Urban Scale Research Articles

Exploring the driving factors of urban flood at the catchment Scale: A case study of multitype megacities in China

With the increasing frequency of extreme rainfall events worldwide, urban flood in densely populated and economically vibrant urban areas has become a pressing issue drawing global attention. Catchments are fundamental units for understanding urban hydrological processes and managing flood. Current research often lacks an investigation into the importance and spatial differences of driving factors for urban flood at the catchment scale, particularly for different types of cities. This study uses 15 megacities in China as case studies and establishes a comprehensive system of driving factors. It categorizes cities based on population levels, climate zones, and spatial locations. The study begins with hotspot analysis to identify areas with high occurrences of urban inundation. Subsequently, it employs Geographic Weighted Regression (GWR), a spatial analysis technique, to unveil the mechanisms driving the spatial distribution of inundation points in different types of cities and the spatial heterogeneity of driving factors at the urban catchment scale. The research findings, based on multiple cities, demonstrate that urban inundation points exhibit clear spatial clustering characteristics. While impervious surfaces and urban buildings are generally more important than factors such as landscape patterns and population density, the importance and explanatory power of driving factors for the spatial distribution of urban inundation vary across different cities and even within different catchments of the same city. The categorization of cities based on population levels and spatial locations demonstrates stronger regularities in driving mechanisms compared to categorization based on climate zones. The study’s results further underscore the complex driving mechanisms behind the spatial distribution of urban inundation. This highlights the need for flood management strategies that consider both universal patterns of inundation occurrence and the specific characteristics of local driving factors to develop targeted and effective mitigation measures.

Spatial Agglomeration Mining of Urban Recreational Amenities: the Case of the Greater Bay Area in China

Understanding how urban recreational amenities are agglomerated and clustered with each other is crucial. This paper attempts to reveal the features of such spatial agglomeration patterns by innovatively developing a model termed as spatial agglomeration mining. Based on the case of Greater Bay Area in China, it achieves three conclusions. First, recreational amenities are highly agglomerated rather than evenly distributed at both regional and urban scales. Second, the regional and urban agglomeration patterns are similar in the sense that the agglomeration core is mainly composed of convenience-service amenities, while the periphery is mainly composed of leisure and entertainment amenities. Third, cities with similar size and economic functions share a similar structure, whereas amenities with a larger number do not guarantee a more central position in the clusters. This study sheds light on the general principle’s relativeness among amenities for urban investors and managers to make better decisions.

Hidden structural information reconstruction and seismic response analysis of high‐rise residential shear wall buildings with limited structural data

AbstractThe high‐rise residential shear wall structure is a crucial component of urban building clusters, while the limited availability of detailed structural information becomes a critical bottleneck in improving the accuracy of seismic performance assessment for high‐rise residential shear wall buildings in urban areas. Based on easily obtainable yet limited structural data at the urban scale, this paper proposes a method to address the shortcomings of existing research on reconstructing hidden structural information and enhance the accuracy of structural seismic performance assessment. It includes a physics‐constrained generative adversarial network module and a fuzzy inference system module to reconstruct the spatial arrangement of shear walls, and material strength grades within buildings, respectively. Validated against two actual buildings, the method outperforms the widely used simplified analysis method at the urban scale, achieving 85.9% accuracy in predicting damage states across various floors.

Local contribution of road traffic and residential biomass burning to black carbon aerosols – Modelling and validation

