Central Urban Area Research Articles

Agglomeration Characteristics and Influencing Factors of Urban Innovation Spaces Based on the Distribution Data of High-Tech Enterprises in Harbin

In the knowledge economy, innovation is playing an increasingly important role in urban sustainable development. Thus, how to strengthen the construction of innovation spaces has become a significant issue. Based on data from high-tech enterprises in the central urban area of Harbin, this study analyzes the distribution, agglomeration characteristics, and influencing factors of urban innovation spaces. The influencing factor index system was constructed based on four aspects: innovation support, service facilities, the social environment, and the natural environment. After analyzing these influencing factors through comparing the results obtained with an ordinary least squares (OLS) model and a geographical weighted regression (GWR) model, the GWR model was determined to have a better fitting effect and was thus used for further analysis. The findings are as follows: (1) Five closely related clusters of innovation spaces are identified. Innovation space has a significant agglomerating distribution pattern. (2) Overall, factors supporting innovation and transportation station factors have a positive effect on the agglomeration of innovation spaces, while natural environment factors have a negative effect on small innovation areas. (3) By region, the aggregation of innovation spaces in downtown areas is slightly negatively affected by service facilities, whereas those in the Harbin New District are mainly supported by policies and those in the Pingfang District are positively affected by the quantity of the labor force. Finally, based on the research results, some suggestions are put forward to promote the development of urban innovation spaces. This paper has certain guiding significance for optimizing and improving the construction and development of urban innovation spaces.

Low Residents’ Satisfaction with Wetland Leisure Demand in Typical Urban Areas of the Semi-Arid Region in Western China: Spatial Variations and Their Causes

Wetlands, as a crucial component of urban green spaces, provide important leisure services for residents. Construction of wetlands has increased with the rapid urban expansion and population growth in China over recent decades, especially in semi-arid regions with scarce water resources. However, the residents’ satisfaction with wetland leisure demand remains unclear. This study evaluated the residents’ satisfaction with wetland leisure demand by a framework of physical calculation, taking Yinchuan City, the capital city of Ningxia Hui Autonomous Region, in the semi-arid region of Western China as a case study area. Spatial variations in residents’ satisfaction and their causes were revealed by a supply–demand relationship between the population capacity of wetlands and the total population of communities under a framework of physical calculation. The results indicated that 4.22% of the study area, which covered 7.38% of the total population, was fully satisfied with wetland leisure demand. Residents’ satisfaction in the urban area as a whole is low at 0.49, with a concentric distribution pattern increasing as the distance from the central urban area increases. The high population density and scanty wetlands mainly induced a relative-low residents’ satisfaction in the central urban area, accounting for 12.02% of the area and 32.70% of the population. Meanwhile, a relative-high residents’ satisfaction in the outer ring of the central urban area accounting for 59.10% of the area and 20.63% of the population was primarily due to the adequate capacity of wetlands. Medium residents’ satisfaction was mainly attributed to the road network density, which partially transferred local wetland leisure demand to adjacent areas. Wetland constructions and internal structural optimization to improve the capacity for leisure in densely populated central urban areas could provide a feasible path to alleviate unbalanced conditions. These results could deepen the understanding of supply–demand dynamics in the wetland leisure service and provide valuable information for optimizing wetland allocation in urban construction in semi-arid regions.

Relationship between the built environment and urban vitality of Nanjing’s central urban area based on multi-source data

Urban vitality reflects the dynamism, openness, available resources, and linkages between different elements in a city. Many studies have focused on the relationship between urban vitality and the built environment, which can benefit urban planners. Based on multi-source data, this study incorporates the aspects of economy, society, culture, and innovation to comprehensively measure urban vitality. Recognizing the significant influence of the built environment on urban vitality, this paper takes Nanjing as the study area. It explores the relationship between urban vitality and the built environment using ordinary least squares and multi-scale geographically weighted regression models based on multi-source data. The results reveal multi-centered urban vitality within Nanjing’s central urban area, which gradually decreases outwards from the city center. Particularly, the southern region has a higher comprehensive vitality than the northern region. While differences in vitality between dimensions are evident, an overall consistent pattern emerges. The spatial distribution of influence varies across different spatial factor. The method provided in this study gives a new view of urban vitality measurement and evaluation.

