Search Results (547)

Street food vending is a crucial part of South Mumbai’s urban informal economy, but is often precarious, unrecognized and unprotected. This study explores the livelihood strategies of South Mumbai’s street food vendors and their complex pathways toward seeking to survive and gain dignity through engaging in decent work. Through a mixed-methods approach, we selected vendors (N = 120) through a systematic random sampling process who participated in semi-structured interviews and a focus group (one) discussion. Descriptive statistics and linear regression methods were applied to analyze the quantitative data, alongside qualitative narratives describing these vendors lived realities. Using the dualism, legalism, and structuralism perspectives of the informal economy, our findings revealed structural inequalities, financial insecurities, and regulatory barriers that mitigate stability. However, the use of social networks, informal credit, and collective strategies for coping under stress illustrates resilience. The urgency of reformed policies to support vendors, including licensing reforms, social protections, and progressive/engaged city planning, is highlighted in our findings, which provide support toward the change in street food vending from survival to dignity, in support of the ILO’s Decent Work Agenda. Full article

Urban informal settlements (UISs), characterized by self-organized housing, a high population density, inadequate infrastructure, and insecure land tenure, constitute a critical, yet underexplored, aspect of contemporary urbanization. They necessitate scholarly scrutiny to tackle pressing challenges pertaining to equity, sustainability, and urban governance. The automated, accurate, and rapid extraction of UISs is of paramount importance for sustainable urban development. Despite its significance, this process encounters substantial obstacles. Firstly, from a remote sensing standpoint, informal settlements are typically characterized by a low elevation and a high density, giving rise to intricate spatial relationships. Secondly, the remote sensing observational features of these areas are often indistinct due to variations in shooting angles and imaging environments. Prior studies in remote sensing and geospatial data analysis have often overlooked the cross-modal interactions of features, as well as the progressive information encoded in the intrinsic hierarchies of each modality. We introduced a spatial network to solve this problem by combining panoramic and coarse-to-fine asymptotic perspectives, using remote sensing images and urban street view images to support a hierarchical analysis through fusion. Specifically, we utilized a multi-linear pooling technique and then established coarse-to-fine-grained and panoramic viewpoint details within an integrated structure, known as the panoramic fusion network (PanFusion-Net). Comprehensive testing was performed on a self-constructed WuhanUIS dataset as well as two open-source datasets, ChinaUIS and $S^{2}UV$. The experimental results confirmed that the performance of the introduced PanFusion-Net exceeded all comparative models across all of the above datasets. Full article

Arbuscular mycorrhizal fungi (AMF) form symbiotic associations with most vascular plants and play an important role in immobilizing heavy metals in soil. Urban green space ecosystems are increasingly affected by heavy metal pollution; however, how different types of green spaces influence AMF diversity, stability, and coexistence mechanisms under heavy metal stress remains unclear. Here, heavy metal-contaminated soil samples were collected from Zhengzhou, China—a large city in the warm temperate monsoon zone of the North China Plain—to conduct high-throughput sequencing and analyze AMF community assembly. (1) AMF community composition varied significantly among green space types, with higher diversity in park green spaces (Shannon = 21.24 ± 2.24) than in street green spaces (Shannon = 11.36 ± 1.17). (2) Heavy metals were the primary factors driving AMF community assembly. Stochastic processes, mainly dispersal limitation, dominated AMF assembly across sites, with a stronger influence in street green spaces. (3) Specialist taxa (mainly Glomus and Claroideoglomus) exhibited higher network connectivity and stability in park green spaces, whereas generalist taxa maintained network resilience in street green spaces. This study elucidates the ecological processes shaping AMF communities in urban ecosystems and provides a scientific basis for AMF-based approaches to heavy metal remediation and sustainable management of urban green spaces. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

