidence-based prioritisation of pathway investments to guide future investment decisions and maximise community benefits. This study developed a spatial multi-criteria decision analysis (MCDA) framework based on available spatial data to assess and prioritize shared pathway linkages in Tauranga, New Zealand. In total, 21 indicators were defined and grouped into eight criteria. The process involved a comprehensive literature review conducted across multiple databases and Multi-Attribute Utility Theory (MAUT) analysis. The conducted MAUT analysis resulted in the development of rules for a more standardized assessment of 91 existing and 128 potential pathway linkages. This assessment is based on participatory evaluation and introduces a utility-function-based hierarchical spatial model. Within the model, eight primary criteria contain network gaps and missing connections, access to key destinations, suburb-to-suburb connectivity, safety deficit areas, equity and accessibility gaps, environmental and feasible corridor opportunities, and cultural significance, forming the basis for a comprehensive analysis of key pathway performance dimensions. To assess the relative significance of indicators, two distinct weighting scenarios were applied: one based on expert evaluation and the other based on equal weights. The results reveal noticeable differences between the two applied approaches. The equal-weight scenario identified 20 high-priority linkages but under- or over-emphasised certain pathways. However, the expert evaluation reduced this distortion and revealed clearer distinct prioritisation patterns. The findings underscore the importance of participatory planning in optimising shared pathway infrastructure in coastal cities. Therefore, the primary contribution of this study not only involves the prioritisation outcomes but also the novel methodological framework, which combines the MCDA framework with MAUT and stakeholder inputs.

Urban heat islands (UHIs) are a defining challenge of contemporary cities, exacerbating thermal stress and increasing energy demand in the built environment. While UHI research has traditionally focused on quantifying air and surface temperature differences between urban and rural settings, there is limited integration of microclimatic effects into building energy performance, particularly within advanced energy-efficient frameworks such as Passive House and nearly zero-energy building (nZEB) standards. This study proposes an original Local Urban Heat Island Index (L-UHI) that operationalizes multiple urban design and landscape parameters—vegetation typologies, water surfaces, shading geometry, surface materials, and anthropogenic activity—to estimate localized thermal elevation relative to a rural baseline. Drawing on novel high-resolution drone thermal data and urban morphology metrics, the L-UHI model aggregates design-driven cooling and warming factors into a composite index that captures site-specific microclimatic impacts.

Beyond proposing a scalable methodology for urban project evaluation, the research advances a quantitative linkage between L-UHI and building energy performance by translating localized temperature amplification into its potential impact on heating and cooling demand. By incorporating L-UHI as a modified boundary condition within building energy assessment frameworks, the study explores how microclimatic intensification may alter annual energy consumption (kWh/m²·year) in high-performance buildings, including Passive House and nZEB standards. Rather than treating urban climate and building efficiency as separate domains, the proposed approach establishes a methodological bridge between urban morphology and energy modeling, enabling the estimation of climate-sensitive performance deviations at the project scale. The framework thus connects urban design decisions with measurable implications for energy demand and thermal stress, contributing to more integrated strategies for climate-adaptive urban development.

Rapid urbanization in large metropolitan cities worldwide has placed increasing pressure on urban transportation infrastructure, particularly along arterial corridors serving mixed uses and employment-intensive areas. In rapidly commercializing urban precincts, such pressures often lead to recurrent congestion, reduced travel efficiency, and increased pedestrian and vehicle conflicts, with implications for energy use, emissions, and urban sustainability. In this context, the present study assesses the traffic performance of the Jogeshwari Vikhroli Link Road in the Powai area of Mumbai, India, a key urban corridor connecting residential, commercial, and institutional zones to suburban railway stations. A road inventory survey and traffic volume count were conducted to analyze congestion at vehicle stressed nodes and links, including Gandhinagar Junction and the IIT Bombay Main Gate Road. The road inventory survey identified infrastructure deficiencies such as broken footpaths, faded lane markings, and the absence of adequate street hardware and furniture, which intensify pedestrian and vehicle conflicts. Traffic volume analysis revealed that out of 18 road stretches studied, six operate below the Indian average Level of Service C. Notably, the IIT Bombay Main Gate Road recorded a peak traffic flow of 2,307 passenger car units per hour per lane, corresponding to Level of Service E. Based on these findings, this study proposes context-sensitive geometric improvements, street infrastructure provisions, and maintenance measures to mitigate congestion and improve corridor performance. These include lane capacity optimization, installation of street hardware and furniture such as lighting, green buffers, and signage, and rehabilitation of existing infrastructure including kerb stones, lane markings, and pedestrian crossings. Due to the absence of Intelligent Transport Systems infrastructure and limited access to traffic surveillance data, the analysis relies on conventional traffic surveys. The findings nonetheless highlight the potential value of data-driven approaches in future urban mobility assessments.

