An integrated model of urban spatial structure: Insights from the distribution of floor area ratio in a Chinese city

Cao, Guang-Zhong; Shi, Qiujie; Liu, Tao

doi:10.1016/j.apgeog.2016.08.010

Cited by 31 publications

(15 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While these models allow capture of multiple features of cities and their interactions, they are linear models which can capture only simple linear relationships among features of urban functions and components. Many cities, in fact, have sprawled into metropolitan areas with spatial expansion and functional diversification [28]. It is therefore not surprising that the nature of the spatial structures of cities is emergent and multifaceted with complex and non-linear relationships between features of urban components and population activities and attributes [29,30].…”

Section: Introductionmentioning

confidence: 99%

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

Fan¹,

Yang²,

Mostafavi³

2021

Preprint

View full text Add to dashboard Cite

Over decades, many cities have been expanded and functionally diversified by population activities, sociodemographics and attributes of the built environment. Urban expansion and development have led to emergence of spatial structures of cities. Uncovering cities' emergent structures and spatial gradients is critical to the understanding of various urban phenomena such as segregation, equality of access, and sustainability. In this study, we propose using a neural embedding model-graph neural network (GNN)-that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The spatial gradient in each direction from city center to suburbs is measured using clustered representations and the shared characteristics among urban areas in the same cluster. Furthermore, we show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county, allowing us to make cross-county comparisons in a quantitative way. Our GNN-based framework overcomes the limitations of previous methods used for examining spatial structures and is highly scalable. The findings reveal non-linear relationships among urban components and anisotropic spatial gradients in cities. Since the identified spatial structures and gradients capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities.

show abstract

Section: Introductionmentioning

confidence: 99%

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

Fan¹,

Yang²,

Mostafavi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To date, the monocentric concept has attracted abundant criticism, which leads to methodological advances in urban geography (e.g., Ahlfeldt, 2011; Cao, Shi, & Liu, 2016). Yet, the rest two challenges to Alonso's bid‐rent theory have received less scholarly attention.…”

Section: Introductionmentioning

confidence: 99%

Beyond the bid‐rent: Two tales of land use transition in contemporary China

Gao

Chen

et al. 2020

Growth and Change

View full text Add to dashboard Cite

The bid‐rent curve has long been recognized as a persistent law in urban and regional studies. We challenge this orthodox theory by highlighting two limitations in demystifying patterns of urban land redevelopment and rural land use utility in transitional China. Empirical evidence shows that distance still matters to both urban and rural land use transition. However, the property‐oriented and scale‐sensitive functional structure of redevelopment is not exactly what Alonso suggested. The temptation of urbanization, coupled with the resistance of rural revitalization, results in an inverted bid‐rent curve in terms of the utilization of rural housing land. The dual‐track nature of both urban–rural household registration (hukou) and land use systems contributes a lot for these unconventional findings. We can therefore summarize that transition of land use in both urban and rural China is largely institutionally driven, and the Chinese government is the dominant agent of creating a unique trajectory of spatial restructuring.

show abstract

“…A variety of studies have been done on urban structure, and significant urban elements are recognized as forming and influencing the urban structure, such as transportation [7,8], road networks [9], distribution of buildings [10], land-use patterns [11], economic performance [12], and human behaviors [13].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Structure Based on Transit-Oriented Development

et al. 2019

View full text Add to dashboard Cite

The fast development of urbanization has led to imbalances in cities, causing congestion, pollution, and urban sprawl. In response to the growing concern over the distribution of demand and supply, a more coordinated urban structure is addressed in comprehensive planning processes. In this study, we attempt to identify urban structure using a Network–Activity–Human model under the Transit-Oriented Development (TOD) concept, since TOD is usually regarded as an urban spatial planning tool. In order to explore the strengths and weaknesses of the urban structure, we define the TOD index and unbalance degree and then classify the urban areas accordingly. We take the city of Beijing as a case study and identify nine urban types. The results show a hierarchical urban structure: the city center covers most of the hotspots which display higher imbalances, the surroundings of the city center are less developed, and the city edges show higher potentials in both exploitation and transportation development. Moreover, we discuss the extent to which the spatial scale influences the unbalance degree and apply a sensitivity analysis based on the goals of different stakeholders. This methodology could be utilized at any study scale and in any situation, and the results could offer suggestions for more accurate urban planning, strengthening the relationship between TOD and spatial organization.

show abstract

An integrated model of urban spatial structure: Insights from the distribution of floor area ratio in a Chinese city

Cited by 31 publications

References 63 publications

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

Beyond the bid‐rent: Two tales of land use transition in contemporary China

Identifying Urban Structure Based on Transit-Oriented Development

Contact Info

Product

Resources

About