Black carbon (BC) is a harmful pollutant to human health and the environment. The aim of this paper is to evaluate the local contribution of road traffic and residential biomass burning to BC pollution levels at urban scale by implementing an integrated modelling approach with high spatial and temporal resolution. The traffic emission was extended to include BC emission factors considering road network as line emission sources. A new module to calculate emissions from residential biomass burning was developed based on the heating degree days methodology and local surveys. The modelling approach implemented in Coimbra, Portugal, was validated with observations collected during a six-month monitoring campaign at urban traffic and urban background stations using thermo-optical and optical methods. At the traffic station, modelling results showed that 86% of BC concentrations are related to traffic activity, which are mostly due to hot exhaust emissions (76%). Nevertheless, biomass burning has a significant contribution in the whole urban area during winter. The results of monthly average BC concentrations obtained from dispersion modelling revealed a contribution from local road traffic and residential biomass burning emissions of around 4 μg m−3 at both monitoring sites during the coldest period, but this contribution was two times higher for the hotspots identified within the urban area. Moreover, hourly values at hotspots obtained from the modelling may achieve 85 μg m−3, thus stressing the importance of spatial and temporal analysis with high resolution. The validation approach applied in this work highlight the need for more transparency in defining “black carbon” reported by modelling to ensure comparability with measurements obtained by different techniques.

Exploring the scale effect of urban thermal environment through XGBoost model

The urban thermal environment constitutes a significant aspect of the urban physical environment and warrants consideration in urban planning. Nonetheless, the nonlinear relationship between various factors and the urban thermal environment across different scales, as well as effective strategies for mitigating it, remain unclear. This study employs XGBoost and SHAP models alongside Partial Dependency Plot to analyze the impact of changes in socio-economic activities, built environment, and blue-green spatial patterns on land surface temperature grades within the Shanghai municipality and central Shanghai. Findings underscore that factors influencing the urban thermal environment vary between the municipality and central urban scales. Specifically, blue-green spatial patterns change hold greater relative importance at the scale of the municipality in Shanghai, while socio-economic activities and built environment changes are more significant at central Shanghai scale. Moreover, the spatial contribution of factors varies across scales, and most exhibit a nonlinear relationship with the urban thermal environment. This in-depth exploration of the scale effect on the urban thermal environment offers insights for formulating urban planning strategies to alleviate associated challenges.

Does self-containment of spatial scale and land use function contribute to mitigate urban heat island effects? Lessons from new towns in Shanghai

The concept of self-containment in new towns has been widely discussed from social and economic perspectives. However, localized interpretations within the context of China's development, particularly regarding climate adaptability and urban heat island (UHI) mitigation, are scarce. To fill this gap, our research analyzed self-containment from the perspectives of urban spatial scale and land use function. Focusing on Shanghai’s five new towns, we empirically demonstrated how self-containment influenced the UHI effects from 2005 to 2020, employing the Geodetector method. The findings reveal that during the daytime, the intensity of UHI in new towns decreased, serving as vital connectivity nodes of UHI within the region. Conversely, during the nighttime, both the intensity and area of UHI showed an increasing trend. The research confirmed that expanding the urban scale and functional diversity are effective strategies for mitigating the UHI. Based on these findings, we offer practical suggestions for the development of new towns: Increase population size while ensuring coordination with development scale; enhance mixed-use functions in large-scale development projects like university towns and industrial parks; and be vigilant of potential functional decline in central areas and increasing thermal impact due to new town development. Overall, this study enriches our understanding of self-containment in Chinese new towns and provides valuable insights for mitigating UHI in other similar contexts.

The non-linear impact of urban rail transit on commercial property prices: Based on a spatial heterogeneity perspective

Urban rail transit (URT) can boost population and business agglomeration, and increase premiums for surrounding properties. Commercial properties with an operational nature typically gain higher premiums and have the potential to capture value. However, the relationship between URT and commercial property price (CPP) has not been fully explored. Therefore, this paper takes Shanghai as an example, based on URT travel data, property sales information, and other multi-source geographic data, comprehensively analyses the intrinsic mechanism of URTs affecting CPPs. The results confirm that URTs have a significant positive effect on CPPs, but the degree of impact varies in the presence of spatial heterogeneity and submarket effects. On the urban scale, the effect increases and then decreases as one moves away from the CBD; at the station area scale, URTs have a non-linear step-decreasing effect on CPPs. Furthermore, the premium that URTs can generate is limited and subject to multiple constraints in terms of location, built environment, residents' travel habits and expectations. The findings can provide a reference for formulating value capture policy, planning URT routes, and enhancing urban economic vitality.