This paper examines how a city’s relationship with birds reflects its evolving identity. It analyzes the anti-pigeon campaigns in London, England, Berlin, and Venice; the relationship between Delhi and sparrows and vultures; the cultural history of Istanbul’s seagulls; Singapore’s Hornbill rejuvenation success story; and Canada’s Bird-Friendly City Certification initiative, using London, Ontario, as an example. When do city managers focus on birds? When do the birds draw attention to themselves? When CDO citizens lobby for or against birds? When does the UN declare a bird-friendly decade? When are there avian flu outbreaks? Are birds just symbols of broader urban revisionings–collateral damage or untargeted recipients of protection? It compares the rhetoric and imagery used by cities to officially clarify their “bird” positionings. Does a city’s campaign against resident birds necessarily mean a campaign against all birds? Can a city be migrating bird-friendly and openly curb resident bird populations at the same time? It analyzes the different ways public health arguments are used both for and against urban birds, as well as how unwanted birds often symbolize changing relationships with tourists. It concludes by positing general lessons that the bird negotiations in London, England; London, Ontario; Berlin; Singapore; Delhi; Venice; and Istanbul can teach cities aiming to become more sustainable and biophilic.

Transit-Oriented Development (TOD) is a cornerstone of sustainable urban planning. Yet, operationalizing TOD requires urban science methods that integrate heterogeneous spatial data, quantify multidimensional performance, and reveal system-wide patterns. From a complexity-informed perspective, station areas function as complex adaptive systems where accessibility, land use, walkability, and socio-demographic demand co-evolve. In Sri Lanka’s Western Province—where railway stations and major bus terminals structure everyday mobility—TOD decisions are often constrained by transport-centric frameworks and simplified indicators that insufficiently represent multimodal realities and rapid urban change.

This study develops an urban informatics pipeline—integrating big-data, sensing, and computing—to diagnose TOD potential across 92 railway stations and 25 major bus terminals using a GeoAI-enhanced Node–Place–Design framework. The Node–Place Model provides the core analytical theory for assessing the balance between transportation performance (node) and surrounding development intensity/function (place), and is extended with a design dimension to better capture pedestrian accessibility and built-form conditions around transit areas. The assessment integrates 39 indicators operationalized under the 7D TOD principles (Density, Diversity, Design, Destination Accessibility, Distance to Transit, Demand Management, and Demographics). Indicators are normalized, weighted using an entropy-based scheme, and aggregated into a composite TOD index for each node. K-means clustering classifies transit areas into TOD typologies and identifies node–place mismatches (e.g., strong node/weak place). At the same time, an XGBoost model estimates the influence of indicators to highlight priority levers for intervention.

Results reveal substantial spatial variation and persistent imbalances between accessibility and development across the multimodal network, indicating where integrated station-area planning and targeted investments could unlock underutilized transit capacity and support sustainable, equitable urban growth. Theoretically, the study advances urban science by translating a complexity-informed, multimodal Node–Place–Design model into scalable, testable diagnostics; practically, it provides evidence-based typologies and policy levers to target station-area interventions, coordinate land-use–transport integration, and prioritize investments.

This research examines whether the selection and spatial positioning of plant species, according to their growth patterns, seasonal behavior, and climatic adaptation, can significantly improve bioclimatic performance and the Thermal Comfort Index in Mediterranean patio housing.

The working hypothesis states that strategically selected vegetation adapted to local climatic conditions and arranged according to solar orientation, vertical development, canopy density, and evapotranspiration capacity produces measurable improvements in thermal stability and indoor comfort when compared to non-optimized planting schemes.

Within the Casa Patio experimental dwelling, promoted by Todobarro in collaboration with the University of Málaga, plant species were selected based on: (1) deciduous or evergreen character for seasonal solar modulation, (2) evapotranspirative potential for passive cooling, (3) morphological growth structure for controlled shading, and (4) drought tolerance to ensure water efficiency under Mediterranean conditions. Vegetation is distributed across perimeter parterres, courtyard spaces, and interior zones to generate differentiated microclimatic effects, including dynamic solar filtering, humidity stabilization, and reduction of diurnal thermal oscillations.

The vegetation system operates synergistically with porous ceramic elements and programmable irrigation, activating evaporative cooling processes that enhance passive temperature regulation. A comprehensive sensor network and on-site meteorological station continuously monitor air and surface temperature, relative humidity, solar radiation, wind variables, and soil moisture, enabling real-time correlation between vegetative performance and indoor comfort thresholds.

Preliminary findings indicate that plant selection based on ecological adaptation and spatial strategy contributes to reductions in peak indoor temperatures, improved hygrothermal stability, and measurable gains in the Thermal Comfort Index during extreme summer conditions. The results support the hypothesis that, when integrated into architectural design as an active environmental system, botanical criteria can function as a quantifiable bioclimatic control mechanism in Mediterranean residential architecture.

