Urban Form Research Articles

Sustainability in Action: Green Infrastructure as a Marker of Sustainable Urban Development

The current form and pace of urbanization is an ongoing threat to sustainable urban development. As these problems span social, environmental, and economic realms, it is imperative that any proposed solution is able to address these problems holistically. Although a number of silver-bullet solutions such as electric vehicle adoption have been introduced, they do not respond to the problems with an integrated approach. Tackling this problem will require a solution that can be changed and applied on a case-by-case basis. Therefore, the development of green infrastructure is the most efficient solution when responding to the problems threatening urban sustainability. The purpose of this paper is to argue for the efficacy of green infrastructure methods by analyzing the evidence provided about factors like temperature and pollution levels in green cities as compared to other proposed solutions, and to explore critiques of green infrastructure and discuss their potential improvements. The research provided contains a mixture of case studies, participant surveys, and other scientific findings and has the potential to make green infrastructure a priority in the future planning of cities.

Exploring the Impact of Urban Morphology on Building Energy Consumption and Outdoor Comfort: A Comparative Study in Hot-Humid Climates

Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology’s role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates.

The evolution of traditional ancient towns in South China: Tangjiawan Ancient Town in Zhuhai city

ABSTRACT Based on the urban morphology theory of the Conzen School and the actual situation of the Tangjiawan Ancient Town, a morphological process analysis of the Tangjiawan Ancient Town was conducted. The end result is a visualization of the process of urban form evolution. The first objective is to describe the morphological framework of Tangjiawan Ancient Town and the morphological changes that occurred during its development. The current urban landscape is divided into units by superimposing morphological units, building types, and land use. The results of the division also reflect the morphological development, structure, and compositional characteristics of the ancient city of Tangjiawan. Using Tangjiawan Ancient Town in Zhuhai city as an example, on-site research, historical evidence, urban morphology analysis methods, and interviews were used to elaborate and analyze the spatial structure and evolution process of traditional ancient towns in South China. In particular, morphological units, building types, and land use are included. The morphological map of the Tangjiawan ancient town is inconsistent with the current economic-oriented development of ancient town planning. This shows that current planning is less concerned with the physical form of architectural heritage, which may lead to further damage to the landscape of the ancient town. This study provides a strong reference for the follow-up planning of ancient town protection of the same type from the perspective of morphology and area and provides ideas for the subsequent preparation of protection plans.

Urban Morphology Influencing the Urban Heat Island in the High-Density City of Xi’an Based on the Local Climate Zone

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

Compact city and urban planning: Correlation between density and local amenities

Compact city and urban planning: Correlation between density and local amenities

An Analysis on Wakability of Gangnam’s Superblocks Based on Urban Form - Comparative Study of Gangnam with Portland and Manhattan

An Analysis on Wakability of Gangnam’s Superblocks Based on Urban Form - Comparative Study of Gangnam with Portland and Manhattan

Comparison of the distributions of centrality indices: Using spatial big data to understand urban spatial structure

This article seeks to estimate various centrality indices based on spatial big data in terms of urban form, function, and location potential within a metropolitan area; it also comparatively analyzes the morphological differences in urban spatial structure between the centrality indices. As a result of estimating the centrality index for each dimension related to the urban spatial structure of the Seoul metropolitan region based on big data, the differentiated distribution patterns of urban centers were confirmed according to the characteristics of available big data by a quantitative and three-dimension morphological comparison methodology. The Mantel tests showed statistically significant mutual correlations between all centrality indices; however, the Passing–Bablok regressions revealed that the location potential centrality defined as Hansen-type accessibility was significantly different from the two centrality indices of the as urban form and function dimensions. The centrality of various dimensions in this paper includes not only the spatial characteristics of detailed spatial units but also the temporal characteristics theoretically. These results imply the need for comprehensive research to be carried out on urban spatial structure and centrality in accordance with the polycentric pattern of spatial structure in metropolitan areas.