Optimization framework for daylight and thermal environment of retractable roof natatoriums based on generative adversarial network and genetic algorithm

This paper proposes a framework for multi-objective optimization of indoor daylighting and thermal comfort in natatoriums with retractable roofs, using genetic algorithms and building performance simulation. The goal is to balance daylight illumination and thermal comfort under different roof opening states. Efficiency of computational fluid dynamics simulation is improved by a multi-objective optimization framework based on non-dominated sorting genetic algorithms, integrating a generative adversarial network to learn the environmental map. The study analyses the impact of different roof opening ratios on environmental objectives and establishes regression equations to support optimization and accurate decision-making. In the optimal opening state, compared to a fully closed roof, the daylight factor and natural airflow velocity increase by up to 3.9 and 5.3 times, respectively, while the universal thermal climate index decreases by 1.0 °C. The introduction of a generative adversarial network proxy model significantly improves computational efficiency, accelerating natural airflow velocity calculation by 70–100 times and reducing simulation time by 98.6 %. This study expands predictive models from the urban scale to the building scale, enhancing simulation efficiency and promoting the application of deep learning models and genetic algorithms in climate-responsive building environment assessment and prediction.

Multi-dimensional equilibrium optimal allocation of water resources in Beijing-Tianjin-Hebei region through spatial association network analysis

The rapid social and economic development has led to problems such as over-exploitation of water resources, strong demand for physical water resources, and disequilibrium of water resources load. Based on the current situation of the water resources system in Beijing-Tianjin-Hebei region, this study proposed an input–output analysis-assessment-prediction-optimization framework. A water resources dissipation environment extended multi-regional input–output model was established, and the spatial equilibrium of water resources load was evaluated comprehensively. Furthermore, a multi-dimensional spatial equilibrium optimal allocation for water resources was built to identify key nodes and sectors. The primary findings were as follows: (1) Shijiazhuang and Tangshan were the main cities with net virtual water outflow, while Beijing and Baoding were the primary recipients of net virtual water inflow; (2) The overall score for the spatial equilibrium assessment of water resource load was low, with dimension scores predominantly ranging between 0.2 and 0.6; (3) The key transfer nodes were predominantly concentrated on the “outflow from Shijiazhuang” and the “inflow to Cangzhou and Langfang” nodes. This research conducted an in-depth exploration of the direction of water resource transfers, the actual stress conditions, and the spatial equilibrium of water resources within the Beijing-Tianjin-Hebei region. Finally, the future adjustment strategy for water-intensive industries at the urban agglomeration scale was obtained.

How Does Urban Scale Influence Carbon Emissions?

How Does Urban Scale Influence Carbon Emissions?

Passive over active: How low-cost strategies influence urban energy equity

This study delves into the energy burden on households, a crucial aspect of energy justice, influenced by urban environment factors and buildings’ passive and active designs. It evaluates the effects of passive and active design features on household energy expenditures at the census tract scale. Applying advanced Machine Learning techniques, including multiple and decision tree regressions, random forests, support vector machines, XGBoost, and Neural Networks, the research assesses the impact of various factors on the energy burden. Findings reveal that passive design elements significantly outweigh active ones in reducing energy costs at the urban scale, as confirmed by a model with a 94.8 % R2 accuracy. The insights provided are vital for policymakers, urban planners, architects, and researchers, pushing for sustainable urban planning and energy justice by prioritizing effective design strategies. This contributes to a broader understanding and implementation of energy-efficient measures in urban development.

