Urban Morphology Research Articles

Street Puppet Theatre Shows on the Façades of Commercial Buildings as a Novel Stimulator for Social Gatherings in Smart Cities

This study investigates the innovative use of street puppet theatre performances on the façades of commercial buildings to stimulate social gatherings in smart cities. This research investigates the impact of a unique musical puppet show held throughout the 2024 holiday season in the well-known shopping mall El Corte Inglés in Madrid, Spain. This study utilises both quantitative and qualitative methodologies, such as space syntax analysis and empirical observations. It uses the UCL Depthmap 10 software program to assess urban morphology and identify integrated and segregated areas. Observations and video recordings were conducted to analyse social gatherings and behaviours during the performances. The findings reveal that these performances attract diverse audiences and foster social engagement, transforming commercial façades into vibrant public spaces. The study concludes that such innovative uses of urban architecture can significantly enhance the sociability of detached and segregated urban spaces. The research demonstrates that this particular dynamic façade not only fosters social sustainability by creating lively urban spaces but also contributes to economic sustainability by attracting potential customers to commercial centres. This research advances the discourse on smart city development and public space utilisation, offering valuable insights for urban planners and policymakers on leveraging interactive urban façades to enhance public engagement and revitalise underutilised spaces.

Investigating urban morphological drivers of household water use in developing economies: A structural equation model approach

Urban populations are snowballing, increasing household (HH) water consumption. The growing housing density, the expanding infrastructure, and altered land use patterns place increasing pressure on the water supply and usage. This makes it harder for urban HHs to obtain clean water and worsens water scarcity, with infrastructural and environmental implications for highly populated metropolitan areas. This study aims to quantitatively evaluate the impact of urban morphology on household water use in a developing economy using a Structural Equation Model (SEM), specifically within the growing urban centres surrounding Kolkata in South Bengal. The primary hypothesis projects an increase in urban morphological indicators and HH characteristics that significantly affect water use. The findings reveal that increases in urban morphological indicators are associated with a 34.2% decrease in HH water use, highlighting the importance of urban planning strategies that emphasize compact and diverse urban forms. This work highlights new ways of building cities that consider morphological aspects and allow cities to expand in a way that conserves HH water while being resilient to rapid urban change.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing “ED of building footprint” and increasing “ED of impervious surface” can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

Assessing urban air pollution dynamics: the impact of traffic emissions and urban morphology in Lecce and Bari, Italy

Urban air pollution, exacerbated by vehicular emissions, poses a significant health threat, especially in densely populated areas. This study, part of the PNRR Italian National Center for Sustainable Mobility (MOST), aims to assess the impact of vehicular pollutants in urban environments. Focusing on Lecce and Bari (Italy), the research integrates data from dispersion models and meteorological analyses to evaluate pollutant concentrations. Preliminary analysis of eight urban areas in Lecce and six in Bari reveals trends in PM10 concentrations influenced by urban morphology and wind dynamics. These findings underscore the essential role of sustainable mobility strategies in mitigating urban air pollution and promoting healthier urban environments.

The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO2 emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO2 emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.

Exploring the relationship between preferred routes for walking exercise and green space layout in a dense urban area: a series of poisson regression models

ABSTRACT The literature considers urban green spaces as a key to promoting physical activity and health. Despite the increasingly recognized walking exercise promotion benefits of urban green spaces, the relationship between walking exercise preferences and green space layout remains unclear. We extracted preferred routes for walking exercise and measured the route heat using a one-year shared data of walking exercise from a mobile outdoor assistant app in the old city of Fuzhou, China. We then evaluated green space layout features within a 500-m buffer zone along the preferred routes in dimensions of scale, form, and function. The relationship between the route heat and green space layout features was analyzed using a series of poisson regression models, with the influence of urban morphology and the built environment allocation being controlled. Results showed that there existed a significant association between preferred routes for walking exercise and the combination of green space layout, urban morphology, and the built environment allocation; the route heat had positive associations with NDVI and comprehensive park area density, and showed negative associations with patch shape index and patch density. This study illustrates the necessity for targeted intervention strategies to promote the development of walkable and healthy urban areas.