This study investigates the nonlinear, interactive, and temporally dynamic effects of urban form on street vitality within community-oriented metro station areas (MSAs) in Beijing. It offers potential reference value for other cities facing comparable challenges in MSA implementation and increasing motorization. This research addresses gaps in prior studies concerning the integration of multi-source data, nonlinearity, and diurnal variation. Utilizing an extended node-place-design framework, urban form is conceptualized through network, interface, and functional dimensions. The empirical analysis employs multi-source datasets, including 128,199 mobile device trips recorded in April 2024, OpenStreetMap for network data, Baidu points of interest for functional data, and Grasshopper for interface metrics, covering 183 street samples within a 1000 m radius of metro stations. Traditional regression models—such as ordinary least squares and spatial autocorrelation and cross-correlation—are used as baselines, while a novel gradient-boosting decision tree with latitude and longitude features is applied to enhance predictive performance. The results indicate that key contributors include road network density (16.89%), road intersections (10.56%), and point-of-interest density (9.74%), with Shapley Additive Explanations dependence plots demonstrating nonlinear thresholds. The analyses reveal synergistic or antagonistic interactions among features. Temporal fluctuations in feature importance further support the presence of diurnal dynamics. The study provides insights for time-sensitive urban planning aimed at enhancing MSA vitality, sustainability, and resident quality of life, while acknowledging that the conclusions are context-specific to Beijing and require additional validation in other urban environments. Full article

As global population ageing accelerates and urban governance increasingly prioritizes livability and age-friendly services, the 15-minute living circles concept has emerged as a key strategy to support daily walking exercise, social participation, and healthy ageing. In waterfront cities, blue spaces function as important everyday settings that contribute to environmental quality, recreational opportunities, and ecosystem services for older adults. This study extends the conventional 5D built environment framework by explicitly integrating blue space elements and characterizes older adults’ walking behaviour using four indicators across two dimensions (temporal and preference-based). We applied XGBoost regression and multiscale geographically weighted regression (MGWR) to identify threshold effects and spatial heterogeneity of blue space elements on older adults’ walking, and used K-means clustering to delineate blue space advantage zones within living circles. The results show that blue space accessibility, street scale, and water body density exhibit significant nonlinear relationships with older adults’ walking. Blue space elements shape walking behavior differentially and with pronounced spatial variation: in some living circles they encourage longer, recreational walks, while in others they stimulate high-frequency, short-distance walking. These effects produce destination preferences and time period preferences. The study highlights the pivotal role of blue spaces in age-friendly living circles and, based on spatial synergies among blue space advantage zones and their components, proposes renewal strategies including expanding the functional reach of blue spaces, constructing blue slow-walking corridors, and integrating blue–green symbiotic networks. Full article

Among wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments. Full article

In the context of sustainable development, the concept of active mobility plays a key role in modern urban areas. To evaluate active mobility in these areas, we formulate a framework for characterising active mobility by calculating street network indices using global, free, and open data. This framework comprises the download and processing of pedestrian, cycling, driving, and public transport street networks from OpenStreetMap, the selection of street network indices from the academic literature, and their implementation and calculation. A total of 50 indicators are reported for each urban area distributed in eight index types, including thematic variables, proximity to Points of Interest (POIs), proximity to public transport, intersection density, street density, street length, link–node ratio, circuity, slope, and orientation entropy. To test the framework, we calculate street network indices for pedestrian and cycling networks for the urban areas of 176 cities from around the world. The resulting dataset is published as open data. An analysis of the calculated indices indicates that cities in higher-income economies generally exhibit better conditions for active mobility, especially in Europe, attributed to better map completeness, and to more compact and connected urban areas where it is easier to access amenities and public transport. Full article

This study addresses the operational and environmental pressures of last-mile delivery in dense cities under limited urban logistics hubs. We propose a resource-efficient framework that repurposes existing convenience stores as robotic delivery hubs and formalize its operation via a two-stage optimization coupling truck and robot routing. In controlled simulations, and in a Seoul street network scenario, the approach reduces total completion time relative to a truck-only benchmark and lowers truck activity (truck-kilometers and curb idling), leading to lower estimated CO₂e under standard emission factors. We also observe a nonlinear relationship between the number of hubs and efficiency, suggesting a coverage “sweet spot”. These results indicate that with minimal new infrastructure, reusing commercial assets can improve operational performance and environmental proxies; social and labor outcomes are not measured here and are left for future field evaluation. Full article