Towards human-AI collaborative urban science research enabled by pre-trained large language models

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, sense public sentiment and so on. They have expanded the breadth of urban research in terms of content, increased the depth and efficiency of the application of multi-source big data in urban research, and enhanced the interaction between urban research and other disciplines. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.

Origin of urban form large siberian city irkutsk

The problem: the disclosure of the laws and features of the development of a large Siberian city will help to understand both its structure and the process of formation of a city-numerous form. The generation of the city involves the natural basis, the population in its pro-strange form, which is expressed in the growth of populated cells of the city. People satisfy the needs for housing, in places of employment, in social services, in a high-quality environment. The historical process of becoming an urban planning form is complex and contradictory. Currently, there is no formula that reveals the dependence of the urban planning form on the totality of development arguments: state policy, the interests of developers, the interests and needs of the population, contradictions that lie in the nature of the city’s investment. Purpose: to identify the prerequisites for explaining the urban planning form of Irkutsk by natural, historical, economic, socio-cultural and religious factors. Methods: identification of city-forming factors, historical process of seeds or primary cells of the planning structure, ways to increment this cell tissue along the axes of the planning framework, analysis of the evolution of the street network, forms of planning packaging of different areas of the city with different genetic codes. Results: the main regularities of origin of a town-planning form of the city of Irkutsk are revealed.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

The relationship between urban form and land-use regulation in China: the case of Nanjing

Urban form is shaped by numerous factors interacting across various social strata, notably influenced by the dynamics of economic development and urban policy. Urban planning policies related to land use play a vital role in the change and management of urban form. In this context, this study examines the influence of land use regulations on urban form in China, using Nanjing as a case study. We investigate relevant urban planning policies at national, provincial, and local city planning administration levels. Depending on their impact on urban form, these policies can be classified as having a direct impact, an indirect influence or no effect, predominantly encompassing mandatory land use conditions, building layout and guiding principles for urban landscape organization. Focusing on land use regulations at the street- block and plot scale, we established a morphological model adhering to these regulations and a correlation evaluation chart. This correlation was validated using 142 plots and 5 street blocks in Nanjing’s historical city centre. Our findings reveal the implications of these policies on the specific physical structure of the urban form through theoretical and empirical analysis. This research significantly contributes to the method of morphological modelling and the revision of relevant urban policy and design specifications.

From theory-building to institution-building: the contributions of Anne Vernez Moudon to urban morphology

This paper examines Anne Vernez Moudon’s contributions as a pivotal figure in the field of urban morphology. Employing a combination of a comprehensive literature review, personal interviews with Moudon, and archival research, the paper explores three critical aspects: (i) her research on San Francisco during the 1970s, where she developed a typomorphological method; (ii) her comparative analysis of the three major schools of urban morphology in the 1980s; and (iii) her critical role in facilitating the inaugural meetings of the International Seminar on Urban Form (ISUF) in the 1990s. In doing so, it offers a nuanced understanding of Anne Vernez Moudon’s multifaceted impact on the field urban morphology, highlighting not only her theoretical contributions but also her role as a catalyst in the institutional evolution of the field.

What do we know about urban form?

This paper gathers fundamental ideas on urban form conceived after the mid-twentieth century. It does not aim to offer a comprehensive literature review or a general theory of urban morphology. The paper’s contribution to debate is the construction of a coherent narrative, gathering different concepts and methods (collected from a growing body of literature of varied relevance) as fundamental parts of a body of knowledge for systematic description, explanation and prescription of the physical form of human settlements. At the beginning of this narrative there is a general morphological framework proposed by M.R.G. Conzen in the mid- twentieth century (section 1). The structural nature of this framework enables insights from within (section 2) and outside the field (section 3), as well as a link into action on the urban landscape (section 4).