Deciphering the effects of 2D/3D urban morphology on diurnal cooling efficiency of urban green space

Urban green spaces (UGS) have emerged as a main nature-based solution proven to effectively cool urban areas, and cooling efficiency (CE) serves as a prominent indicator for assessing the performance of UGS. Yet few studies have explored the diurnal dynamics of CE and the comprehensive effects of both 2D and 3D urban morphology on CE over a 24-h period. In this study, we employed diurnal land surface temperature (LST) data from Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to delve into the diurnal CE of UGS within the urban core area of megacity Paris, France. Meanwhile, using high-resolution land cover data, digital height model, and explainable machine learning models, we elucidated the intricate relationship between various 2D and 3D urban morphology metrics and CE during a 24-h cycle. In summary, city-scale continuous monitoring revealed diurnal CE variations, with values ranging from 0.008 °C to 0.13 °C and a standard deviation of 0.04 °C. On an average of a day, the relative importance of the 2D and 3D urban form indicators was 77.25 % and 22.75 %, respectively. Enhancing the dispersion of trees or improving the integrity of non-tree vegetation patches proves to be an effective strategy for augmenting CE. The results can be further used in urban planning to improve urban thermal resilience and adaptability.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Multi-scale correlation analysis between geometric parameters and solar radiation in high density urban environment——Case study in Nanjing

This study explores the impact of multi-scale urban morphology on solar radiation in high-density environments, focusing on Nanjing as a case study. Using Grasshopper, the research decomposed urban models into four scales—district, block group, block, and plot—and analyzed eight morphological indicators, including site coverage (SC), floor area ratio (FAR), and block surface ratio (BSR). Solar radiation was simulated for the entire year, the summer solstice, and the winter solstice. Key findings reveal that different scales exhibit varying influences on solar radiation. At the district scale, BSR shows the highest correlation with annual solar radiation (R2 = 0.96), while at the neighborhood cluster scale, the minimum distance between buildings (DBmin) is most correlated (R2 = 0.7). At the block scale, BSR significantly correlates with summer solar radiation (R2 = 0.55). At the plot scale, no single indicator strongly correlates with solar radiation, but a combination of indicators is more relevant. These findings enable rapid solar performance assessments in urban design, promoting efficient solar energy use.

Meta-Connectivity in Urban Morphology: A Deep Generative Approach for Integrating Human–Wildlife Landscape Connectivity in Urban Design

Meta-Connectivity in Urban Morphology: A Deep Generative Approach for Integrating Human–Wildlife Landscape Connectivity in Urban Design

Assessing the Impact of Urban Morphologies on Waterlogging Risk Using a Spatial Weight Naive Bayes Model and Local Climate Zones Classification

Assessing the Impact of Urban Morphologies on Waterlogging Risk Using a Spatial Weight Naive Bayes Model and Local Climate Zones Classification

The Influence of Three-Dimensional Building Morphology on PM2.5 Concentrations in the Yangtze River Delta

The rapid urbanization of urban areas in China has brought about great variation in the layout of cities and serious air pollution. Recently, the focus has been directed toward understanding the role of urban morphology in the generation and spread of atmospheric pollution, particularly in PM2.5 emissions. However, there have been limited investigations into the impact of three-dimensional (3D) features on changes in PM2.5 concentrations. By analyzing a wealth of data on building structures based on a mixed linear model and variance partition analysis in the Yangtze River Delta throughout 2018, this study sought to examine the associations between PM2.5 concentrations and urban building form, and further compared the contributions of two-dimensional (2D) and 3D building features. The findings revealed that both 2D and 3D building forms played an important role in PM2.5 concentrations. Notably, the greater contribution of 3D building forms on PM2.5 concentrations was observed, especially during the summer, where they accounted for 20% compared to 7% for 2D forms. In particular, the building height range emerged as a crucial local factor affecting PM2.5 concentrations, contributing up to 12%. Moreover, taller buildings with more variability in height were found to aid in the dispersion of pollution. These results underscore the substantial contribution of 3D building morphology to PM2.5 pollution, contrasting with previous studies. Furthermore, compact buildings were linked to lower pollution levels, and an urban landscape characterized by polycentric urban structures and lower fragmentation was deemed more favorable for sustainable urban development. This study is significant in investigating the contribution of 3D morphology to PM2.5 and its importance for pollution dispersion mechanisms. It suggests the adoption of a polycentric urban form with a broader range of building heights in urban planning for local governments in the Yangtze River Delta.

Characterizing the seasonal relationships between urban heat island and surface energy balance fluxes considering the impact of three-dimensional urban morphology

Urbanization has disrupted the energy balance of natural surfaces, leading to the formation and intensification of urban heat island (UHI) phenomenon. Current research on the relationship between surface energy balance (SEB) and UHI mainly focuses on either a macro perspective of large-scale regions or a micro perspective based on numerical microclimate simulations. However, there are still relatively few studies conducted on the local scale of cities. Here, we investigated the relationships between seasonal SEB and land surface temperature (LST) based on local climate zones (LCZ) and explored the impact of three-dimensional (3D) urban morphology on SEB. Our results revealed that: (1) urban building spaces have relatively higher LST, sensible heat flux, and storage heat flux, but lower net radiation and latent heat flux, compared to urban blue and green spaces; (2) among LCZ built types, the compact and large low-rise buildings have high LST and heat-inducing energy fluxes, while the compact and open high-rise buildings exhibit the opposite situation; (3) the component proportions of SEB fluxes exhibit a more significant linear correlation with UHI intensity compared to the relative differences between SEB fluxes; and (4) 3D building morphology has a higher relative importance in influencing the variations of surface energy balance ratio (SEBR) components than 3D vegetation morphology. Specifically, building height has the greatest impact on the seasonal variations of SEBR components, while the 3D urban greening ratio has relatively high importance among vegetation morphology. These findings enable us to better consider local UHI effect from the perspective of SEB.