Street vitality is an important indicator of urban attractiveness and sustainable development, and it has become a central topic in contemporary urban planning and research. Using the PRISMA methodology, this review systematically examines four major technologies including machine learning (ML), space syntax, GPS, and sensors, together with six categories of data that are commonly used in street vitality studies. The analysis traces the methodological development of these approaches and identifies application trends across both macro and micro spatial scales. ML has become the leading technology in this field, showing strong performance in dynamic modeling, pattern recognition, and the integration of multiple data sources. GPS provides high temporal accuracy for tracking mobility and identifying spatiotemporal dynamics. UAVs and sensor networks make it possible to observe environmental and behavioral responses in real time. When combined, these technologies support four main research themes: the built environment and vitality, pedestrian mobility and urban dynamics, spatial and visual characterization, and social interaction. Other complementary data sources, including social media, online maps, surveys, and government statistics, expand analytical coverage and improve contextual interpretation across different spatial and cultural settings. The review emphasizes the need to connect advanced technologies and diverse data sources with broader concerns of governance, ethics, and civic participation, while maintaining a focus on methodological and data-based synthesis. By clarifying the technological pathways and data foundations of street vitality research, this study provides a structured reference for researchers, urban designers, and policymakers who aim to develop evidence-based and socially responsive frameworks for urban space evaluation and planning. Full article

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents. Full article

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

A sensor network based on 10 stationary nodes distributed in Bari (Southern Italy) has been deployed for urban air quality (AQ) monitoring. The low-cost sensor systems have been installed in specific sites (e.g., buildings, offices, schools, streets, ports, and airports) to enhance environmental awareness of the citizens and to supplement the expensive official air-monitoring stations with cost-effective sensor nodes at high spatial and temporal resolution. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO₂, O₃), optical particle counter (PM₁₀), and NDIR infrared sensor (CO₂), including micro-sensors for temperature and relative humidity. The sensors are operated to assess the performance during a campaign (July 2015–December 2017) of several months for citizen science in sustainable smart cities. Typical values of CO₂, measured by distributed nodes, varied from 312 to 494 ppm (2016), and from 371 to 527 ppm (2017), depending on seasonal micro-climate change and site-specific conditions. The results of the AQ-monitoring long-term campaign for selected sensor nodes are presented with a relative error of 26.2% (PM₁₀), 21.7% (O₃), 25.5% (NO₂), and 79.4% (CO). These interesting results suggest a partial compliance, excluding CO, with Data Quality Objectives (DQO) by the European Air Quality Directive (2008/50/EC) for Indicative (Informative) Measurements. Full article

Urban green spaces (UGS) are pivotal to urban sustainability, yet their morphology—patch size, shape, and configuration—remains insufficiently linked to institutional drivers. We investigate how land tenure strength shapes UGS morphology across 36 cities in nine countries. Using OpenStreetMap data, we delineate UGS and compute landscape metrics (AREA, PARA, SHAPE, FRAC, PAFRAC) via FRAGSTATS; we develop a composite index of land tenure strength capturing ownership, use-right duration, expropriation compensation, and government land governance capacity. Spearman’s rank correlations indicate a scale-dependent coupling: stronger tenure is significantly associated with micro-scale patterns—smaller patch areas and more complex, irregular boundaries—consistent with fragmented ownership and higher transaction costs, whereas macro-scale indicators (e.g., overall green coverage/connectivity) show weaker sensitivity. These findings clarify an institutional pathway through which property rights intensity influences the physical fabric of urban nature. Policy implications are twofold: in high-intensity contexts, flexible instruments (e.g., transferable development rights, negotiated acquisition, ecological compensation) can maintain network connectivity via embedded, fine-grain interventions; in low-intensity contexts, one-off land assembly can efficiently deliver larger, regular green cores. The results provide evidence-based guidance for aligning green infrastructure design with diverse governance regimes and advancing context-sensitive sustainability planning. Full article

To overcome the limited accuracy of existing street vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous spatiotemporal datasets. The network introduces a two-backbone architecture for stronger multi-scale fusion, Spatial Pyramid Depth Convolution (SPDConv) for richer urban scene features, and Dynamic Sparse Sampling (DySample) for robust occlusion handling. Validated in Yibin, the model achieves 90.4% precision, 67.3% recall, and 77.2% mAP@50 gains of 6.5%, 5.3%, and 5.1% over the baseline. By fusing Baidu heatmaps, street-view imagery, road networks, and POI data, a spatial coupling framework quantifies the interplay between commercial facilities and street vitality, enabling dynamic assessment of urban dynamics based on multi-source data fusion, offering insights for targeted retail regulation and adaptive traffic management. By enabling continuous monitoring of urban space use, the model enhances the allocation of public resources and cuts energy waste from idle traffic, thereby advancing urban sustainability via improved commercial planning and responsive traffic control. The work provides a methodological foundation for shifting urban resource allocation from static planning to dynamic, responsive systems. Full article