Urban mobility planning in mid-sized cities often faces the challenge of insufficient or outdated data, limiting the development of reliable performance indicators for decision-making. Within the context of Transport System Models (TSM), this work presents a methodological proposal based on traffic microsimulation using open-source tools—specifically, the Simulation of Urban MObility (SUMO) platform—to support the estimation of key performance indicators (KPIs) related to sustainable urban mobility. The approach positions microsimulation as an operational-level component that complements traditional transport system analyses and ICT-based data sources in data-scarce environments.

As an initial case study, the downtown area of Bahía Blanca (Argentina) was selected due to its high traffic density and circulation complexity. A simplified microsimulation model is being configured using publicly available information on the street network, signal timing, and approximate traffic volumes, which are typical inputs obtained from urban ICT infrastructures. SUMO was selected for its open-access nature, transparency, and adaptability to urban contexts with limited data availability. In future stages, real-world traffic flow and vehicle-type data generated by our research group will be incorporated to improve traffic pattern accuracy and model calibration. Statistical validation metrics commonly used in transportation engineering, such as the GEH statistic, will be applied to support KPI estimation.

Preliminary experiments indicate that the proposed approach can reproduce general traffic dynamics and generate representative KPIs, including average speed, travel time, queue length, level of service, and estimated emissions. Although the results are exploratory due to current data limitations, they demonstrate the feasibility of integrating microsimulation within a broader TSM- and ICT-oriented decision-support framework to identify data needs and assess traffic management improvements.

The proposed framework contributes to the Sustainable Development Goals by supporting evidence-based mobility planning (SDG 11), promoting energy-efficient circulation patterns (SDG 7), and enabling the assessment of emission-reduction scenarios (SDG 13).

Rapid urbanization in tropical megacities has significantly altered local energy balances, intensifying severe microclimatic degradation. While traditional urban climatology depends on Land Surface Temperature (LST) and the Urban Heat Island (UHI) effect, these physical metrics often fail to detect the true human thermal discomfort experienced by residents. To address this critical gap, this study evaluates the spatiotemporal dynamics of human-centric urban thermal discomfort in Dhaka over a two-decade period from 2005 to 2025. Utilizing the Google Earth Engine (GEE) Python API, this study combines Landsat 5 and 8 optical-thermal data with ERA5-HEAT atmospheric reanalysis. This study uniquely quantifies localized structural degradation through the Urban Thermal Field Variance Index (UTFVI). It applies bilinear spatial interpolation to downscale coarse atmospheric data into high-resolution (30m) Universal Thermal Climate Index (UTCI) gradients, effectively translating surface anomalies into actual heat-health exposure. The findings reveal a rapid deterioration of Dhaka's urban thermal environment. Densely built-up areas saw a dramatic shift in peak LSTs over two decades, climbing from 32°C to over 40°C. At the same time, heat stress for people intensified. By 2025, average temperatures (UTCI) will hit 37.8°C to 38.1°C, putting residents in the 'Strong Heat Stress' category. Data from 500 random locations shows a strong, steady link between extreme environmental heat (UTFVI > 0.15) and the highest levels of physical discomfort. These findings confirm that the localized depletion of permeable surfaces directly forces severe atmospheric heat stress, providing a replicable, dual-index framework to guide targeted microclimatic interventions and climate-resilient urban planning in the Global South.

Rapid urbanization in Wolkite City, Ethiopia, has accelerated population growth and infrastructure development at the expense of green space. This study quantifies the relationships between green space and settlements from 1985 to 2024 and their long‑term trends using remote sensing, GIS‑based spatial indicators, and social research. The novelty of this research lies in the proposal of three original indicators for urban development trends—Green Space Loss Indicator (LOST_GREEN), Settlement Increase Indicator (SII), and Cumulative Ratio Index (CRI)—explicitly tailored to the socio‑spatial conditions of Sub‑Saharan African cities, with Wolkite, Ethiopia, as an illustrative case. These indicators track spatiotemporal land‑use dynamics, reveal a strong inverse relationship between settlement expansion and green space extent, and delineate critical zones in need of immediate planning action. Household survey results indicate uneven awareness of the benefits of green spaces across socioeconomic groups, with education, occupation, and income predicting environmental consciousness. Residents with higher levels of education and stable employment more frequently recognized the ecological and social value of green spaces. By integrating spatial metrics with social evidence, the study derives actionable guidance for urban planning and governance to support sustainable growth in Wolkite. Beyond this case, it offers a replicable assessment framework for fast‑growing Sub‑Saharan cities and underscores the importance of public awareness and participatory planning in mitigating ecological degradation and balancing urban expansion with environmental integrity.