Situating spatial determinism in urban design and planning for sustainable walkability: a simulation of street morphology and pedestrian behaviour

Streets and roads have been constantly identified as key spaces to improve walkability and—by extension—the entire urban environment. Such improvements lie on planning and design theories that are often supported by qualitative evidence and as such, tend to remain highly speculative on how street design influences walkability in more deterministic ways. Drawbacks in further exploring this street-walkability correlation are partly defined by an explicit rejection to ‘spatial determinism’ in urban morphology and social sciences, but also because combining differing epistemological approaches for understanding social processes linked to the physical aspects of the space is complex. In this paper, it is proposed that qualitative (social-based) theories of the space and quantitative (positivist) understandings of people’s behaviour can complement each other to elaborate upon ‘spatial determinism’ with focus on streetscapes and pedestrian behaviour. By using Agent-Based Modelling (ABMs), morphological components of streets linked to population characteristics are analysed to understand walkability from the perspective of spatial determinism. The findings suggest that the street settings directly affect pedestrian behaviour, and that changes in the built environment result in changes on walkability patterns. Ultimately, the modelling exercise shed light on street design and walkability, but also on wider debates on how urban design and social processes correlate in more deterministic and, therefore, predictable ways.

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Development of a morphology-based wind speed model in the urban roughness sub-layer

Understanding near-surface wind variations in urban environments is essential for a wide range of applications, including urban weather forecasting and wind impact assessment. In this study, we introduced a novel model for mean wind speed profiles within the urban roughness sublayer based on theoretical analysis. To calibrate the model's coefficients, we conducted large-eddy simulations of airflow over a variety of idealized urban configurations. The resulting expression effectively captures wind speed variations across different urban morphologies. Subsequently, a regression modeling was employed to identify the relationships between building morphological parameters and these coefficients. This highlights the pivotal role of using building morphology to predict near-surface velocity profiles. The proposed model also yields significantly more accurate wind speed predictions within the urban canopy layer than traditional exponential profiles. The findings in the present study lay a more robust foundation for assessing urban wind conditions and improving urban-scale weather forecasts.

Outdoor mobile broadband performance analysis in Malaysia, Singapore, and Thailand

This paper presents the measurement of mobile broadband (MBB) performance and specifies insightful knowledge about 3G and 4G technologies of dense urban and urban morphologies in Malaysia, Thailand, and Singapore. This study will also contribute to planning efficiently for the deployment of 5G network in these three countries. The data to characterize and analyze the MBB performances are gathered through a driving test (DT) conducted in areas of the three countries for two months starting from January to February by utilizing an unbranded smartphone named Samsung Galaxy S6. The performance data in our MBB analysis was collected based on diverse national mobile network operators (MNOs) in outdoor areas of the selected states in the mentioned countries. Four different performance metrics (coverage, speed, latency, and satisfaction) were investigated to assess the network performance for one of MBB's services that contain video streaming services. Within video services, two distinct resolutions including 720p (low) and 1080p (high) for an identical YouTube video were loaded alternately by the same smartphone. The research is meant to mimic real scenarios in the case of the unlocked smartphone to any technology during the time of loading a video, which contributes to knowing the network coverage of 2G, 3G, and 4G technologies in the chosen areas. The analysis of the 2G technology is excluded in the current paper because of the limited data amount of 2G. The MBB performance measurement results in the tested areas revealed that, on average, the 4G network outperformed the 3G network according to the investigated four performance indicators. Therefore, across the tested territories, the 4G technology offered better network coverage compared to the 3G technology. The vMOS scores for 4G technology across video services were more than 3, whereas the 3G technology has achieved scores smaller than 3. Also, the 4G network has achieved E2E RTT-Ping server latency improvements of 89.90%, 59.30%, 67.48%, and 58.04% compared to the 3G network in the urban and dense urban of Klang Valley, Malaysia, urban of Singapore, and urban of Thailand, respectively. Moreover, it is found that 4G technology can provide improvements up to the factors of 1.29, 1.61, 1.98, and 1.35 in the download speed for video streaming compared to the 3G technology for urban and dense urban of Klang Valley, Malaysia, urban of Singapore, and urban of Thailand, respectively.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.