Illustrating the nonlinear effects of urban form factors on transportation carbon emissions based on gradient boosting decision trees

While prior studies have examined the effects of urban form on transportation carbon emissions, the exploration of nonlinear influences remains limited. This study presents an approach that transcends simple quantification of urban form's impact on transportation carbon emissions by also identifying the threshold range over which urban form variables exert their influence. Using 282 Chinese prefecture-level cities as the sample, this study employs gradient boosting decision trees to identify the nonlinear effects and the relative importance of urban form factors on transportation carbon emissions. We find that urban form factors jointly account for 31.32 % of the predictive power in estimating transportation carbon emissions after controlling for transport facilities, socioeconomic, and demographic factors. Urban polycentricity and transportation carbon emissions generally exhibit an obvious and complex nonlinear relationship. In addition, polycentricity, urban dispersion, the number of (sub)centers, and population density all have clear threshold effects on transportation carbon emissions. We further identified their effective ranges to guide urban development and land use planning.

Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning

Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address this gap, this study proposes a framework that combines planning knowledge and machine learning to identify urban block typologies in complex urban environments. The framework first classifies blocks based on their size types according to planning regulations and development types. Then, a combined hierarchical and Gaussian mixture model is used to discover clusters with complex feature data distributions. These clusters are translated into block typologies using a semantic naming system and illustrated with representative blocks. Finally, the validity of the results is tested by comparing them with other building features, such as building age. The proposed integrated framework was applied to Seoul, resulting in the discovery of 27 residential block typologies across the entire city. These typologies show significant correlations with the average building age in the blocks. The application of expert knowledge in the clustering method demonstrates higher accuracy compared to conventional clustering approaches. Overall, this framework holds significant potential for discovering urban block typologies that are consistent with planning domain knowledge and provide urban planners and designers with valuable insights into the characteristics of urban block typologies, thereby helping decision-making regarding zoning regulations, urban design, and urban development strategies.

Thermal comfort study of urban waterfront spaces in cold regions: Waterfront skyline control based on thermal comfort objectives

In waterfronts, thermal comfort is affected by climate and urban morphology. Existing studies on waterfront thermal comfort have mainly focused on warm and hot regions, focusing on the cooling effects of water and vegetation during the summer. The research methods are mostly questionnaire surveys and simulations, whereas the urban form and climate characteristics of cold regions are rarely considered, and the results of these studies are seldom applied to design practices. This study focused on the thermal comfort of waterfronts in cold regions. Computational Fluid Dynamics, Mean Radiant Temperature, and the Universal Thermal Climatic Index were used to examine the effects of tree–water features (winter river ice and tree defoliation) on thermal comfort in cold regions, and correlation analyses were combined to screen for relevant urban morphology factors for waterfront thermal comfort. Regression analyses were conducted to understand the influence of six factors, namely, building orientation, floor area ratio, open space ratio, building height to distance between river and building ratio, standard deviation of the first building row, and vegetation concentration, on waterfront thermal comfort. After training and comparing the four thermal comfort prediction models, a genetic algorithm combined with an artificial neural network was applied to optimize the design of the urban morphology of the six waterfront blocks. The optimization improved the waterfront thermal comfort performance by 22%, 7%, 77%, 106%, 3%, and 3%, respectively.

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns.

Urban form and socioeconomic deprivation in Isfahan: An Urban MorphoMetric approach

Studies on urban deprivation date back to the 19th Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses such a gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making.

Characterizing urban spatial structure through built form typologies: A new framework using clustering ensembles

Prior research on urban form typologies has largely relied on qualitative classification methods, resulting in subjective and limited analyses. Recently, the emerging data-intensive studies often use a single clustering algorithm and parameter setting, raising concerns about the reliability of the findings. This paper introduces a novel clustering analytical framework for conducting typological studies on urban form that yield stable and reliable results. We employ clustering ensembles, which can combine multiple clustering algorithms to further provide a comprehensible output that facilitates interpretation and knowledge generation. By applying the new framework using 3D building data in Guangzhou, we identify eight typologies of urban built forms and reveal a consistent polycentric pattern across different clustering algorithms and parameter settings. The findings have implications for urban land use planning and regulations by integrating 3D representations of urban form.