Simulating the urban spatial structure with spatial interaction: A case study of urban polycentricity under different scenarios

Polycentric urban development is gaining momentum in both scholarly research and real-life practice. This brings new demand for planning support systems to simulate and analyse the urban spatial structure in terms of polycentricity under various urban policy scenarios. With the help of emerging urban data, urban simulation techniques, and network science, this study proposes a workflow to simulate the urban spatial structure with spatial interaction as a part of the planning support system. Using Singapore as a case study, this study has explored the resulting urban spatial structure with four employment distribution strategies. The results suggest that planning practices impact urban spatial structure and its spatial interaction by redistributing urban morphological elements, such as employment in this study. Also, our results show that the physical urban spatial structure and spatial interaction are closely related. These results reinforce the role of urban planning practice to achieve a more sustainable and coherent urban built environment. Through this empirical evidence, our workflow exemplifies the potential of the planning support system to help urban planners and governments understand their urban policy regarding urban polycentricity

Measuring polycentric urban development : the importance of accurately determining the ‘balance’ between ‘centers’

In recent years, much research has been devoted to developing appropriate analytical frameworks to capture polycentric urban development (PUD). In a recent contribution to this journal, Bartosiewicz and Marcińczak (2020) present what is arguably the most comprehensive, comparative review to date of the degree to which different analytical frameworks produce consistent results. The purpose of this research note is to show why we believe parts of Bartosiewicz and Marcińczak's (2020) findings need nuance and qualification. Our starting point is that a useful comparison between different studies and measurement frameworks needs to consider the relevance of consistency in several key dimensions, two of which are particularly pertinent here: (1) the careful specification of what constitutes a ‘center’ in a polycentric urban system, and (2) the identification of the ‘balance’ between centers as a measure of the degree of polycentricity. Two brief empirical analyses of the degree of morphological polycentricity in Polish NUTS-3 areas and the Chinese city-regions along the ‘Yangtze Economic Belt’ are included. Finally, suggestions are provided to facilitate future comparative analyses of PUD

Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning

The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development

A spacematrix and clustering approach to understanding the morphology of Singapore’s Housing Development Board (HDB) estates

Urban morphology profoundly influences city planning and experiences significant transformations as cities evolve. This paper investigates paradigm shifts in block-level planning through a case study of Singapore, a city celebrated for its precision in urban planning and swift transformation. Integrating urban morphology theories with empirical data, we explore Singapore’s block-level urban form across various stages of development. Utilising a Spacematrix approach alongside a clustering analysis of urban blocks, we categorise Singapore’s towns into four distinct clusters: Suburban, Balanced Mix, Dense Urban, and Vertical Growth, each reflecting unique density patterns and building forms. This clustering reveals how Singapore’s planning ideologies have transitioned from maximising space utilisation to prioritising sustainability and quality of living. This signifies a paradigm shift towards a comprehensive and inclusive urban design ethos. The paper contributes to the urban planning discourse by underscoring the technological advancements, especially with merging spatial data and GIS, in shaping modern urban analytics and planning. The insights from the clustering analysis enhance our understanding of Singapore’s exceptional urban path and offer valuable perspectives for other metropolises navigating the complexities of urban expansion and sustainability

9-(1,1-Dimethyl-3-oxobut­yl)adenine

The title compound, C11H15N5O, crystallizes with two independent mol­ecules in the asymmetric unit, both of which contain essentially planar imidazole and pyrimidine rings [maximum deviations = 0.002 (2) and 0.026 (2) Å, respectively, for the first mol­ecule, and 0.001 (2) and 0.025 (2) Å for the second]; the dihedral angles between the rings are 2.1 (2) and 1.7 (2)° in the two mol­ecules. The crystal structure is stabilized by inter­molecular N—H⋯N hydrogen bonds, defining chains along a, which are further linked by weak inter­molecular π–π contacts [centroid centroid distance = 3.7989 (16) Å] into planes parallel to (01)

Machine learning-based characterisation of urban morphology with the street pattern

Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns

Characterization of the duck enteritis virus UL55 protein

<p>Abstract</p> <p>Background</p> <p>Characteration of the newly identified duck enteritis virus UL55 gene product has not been reported yet. Knowledge of the protein UL55 can provide useful insights about its function.</p> <p>Results</p> <p>The newly identified duck enteritis virus UL55 gene was about 561 bp, it was amplified and digested for construction of a recombinant plasmid pET32a(+)/UL55 for expression in Escherichia coli. SDS-PAGE analysis revealed the recombinant protein UL55(pUL55) was overexpressed in Escherichia coli BL21 host cells after induction by 0.2 mM IPTG at 37°C for 4 h and aggregated as inclusion bodies. The denatured protein about 40 KDa named pUL55 was purified by washing five times, and used to immune rabbits for preparation of polyclonal antibody. The prepared polyclonal antibody against pUL55 was detected and determined by Agar immundiffusion and Neutralization test. The results of Wstern blotting assay and intracellular analysis revealed that pUL55 was expressed most abundantly during the late phase of replication and mainly distributed in cytoplasm in duck enteritis virus infected cells.</p> <p>Conclusions</p> <p>In this study, the duck enteritis virus UL55 protein was successfully expressed in prokaryotic expression system. Besides, we have prepared the polyclonal antibody against recombinant prtein UL55, and characterized some properties of the duck enteritis virus UL55 protein for the first time. The research will be useful for further functional analysis of this gene.</p

Mapping street patterns with network science and supervised machine learning

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities