Urban Spatial Structure Research Articles

Research on Jinhua City Sports Facilities Planning and Urban Spatial Structure Optimization

With the increasing awareness of national fitness, the planning and management of sports venues has become the key to improving the level of urban public services. Taking the POI data of sports venues in Jinhua City as the research object, this paper analyzes the spatial distribution characteristics of sports venues in Jinhua City, traffic accessibility and its relationship with housing prices. The study found that all the sports venues in Jinhua have good traffic accessibility, and there is a positive correlation with housing prices. Among them, the average housing prices in urban public sports venues and commercial fitness and leisure venues are higher. The research results provide a scientific basis for the planning and management of sports venues in Jinhua City, and are of great significance for promoting the balanced development of sports facilities and improving the quality of life of residents.

Quantifying urban spatial resilience using multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN)

In the face of rapid urbanization and increasing environmental risks, building resilient cities has become a global priority. This study introduces a novel methodology for quantifying urban spatial resilience by integrating multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN). The urban spatial resilience assessment system encompasses five critical criteria layers (urban spatial scale, structure, form, function, and network resilience) and 29 indicator layers. An empirical study of Yinchuan City, China, demonstrates the effectiveness of the MCDA-BPNN model in quantifying urban spatial resilience. The models identified significant disparities in resilience between central and peripheral urban zones, with central areas showing higher resilience scores compared to peripheral areas. This spatial pattern was most pronounced in structure, functional and network resilience. These results contribute to the field of urban environmental science by advancing the fundamental understanding of urban spatial resilience mechanisms and their spatial distribution. The study has significant implications for urban planners, policymakers, and risk management authorities, enabling data-driven decision-making and the development of targeted, resilience-oriented urban policies and strategies. The MCDA-BPNN approach offers a promising tool for comprehensive urban resilience assessment in diverse urban contexts.

Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming

ABSTRACT In the pursuit of sustainable urban development, it is imperative to address strategies for optimizing and harmonizing the configuration of urban spatial layouts, especially in response to rapid urbanization. Urban road networks play a pivotal role in shaping intricate urban spatial patterns as they intricately weave throughout the cityscape, significantly influencing land utilization, spatial arrangements, and the mobility of residents. The urban environment is a complex system teeming with multifaceted human activities. With the continuous evolution of advanced data and computational technologies, it is now feasible to construct intricate models for an in-depth exploration of urban spatial structures. In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. Within this framework, deep learning and unsupervised clustering are applied to complete the hierarchical clustering of road networks and exploit the intrinsic association between cities. Meanwhile, hierarchical clustering and natural and humanistic attributes of cities are connected to investigate the strength of different attributes on specific clusters. The research has the following results: (1) In addition to the alignment of urban road network clustering outcomes, quantitative unevenness and geographical heterogeneity are also observed. (2) Multiple attributes have a complicated impact on urban space, including city size, and socioeconomic and geographic characteristics. Among these factors, socio-economic variables emerge as the principal drivers influencing urban spatial structure. (3) The research enhances the model’s generalization capability and predictive accuracy. As attributes increase and model learning improves, both the Area Under Curve (AUC) and F-1 scores exhibit continuous enhancements, ultimately achieving error-free predictions. The study’s findings offer valuable insights into urban spatial structures and systems, supporting urban decision-makers in addressing challenges associated with urban growth.

Assessing the effects of urban green spaces metrics and spatial structure on LST and carbon sinks in Harbin, a cold region city in China

In the face of accelerated urbanization, urban green spaces (UGSs) are crucial in alleviating urban heat island effects. This study investigates the link between UGS landscape patterns, plant community characteristics, Land surface temperature (LST), and Annual carbon sequestration of plant community (ACSPC) across different seasons and scales in Harbin by remote sensing and field measurements. The results show that (1) The LST in urban built-up areas shows an increasing trend, and its correlation with UGS exhibits seasonal fluctuations. (2) Landscape pattern metrics significantly relate to LST, particularly at 1500 and 1260 m window scales. (3) In summer and winter, Aggregation index (AI) , Mean patch area (AREA_MN), Edge density (ED), Percentage of landscape (PLAND), Mean patch shape index (SHAPE_MN), Mean diameter at breast height (MDBH), Mean canopy diameter (MCD), Average carbon sequestration capacity(ACSC), and Leaf area index (LAI) are positively correlated, whereas Sky view factor (SVF) is negatively correlated with ACSPC. (4) LAI, SVF and MCD (winter) significantly affect LST. MDBH, MCD, and SVF in summer significantly influence ACSPC. This research elucidates the effect of UGS landscape patterns and plant community characteristics on urban heat islands and carbon sinks, providing vital insights for urban ecological improvement.

Exploring the spatial trade-off effects of green space on older people’s physical inactivity: Evidence from Shanghai

Given that green space patterns and population density often show opposing trends over space, this could lead to trade-off (distance/population decay) relationships. Consequently, this paper utilized the Study on Global Ageing and Adult Health (SAGE) data from Shanghai, China, to investigate whether there is a spatial trade-off in the health effects of green spaces on people’s inactivity. Our findings confirmed the trade-off effects of green spaces in reducing sedentary time, using interaction terms to highlight how the health impacts of green spaces vary across different population densities and may thus lead to inconsistent relationships between green space and health outcomes. Generally, the ability of green spaces to affect sedentary time decreased as population density increases, particularly regarding park proximity and the Normalized Difference Vegetation Index (NDVI). Our research also revealed that the trade-off in health benefits from green spaces was influenced by the type of green space. For instance, park proximity, park area, and NDVI showed trade-off effects, with higher population densities enhancing the impact of NDVI on increasing sedentary time (β = 133.871), despite NDVI itself decreasing with a rise in population density (r = -0.68). Conversely, the Green Vegetation Index (GVI) did not demonstrate such an effect. The unequal spatial distribution and composition of green space may further exacerbate health inequities. Therefore, this paper recommends that future provisioning of green spaces should take into account the local urban spatial structure.

Evaluating the Spatial Heterogeneity and Driving Factors of Sustainable Development Level in Chengdu with Point of Interest Data and Geographic Detector Model

Over the past few decades, China has undergone the largest and fastest urbanization process in world history. By 2023, Chengdu’s urbanization rate had reached 80.5%, significantly higher than the national average of 66.16%. Studying the urbanization experience of Chengdu is of great significance for optimizing urban planning policies in Chengdu and other cities in China. Although much literature has explored the urbanization process from macro and micro perspectives, studies using a top-down approach to examine urban fringe expansion are relatively scarce. This study first applies the entropy weight method to analyze the spatial-temporal evolution trends of urban development, identifying areas of imbalanced development and prominent issues. Secondly, the K-means machine learning algorithm and nightlight data are used to reconstruct and classify urban regions, and a comparative analysis is conducted with administrative divisions to further identify unreasonable areas in urban spatial distribution and structure. Finally, POI data and the geographical detector method are used to analyze the micro-driving forces in areas of imbalanced development, identifying major limiting factors and solutions. The study found that the gap between urban and rural development in Chengdu is narrowing during the urbanization process, but there is severe differentiation in the second circle of Chengdu, where economic development is accelerating but residents’ happiness is declining. Moreover, analysis based on urban nightlight data and land-use data reveals that the expansion areas on the urban-rural fringe are mainly concentrated in the second circle of Chengdu. Micro-level driving factor analysis found that the western region of the second circle has many but small urban settlements, with a dense road network but scattered functional areas. The eastern region has inefficient and extensive use of construction land. Additionally, the mismatch between student status and household registration has resulted in relatively lagging educational resource development, and high entry barriers have hindered the progress of urbanization, leading to low per capita welfare expenditure. These reasons are the main factors causing the decline in residents’ happiness, and this impact shows significant differences at different temporal and spatial scales. Encouraging innovation in research and development or education can serve as a long-term and effective driving force for promoting sustainable urbanization. This study provides valuable insights for scientifically planning sustainable urban development and promoting the urbanization process.

Polycentric spatial Structure, digital economy and urban green sustainable development

The urban spatial structure profoundly influences green sustainable development, while digital technology is instrumental in fostering the advancement of eco-friendly smart cities. This study explores whether optimizing urban spatial layout can utilize the transformative potential of the digital economy (DE) to achieve goals such as urban low-carbon construction, achieving "carbon neutrality" and promoting sustainable development. We examine the influence of polycentric spatial structure and the digital economy on urban carbon emissions (UCE) from a spatial perspective. We found that a polycentric spatial structure reduced local and neighboring carbon emissions significantly, and the expansion of the DE further amplifies this effect. Supported by digital technologies, three main pathways effectively reduce UCE: improving transportation infrastructure, facilitating industrial transformation and upgrading, and promoting market integration. Further analysis revealed that the effectiveness of these beneficial impacts is influenced by regional heterogeneity and urban scale. Moreover, cities with advanced green technologies and those situated within urban agglomerations are better positioned to leverage this dividend effect to foster green, low-carbon, and sustainable urban development. Lastly, based on our research findings, we provide policy recommendations aimed at promoting green and sustainable urban development.

Urban spatial structure and air quality in the United States: Evidence from a longitudinal approach

Previous studies on the relationship between urban form and air quality: (1) report mixed results among specific aspects of urban spatial structure (e.g., urban expansion, form, or shape) and (2) use primarily cross-sectional approaches with a single year of data. This study takes advantage of a multi-decade, longitudinal approach to investigate the impact of urban spatial structure on population-weighted concentrations of PM2.5 and NO2. Based on fixed-effect regression models for 481 urban areas in the United States spanning from 1990 to 2015, we found significant associations between various aspects of urban spatial structure and air quality after controlling for meteorological and socio-economic factors. Our results show that population density, compact urban form, circularity, and green space are associated with lower concentrations. Conversely, higher rates of urban expansion, industrial area, and polycentricity are associated with higher concentrations. For large cities (total population: 180,262,404), we found that increasing key factors from each urban spatial structure category (i.e., greenness, population density, compactness, circularity) by a modest 10% results in 10,387 (12,376) fewer deaths for PM2.5 (NO2). We recommend that policymakers adopt comprehensive strategies to increase population density, compactness, and green spaces while slowing urban expansion to reduce the health burden of air quality in US cities.

Study on the change of urban spatial structure in Three Northeast Provinces of China based on the coupling relationship between POI and nighttime light data

Identifying and measuring urban spatial structure is a prerequisite for understanding urban spatial characteristics, formulating urban development strategies and testing urban planning results. As important data sources that can visually reflect the spatial distribution characteristics of urban socio-economic and other physical elements, urban points of interest (POI) and nighttime light data play an important role in the study of urban spatial structure. In this study, the main urban areas of 36 cities of the Three Northeast Provinces (Heilongjiang Province, Jilin Province, Liaoning Province) were selected as the study area, the POI (6,553,294 points of interest) and nighttime light data from 2010, 2016, and 2022 were chosen as the basic research data, and the methods of point kernel density estimation, data griddedness, and multifactor combination mapping were used to analyze the developmental dynamics of the urban spatial structure. The study demonstrated: (1) The spatial coupling consistency of POI and nighttime light data in the main urban areas of the Three Northeast Provinces was high, and both had good applicability in urban spatial structure research; (2) POI and nighttime light values formed the spatial pattern of “axis + core − periphery” with Shenyang, Dalian, Changchun and Harbin as the core, while the coupling relationship between the POI and nighttime light data identified that the main urban areas in the Three Northeast Provinces presented a centralized agglomeration type, a decentralized grouping type, a belt combination type and a radial expansion type urban spatial structure; (3) From the perspective of changes in the coupling relationship between POI and nighttime light, most of the main urban areas of resource-mature cities, resource-regeneration cities and non-resource cities were affected by the regional development agglomeration and the “T” railway network, and the “high/medium–high/medium” area showed an expanding tendency; most of the main urban areas of resource-decline cities were affected by the lower development potential and the deprivation of economic factors by the surrounding core cities, while the “high/medium–high/medium” areas showed a contracting tendency. The results of the study can provide a scientific basis and theoretical reference for the future adjustment of urban spatial structure, planning and construction as well as resource allocation in the main urban areas of the cities in the Three Northeast Provinces.

A Spatial Semantic Feature Extraction Method for Urban Functional Zones Based on POIs

Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomplete. To consider the spatial features of points of interest when extracting semantic features of urban functional zones, this paper improves the Latent Dirichlet Allocation topic model and proposes a spatial semantic feature extraction method for urban functional zones based on points of interest. In the proposed method, an assumption (that points of interest belonging to the same semantic feature are spatially correlated) is introduced into the generation process of urban functional zones, and then, Gibbs sampling is combined to carry out the parameter inference process. We apply the proposed method to a simulated dataset and the point of interest dataset for Chaoyang District, Beijing, and compare the semantic features extracted by the proposed method with those extracted by the Latent Dirichlet Allocation. The results show that the proposed method sufficiently considers the spatial features of points of interest and has a higher capability of extracting the semantic features of urban functional zones than the Latent Dirichlet Allocation.

Monocentricity or polycentricity? The study on the enterprise environmental performance of urban spatial structure

Under the promotion of urban planning and land development policies, the urban spatial structure of China is undergoing a transformation towards polycentricity. The improvement of environmental performance during the process of urban spatial transformation is crucial for urban sustainable development. This paper uses high-resolution population grid data from LandScan for the years 2001–2019 to measure the urban spatial structure of Chinese cities. Then we match the Chinese Industrial Enterprise Database with the Chinese Industrial Enterprise Pollution Emission Database for the years 2001–2014. This paper empirically examines the impact and mechanisms of the polycentric spatial structure of Chinese cities on enterprise pollution reduction at the micro-level. The findings indicate that compared to monocentricity, polycentricity is more conducive to improving enterprise environmental performance. Mechanism tests demonstrate that the improvement in enterprise environmental performance resulting from polycentricity can be achieved through three channels: increasing urban population and density, mitigating urban environmental pollution, and promoting enterprise innovation ability. Heterogeneity analysis reveals that polycentricity is more beneficial for emission reduction among small enterprises, young enterprises and enterprises in regions with higher environmental regulations. This paper provides micro-level evidence and policy implications for further strengthening the aggregation effect of polycentricity and improving enterprise environmental performance in the future.

Subsidizing residents or companies? An equilibrium-based analysis of subsidy strategies for EV charging facilities

This study examines the charging location choice behavior of residents in relation to employer-provided charging facilities and investigates the impact of different government subsidy strategies for charging facility construction on urban spatial structure and traffic-related air pollution. Using a monocentric two-zone city model, we analyze the residential distribution and travel mode choice of urban residents under various subsidy strategies from the perspective of social planners. The government can optimize urban resource allocation through transport infrastructure investments and implementation of emission taxes to maximize the so-called residents’ utility. We observe that despite the distorting effect of employer-provided charging facilities on travel mode choice, residents still experience an enhanced utility due to the positive environmental impact resulting from reduced pollution levels associated with electric vehicles (EVs) compared to gasoline vehicles (GVs). We provide numerical evidence to show that government subsidies, whether directed towards companies or residents, can effectively enhance the adoption of EVs and improve utility. In low-density cities, it is advisable for the government to encourage and subsidize residents in building charging facilities. Conversely, in high-density cities, prioritizing subsidies for companies would be more beneficial in improving utility and expanding the coverage of charging facilities. Furthermore, in cities where resident environmental awareness is relatively low, it is recommended that the government increase subsidies to promote EV adoption.

Research on the Spatial Structure of the Beijing–Tianjin–Hebei Urban Agglomeration Based on POI and Impervious Surface Coverage

Exploring urban spatial structures through spatial coupling analysis methods is an important method to provide theoretical support for the construction of sustainable urban structures. In order to make up for the neglect of POI species differences in previous studies, information entropy was introduced to calculate POI confusion, and a comprehensive POI index was constructed by combining kernel density and the entropy weight method; impervious surface coverage was extracted based on land cover data. The spatial distribution of the Beijing–Tianjin–Hebei urban agglomeration and some typical cities was analyzed by coupling two types of data using the dual-factor mapping method. The research indicates the following: (1). The spatial distribution of the two sets of data in the Beijing–Tianjin–Hebei region is highly consistent, indicating a state of high spatial coupling; Beijing has the highest proportion of coupling in the same region at the city level (73.39%). (2). The areas with different coupling of the two types of data are mainly distributed in the urban fringe areas transitioning from the city center to the suburbs, as well as in large-scale areas with single functionality such as airports, scenic spots, and ports. This study shows that analysis combining the POI comprehensive index and impervious surface coverage can effectively characterize urban spatial structure characteristics, providing a new perspective for the study of the spatial structure of the Beijing–Tianjin–Hebei urban agglomeration. It is of great significance for a deeper understanding of the laws of urban agglomeration spatial structures and guiding the coordinated development of the Beijing–Tianjin–Hebei urban agglomeration.

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Metropolitan spatial structure based on commuting pattern: a comparison between Jakarta and Surabaya metropolitans

ABSTRACT Different urban structures can present different problems. Therefore, it is important to organize the spatial structure of the city to ensure the movement and welfare of city residents. Metropolitan Jakarta and Surabaya are the two largest metropolitan areas in Indonesia and play a very important role as centres of national social and economic activities. This study aims to identify urban spatial structures based on commuter movement patterns in both metropolitan areas. This is done by calculating the spatial interaction and visualization of the Desire Line Map. The results of the analysis show that Jakarta and Surabaya Metropolitan Areas still tend to be monocentric because the attraction of their movements is still dominated by their respective cores.

Assessment and optimization of urban spatial resilience from the perspective of life circle: A case study of Urumqi, NW China

This paper presents an original framework for assessing and enhancing urban resilience from the perspective of the life circle that emphasizes the daily activity spaces of residents, using Urumqi, a major city in northwest China's arid oasis region, as a case study. Based on multi-source urban geographic big data, we introduce Gini coefficient and Lorenz curve to analyze the spatial matching relationship between urban spatial pressure and urban spatial resilience, and divided the priority of planning intervention by priority index. Our findings for the case study Urumqi reveal a strong "center-periphery" structure of urban resilience and spatial pressures. Based on the findings, we proposed targeted planning interventions prioritized by a resilience index that considers the life circle to address disparities and improve urban resilience. Our study advocates for urban planning to focus on the life circle, aiming to develop a multi-center and cluster-type resilient urban spatial structure to foster a "people-oriented" resilient city.

Research on urban agglomeration spatial network structure in the middle reaches of the Yangtze River based on real-time traffic accessibility scenario analysis

ABSTRACT Investigating the spatial network structure of urban agglomerations based on traffic accessibility has an important practical significance in accelerating the process of regional integration. Taking urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) as the case study, this study aims to examine the characteristics associated with the spatial network structure based on traffic accessibility and its changes under scenarios of different traffic accessibilities by adopting real-time traffic data, modified gravity model, and social network analysis (SNA). The main conclusions are as follows. Wuhan, Changsha, and Nanchang are the central cities in the spatial network; the spatial network structure of UAMRYR is imbalanced and in the agglomeration stage of regional economic development. With the development of traffic accessibility, resources first gather in the individual center cities and then spread to other cities, and the network tends toward equilibrium.

Residential location choices and commuting patterns considering telecommuting

The COVID-19 pandemic has initiated more telecommuting (or working from home) than ever. This paper provides an economic analysis of the impacts of telecommuting on the residential location choice equilibrium. In particular, this paper employs a simplified monocentric city model of urban spatial structure to analytically examine the impacts of telecommuting on residential location choices and the associated commuting pattern in a city, considering that telecommuting may have positive, zero, or negative impact on individual income level. We found that the income effect of telecommuting and its relative magnitude against the commuting cost govern whether telecommuting will attract more to live in the suburb or the downtown. Moreover, the impacts of housing supply and commuting costs on the residential location choice equilibrium are dependent on the level of telecommuting, where such impacts diminish when the level of telecommuting increases. Furthermore, this paper derives and examines analytical solutions for the optimal level of telecommuting, the optimal housing supply and the optimal transport investment (that reduces commuting cost) in order to improve social welfare or the housing operator’s profit in the context of telecommuting. How these optimal solutions are related to the level of telecommuting is also analyzed. This study provides understanding on how telecommuting may affect the joint equilibrium of residential location choice and commuting and how housing supply and transport investment should be adjusted in response to telecommuting.

The spatial‐temporal evolution of urban development patterns in Chinese cities: Dynamics and interpretations

AbstractThis paper examines the spatial‐temporal evolution of urban spatial structure across 269 Chinese prefectural cities from 2002 to 2019. Our analysis identifies a consistent trend toward a more polycentric configuration in the 25 Chinese mega‐cities during this period, primarily due to population growth and a supportive policy environment. However, the evolutionary pathways of small‐ and medium‐sized cities unfolded in a rather complex and diverse manner, with some becoming more polycentric while the majority adhering to a monocentric trajectory. In these cases, population growth is usually associated with a more monocentric pattern, characterized by rapid expansion of the urban core, while polycentric development is primarily attributed to specific spatial policies that support the emergence of subcenters. We conclude that polycentric development, while potentially suitable for mega‐cities to alleviate diseconomies of scale, may be less appropriate for small‐ and medium‐sized cities as it may constrain growth associated with agglomeration economies. We suggest that the development and implementation of regional spatial policy should be considerate of local historical paths and contextual factors. Finally, we propose a stylized framework to more accurately reflect the diverse and complex nature of urban spatial structure evolution in Chinese prefectural cities.

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.