Integrated effects of pavement simulation models and scale differences on the thermal environment of tropical cities: physical and numerical modeling experiments

Simulation methods attempt to explain what happens in full-scale environments. However, as simplification procedures, they also have their limitations and opportunities. One of the applications is to use the output data of a physical model to calibrate numerical simulation, or even to use outputs of numerical simulations to analyze urban scale studies. But it is uncertain the error in the interaction between these models. This study aims to analyze the impact of scale analysis and pavements simulation model modification on ambient and surface temperature of asphalt pavement in a physical model of a tropical city street canyons. Therefore, a scaled outdoor experiment was conducted, and a numerical simulation model, using ENVI-met software, was used to investigate the spatiotemporal variation of air and pavement surface temperature, in urban (1:1) and reduced (1:15) scales. For studies on the surface temperature of pavements, within the temperature range of 12 ºC to 37 ºC, it is recommended to calibrate physical models using as input, data derived from numerical simulation models, yielding a mean absolute percentage error (MAPE) of 4.9%. For estimating data in real-world urban scale, within the air temperature range of 15 ºC to 37 ºC, it is proposed to use output data from simulated models in ENVI-met, that presented a mean absolute error (MAE) of ± 0.59 or physical models (MAE = ± 0.66). These results would be useful for the development of urban surface temperatures parametrizations.

A scale development study on the perception of the sustainable urban environment

AbstractIn order to assess sustainability, it is necessary to monitor social conditions on environmental, global, national and urban scales. Therefore, the objective of this study is to develop a scale to determine PSUE. In addition, it is also examined whether there is a significant difference between the components that determine the perception of sustainable urban environment according to the characteristics of the participants. For this purpose, Exploratory Factor Analysis, Spearman–Brown correlation test, Cronbach alpha test, Mann–Whitney test and Kruskal–Wallis test are implemented. According to the findings acquired, it has been detected that the components that determine PSUE are “spatial strategies related to sustainable environment and transportation”, “spatial strategies related to ecological sustainability and solid waste management” and “spatial strategies related to social and economic sustainability” respectively. In this study, differences have been identified in the perspectives on social and economic sustainability strategies according to gender. However, participant’s viewpoints regarding strategies related to sustainable environment and transportation, as well as ecological sustainability and solid waste management, are similar. Additionally, significant differences exist in social and economic sustainability strategies based on the age of the participants. The urban or rural character of the settlement where the students families live or grew up influences their perspectives on strategies based on social sustainability and economic relations.

Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: The case of Prague

Extreme heat in urban areas has a severe impact on urban populations worldwide. In light of the threats posed by climate change, it is clear that more holistic and people-oriented approaches to reducing heat stress in urban areas are needed. From this perspective we aim to identify and compare thermal hotspots and places with favourable thermal conditions, based on three different methods – thermal walk, participatory-based cognitive mapping, and remote sensing in a Central European city. Although major hotspots in large low-rise development zones were identified by all three methods, the overall agreement between on-site thermal sensation votes, cognitive maps and surface temperatures is low. In the urban canyon of compact mid-rise and open mid-rise development, the thermal walk method proved to be useful in the identification of the specific (parts of) streets and public spaces where citizens can expect thermal discomfort and experience heat stress, e.g. crossroads, arterial streets with a lack of greenery, north facing unshaded parts of streets, and streets with inappropriate tree spacing. Cognitive maps on an urban neighbourhood scale are not specific enough on a street level; however, as a supplementary method they can help identify discrepancies between on-site sensations and thermal conditions. For further research on effective and cost-efficient urban heat mitigation, we suggest combining thermal walks with numerical model simulations.

The deviation characteristics and influencing factors of urban scaling laws in China: industrial and sectoral perspective

