Interplay between geo-population factors and hierarchy of cities in multilayer urban networks

Макаров, В. В.; Hramov, Alexander E.; Kirsanov, Daniil V.; Maksimenko, Vladimir A.; Goremyko, Mikhail V.; Іванов, А. В.; Yashkov, I.; Boccaletti, Stefano

doi:10.1038/s41598-017-17576-8

Cited by 14 publications

(4 citation statements)

References 37 publications

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“…For urban and regional planning, primal planar graphs supplemented with contextual built environment data can support various applications and use cases. These networks are essential in transportation planning for assessing traffic flow, identifying bottlenecks, and optimizing road networks [36][37][38] . Network population estimates can also help evaluate the accessibility of public facilities, such as schools, hospitals, or parks, empowering planners to pinpoint underserved areas and prioritise infrastructure investments [39][40][41] .…”

Section: Background and Summarymentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

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Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset’s strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

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Section: Background and Summarymentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

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“…Such an integrated framework to understand cities through data-powered tools sits at the edge between many different disciplines such as statistical physics, social sciences, economics, digital health and wellbeing, and engineering, to name only a few ones. e state-of-the-art applications reviewed above provide us with a promising benchmark by characterizing urban complexity in terms of multiplex networks and possibly point to the multiplex city as a computationally appropriate and conceptually scalable [195] representation of urban complexity [196], which will allow the development and deployment of new urban governance strategies, as well as the redesign of old ones [54].…”

Section: Outlooksmentioning

confidence: 99%

Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities

2021

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For decades, from design theory to urban planning and management, from social sciences to urban environmental science, cities have been probed and analyzed from the partial perspective of single disciplines. The digital era, with its unprecedented data availability, is allowing for testing old theories and developing new ones, ultimately challenging relatively partial models. Our community has been in the last years providing more and more compelling evidence that cities are complex systems with emergent phenomena characterized by the collective behavior of their citizens who are themselves complex systems. However, more recently, it has also been shown that such multiscale complexity alone is not enough to describe some salient features of urban systems. Multilayer network modeling, accounting for both multiplexity of relationships and interdependencies among the city’s subsystems, is indeed providing a novel integrated framework to study urban backbones, their resilience to unexpected perturbations due to internal or external factors, and their human flows. In this paper, we first offer an overview of the transdisciplinary efforts made to cope with the three dimensions of complexity of the city: the complexity of the urban environment, the complexity of human cognition about the city, and the complexity of city planning. In particular, we discuss how the most recent findings, for example, relating the health and wellbeing of communities to urban structure and function, from traffic congestion to distinct types of pollution, can be better understood considering a city as a multiscale and multilayer complex system. The new challenges posed by the postpandemic scenario give to this perspective an unprecedented relevance, with the necessity to address issues of reconstruction of the social fabric, recovery from prolonged psychological, social and economic stress with the ensuing mental health and wellbeing issues, and repurposing of urban organization as a consequence of new emerging practices such as massive remote working. By rethinking cities as large-scale active matter systems far from equilibrium which consume energy, process information, and adapt to the environment, we argue that enhancing social engagement, for example, involving citizens in codesigning the city and its changes in this critical postpandemic phase, can trigger widespread adoption of good practices leading to emergent effects with collective benefits which can be directly measured.

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“…The second category is based on the spatial interaction between settlements. The identification method of this category involves constructing a spatial interaction network of settlements using a spatial interaction model or actual survey data, and then identifying the spatial structure of settlements based on the strength of spatial interaction between settlements [27][28][29][30][31]. The optimization method involves selecting central towns and villages based on the network analysis method, and then determining their spheres of influence using a spatial interaction model or a Voronoi diagram [15,29,32].…”

Section: Introductionmentioning

confidence: 99%

Identification and Optimization of the Spatial Structure of Urban and Rural Settlements from a Hierarchical Network Perspective

Kong

et al. 2021

Land

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China has entered a new era of comprehensive promotion of rural revitalization and integrated urban–rural development. The identification and optimization of the spatial structure of urban and rural settlements are of great significance to the realization of rural revitalization and integrated urban–rural development. This study proposes a method framework for identifying and optimizing the spatial structure of urban and rural settlements from a hierarchical network perspective. This framework includes three parts—namely, the identification, analysis, and optimization of the hierarchical network structure of these settlements. The identification of this structure is the foundation of this study. To realize the identification, for each settlement, this study finds and retains its strongest interaction with other settlements which have a stronger capacity for development. This study used Hua County in Henan, China, as an example to verify the method framework. Results showed that the spatial structure of the urban and rural settlements in Hua County identified in this study was complete and continuous, with significant hierarchical and community characteristics. Based on this, a five-level optimization system of these settlements was constructed. This study expands the method used to study the spatial structure of urban and rural settlements from the network perspective and has a theoretical and practical significance for the optimization of the spatial structure of these settlements.

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Interplay between geo-population factors and hierarchy of cities in multilayer urban networks

Cited by 14 publications

References 37 publications

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities

Identification and Optimization of the Spatial Structure of Urban and Rural Settlements from a Hierarchical Network Perspective

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