ABSTRACT When facing the complex and enormous system of cities, it is imperative to interpret urban development phenomena in a scientifically rigorous manner. Urban scaling laws describe the logarithmic relationship between the population size of a city and various systemic indicators, providing insights into the reasons behind urban population distribution patterns and scaling characteristics. While research related to urban scaling laws has garnered increasing attention, current studies often focus on revealing the relationships between urban size and economic, social, and infrastructural indicators, neglecting investigations into the relationships between population size and industrial sector indicators. Therefore, this study approaches the topic from the perspective of industries and sectors, employing methods such as ordinary least squares regression and comparative analysis to identify the deviation characteristics of urban scaling laws in China. Additionally, the XGBoost + Shap value modeling method is utilized to discern the factors influencing these deviations. The study findings indicate that from 1988 to 2019, China’s secondary industry scaling index exhibited superlinear growth, while the tertiary industry scaling index demonstrated linear growth, with significant differences observed across various industries. The range of deviation in China’s industry scaling index is relatively large. Among the variables considered, the growth rate of gross domestic product (GDP) exerts the strongest influence on the industry scaling index, followed by urbanization growth rate and urbanization rate. Factors such as population density, per capita national income, and the proportion of secondary industry to GDP also impact industry scaling indices. The research reveals disparities in urban development and suggests responses through urban policy interventions, thereby providing a basis for urban planning and policy formulation.

Spatiotemporal Characterization of the Three-Dimensional Morphology of Urban Buildings Based on Moran’s I

The three-dimensional morphological analysis of urban buildings constitutes a pivotal component of urban planning and sustainable development. Nevertheless, the majority of current research is two-dimensional in nature, which constrains the comprehensive understanding of urban spatial–temporal evolution. The existing body of three-dimensional studies frequently fails to consider the temporal dimension of architectural change and lacks a detailed examination of micro areas such as communities and streets. In order to accurately identify the patterns of spatial–temporal evolution in urban architectural morphology, this study focuses on the Yau Tsim Mong District in Hong Kong, utilizing three-dimensional data. By innovatively integrating temporal factors, constructing a spatial–temporal weight matrix, and applying the spatial–temporal Moran’s I, this study conducts an in-depth quantitative analysis of Coverage, Staggeredness, and Duty Cycle at the community scale, neighborhood scale, and urban scale. From 2014 to 2023, the global spatial–temporal Moran’s I of key urban morphology indicators in Yau Tsim Mong District has exhibited a marked increase, underscoring the close interrelationship and significant optimization between urban morphology and overall development. The findings illustrate that urban architecture is undergoing a process of agglomeration and high homogeneity, with strategic shifts emphasizing efficient spatial utilization and refined design. The analysis at the neighborhood scale is of particular importance, as its independent and complete spatial structure effectively captures local dynamics, revealing high-value agglomeration and low-value dispersion characteristics. This suggests that buildings in the Yau Tsim Mong District are being constructed in a more compact manner at the neighborhood level, which reflects the precision and efficiency of urban planning and the rationality of spatial planning. These significant findings provide valuable references for the development planning and governance of sustainable cities. They enhance urban governance capabilities and promote the optimization of urban development strategies, ensuring steady progress on the path of efficiency, harmony, and sustainability.

Multi-objective optimization of buildings in urban scale for early stage planning and parametric design

Passive design of buildings in early design stage is essential for energy efficiency and sustainability. In this study, a streamlined tool for parametric study and passive design of buildings in urban scale considering thermal loads and natural lighting performance is presented, which markedly enhances the efficiency of early-stage urban planning. The tool is comprised of three sub-models, including (1) degree-month based annual building thermal load prediction model, (2) simplified climate modification model that takes into account the effects of building layout on solar heat gain and urban heat island, (3) multi-objective optimization algorithm NSGA-II, which optimizes both energy-saving and natural lighting performance. Model validation results show that this tool has an average error of 11.44% and 15.82% in simulating building thermal load compared with Dragonfly and CitySim, and has an average error of 9.9% in predicting average daylight factor compared with Radiance. Multiple optimization scenarios and sensitivity analysis in 10 typical climatic cities were conducted. It is found that in cold and hot climatic cities, it is more beneficial to set energy target as the optimization objective, meanwhile, establishing natural lighting as a constraint, rather than solely minimizing thermal loads. In contrast, the inherently lower thermal loads of buildings in milder climatic cities help to achieve both energy-saving and natural lighting goals, and it is also found that the increased thermal loss due to a higher window-to-wall ratio can be offset by employing thicker wall and roof insulation or by improving building layouts.

Energy consumption of UAE public schools. Mapping of a diversified sector assessing typology, conditions, and educational systems

The UAE school sector impacts several Sustainable Development Goals of the UAE Vision 2021. The sector is designed according to a standardized system of Model buildings, repeated several times throughout the country, comprising facilities that are often obsolete at both functional and technological level; hence, frequently presenting poor energy performances. The paper presents the first comprehensive review of the energy consumption state-of-the-art of the public-school sector in the Emirate of Sharjah, United Arab Emirates, including the first necessary dataset for future upgrade actions. The study was completed by means of a deep mapping, functional, typological, and pedagogical analysis. The collected data of real energy consumption of 69 schools highlighted an average energy consumption of 174 kWh/m2 year, with very consistent results for each educational cycle, even if a larger variance was expected due to difference in size, typology, and working hour. More significant differences can be observed when comparing the result of school with different typologies, stressing the relevance of further investigating the standardized Model system. To quantify their correlation to the buildings’ energy consumption, linear regression analyses were conducted on a number of notable factors, such as building age, net floor area, roof area, shape coefficient, number of students, number of classrooms and labs, presence of gyms and multipurpose halls, and implementation of digital technologies. Schools’ capacity/occupancy ratio, net floor area and roof area, and the use of energy-demanding devices for novel smart learning techniques were found having the highest impact. The tested multiple regression model, which displays a marginal error of 5–30 % when applied to single buildings and 9 % when used to assess larger clusters, could facilitate the estimation of the energy consumption of schools and support stakeholders’ decision making. The outcomes of the study can assist governmental agencies in elaborating suitable refurbishment policies for the energy and functional upgrade of the educational sector, both at the building and urban scale, and school administrator in tackling appropriate actions toward the schools use and resources optimization.

Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning

Efficiently predicting the seismic response of urban building clusters is essential for preemptively identifying potential seismic hazards prior to an earthquake and optimizing resource allocation post-event. However, complete information of buildings at a city scale is generally un-accessible or non-existent. Existing methods struggle to reconcile low information demands, high computational accuracy, and computational efficiency. This paper proposes a fast prediction method for structural seismic time-history responses that combines deep learning methods with easy-getting structural parameters at an urban scale. An end-to-end network with adaptive multilevel fusion output is designed, which incorporates the autoencoder concept for predicting the structural seismic time-history responses based on ground motions records and five easy-getting structural parameters. The models are compared and optimized considering the training hyperparameters and network architecture, resulting in an optimized model with low complexity that provides valuable reference values for structural seismic response. Besides, the proposed model is applied to four actual buildings with different construction time, occupancy types, and floor sizes, demonstrating its good prediction performance and significant computational advantages comparing to the universally used MDOF method.

Measuring solar radiation and spatio-temporal distribution in different street network direction through solar trajectories and street view images

Under the dual pressures of rapid urbanization and global warming, the management of solar radiation has become an urgent issue to address. In this context, the potential regulatory role of urban street networks on solar radiation has garnered widespread attention. However, existing research lacks an understanding of the impact of street direction. By utilising street view data and solar trajectory simulation technology, it becomes possible to facilitate the revelation of spatiotemporal differences in solar radiation across streets with different direction. The results show that solar radiation is generally lower on north–south streets than on east–west streets. In terms of spatial differences, the average solar radiation in the fifth ring of Beijing is 25.38% higher than that in the second ring. Temporally, the average solar radiation in August, the highest month, is 15.98% greater than in October, the lowest month. Additionally, solar radiation on east–west street direction shows more intense variations in summer. A periodic variation in solar radiation was also discovered in relation to street angles, with different frequencies and amplitudes at 30° and 180°. This study is the first to focus on the directional attributes of roads at an urban scale. By utilising 100,000 street view images, it calculates and analyses the spatiotemporal distribution of solar radiation in Beijing during summer across different direction. The findings provide new insights into the relationship between the distribution of solar radiation and the morphology of urban roads.