Analysis of the evolution and influencing factors of ecological environment quality after the transformation of resource-exhausted cities based on GEE and RSEI: a case study of Xuzhou City, China

ABSTRACT This study used Google Earth Engine (GEE) to get and process Landsat images of Xuzhou from 2008 to 2020, and built remote sensing ecological indices (RSEI). This study analyzed the spatiotemporal features and drivers of ecological environment quality after Xuzhou’s transformation using landscape pattern index, spatial autocorrelation, and geographic detector methods. The results showed that: (1) The ecological environment quality of Xuzhou improved steadily but unevenly from 2008 to 2020; the west and central urban areas had poor but improving quality, while the east had the opposite trend; (2) The landscape pattern indices of ecological environment quality in different regions of Xuzhou did not change consistently from 2008 to 2020; (3) The spatial distribution of ecological environment quality had significant autocorrelation, with high-high clusters in the east and low-low clusters in the center and the urban areas of each district and county; (4) NDVI is the component index that has the greatest impact on the changes in the RSEI. Among the single factors, land use conditions have the strongest influence on ecological environmental quality. After the interaction of dual factors, the explanatory power is stronger than that of any single factor. The research results can support urban planning and design, ecological environment protection, etc. in Xuzhou, and offer insights for monitoring and evaluation of ecological environment quality after the transformation of resource-exhausted cities.

A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.

Beyond built environment: Unveiling the interplay of streetscape perceptions and cycling behavior

As an important means of shared micro-mobility, shared bicycles have become a crucial component of urban transportation in China. The impact of the built environment on bicycling has been widely acknowledged. However, can streetscape perceptions influence bicycle-sharing volume (BSV) and supplement the built environment? We first obtained millions of pieces of shared-cycling data from the Shenzhen Open Data Platform and carried out geographical quantification of BSV. As for streetscape, we improved the classification of subjective streetscape perception based on street view images using the k-means clustering algorithm and conducted predictions using XGBoost. Through the application of different regression models, we unveiled the nonlinear spatial interdependencies between BSV and streetscape perceptions as a complement to the built environment. Our findings indicate that greenery, vivid street-front facades, and diverse street facilities can promote BSV. Targeted strategies are proposed for different districts. For instance, urban planners can provide incentives for high-income groups in central urban areas to adopt active travel, and increase the supply of shared bicycles in suburban areas with high building density, particularly in industrial urban villages. As a supplement to the long-term planning recommendations derived from the macro-built environment analysis, an in-depth spatial perception quantitative assessment proffers a human-centric, flexible blueprint for urban street design.

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO2 emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO2 emissions by approximately 66.74% compared to the existing logistics network.

Study on the Evaluation of Urban Park Landscape Pattern Index and Its Driving Mechanisms in Nanchang City

Urban planning is often influenced by industrial and construction activities, leading to a lack of attention to the planning and construction of urban parks, which results in prominent spatial layout problems. Urban parks, as an important part of the urban green space system, play a significant role in improving the ecological environment, promoting urban economic development, and enhancing the daily living standards of the people. As a typical representative of China’s second and third-tier cities, Nanchang’s analysis of the evolution process of urban landscape patterns has reference significance for other similar cities in China. This paper is based on the theoretical foundations of landscape ecology, human geography, and urban planning, and analyzes the evolution of the park landscape pattern in the central urban area of Nanchang from 1999 to 2019 from the perspective of urban context, revealing the driving mechanisms. It provides important references and bases for the further optimization and construction development of the park landscape pattern in Nanchang. The results show that the park area and number in the central urban area of Nanchang have significantly increased, with the overall layout evolving from “central aggregation” to “core aggregation in each area”, and from “central scarcity, more on the periphery” to “inward concentration, outward diffusion”. However, the distribution of various types of parks is uneven, and there is a lack of green corridor links between parks. The park landscape pattern is driven by multiple factors such as natural factors of urban context, socio-economic factors, urban construction factors, historical policies, and related planning, which can have positive or negative effects. Incorporating relevant urban factors into the park planning system analysis can promote the benign development of urban context and park landscape layout, thereby achieving the “parkification” of the city.

Neural Cellular Automata-based Land Use Changes Simulation

Abstract. Simulating land use and land cover changes (LUCC) is important for urban planning and environmental studies. In this study, we introduce a neural cellular automata (NCA) model that integrates biological principles and convolutional neural networks (CNNs) for land use simulation. We conduct experiments in the city of Wuhan, China. The NCA model achieved the highest performance with an OA of 0.858, F1 score of 0.753, Kappa coefficient of 0.799, and FOM of 0.427. Comparisons of land use data of Wuhan city from 2000 and 2010 with the simulated optimal results indicate that forest areas closer to urban centers are more susceptible to modernization processes, showing the advantage of NCA in accurately simulating land use changes in the central urban area.

Characteristics of Coupling between Multi-scale Community Living Circles Public Service Facilities Supply and Spatial Centers

In response to changing demand structures due to slower population growth and economic shifts, this study examines the coordination between public service facilities and urban spatial centers, crucial for people-centered community development and spatial agglomeration laws. Utilizing crystal-growth algorithms and spatial autocorrelation, the research algorithmically interprets spatial centers, and conducts kernel density and natural break classification analyses on public service facilities. The case of Guangzhou's central urban area reveals: 1) The spatial centers are in Liwan, Yuexiu, and Haizhu districts; 2) Centers are dispersed within a five-minute range but cluster over thirty minutes; 3) Analysis of coupling at different scales highlights areas needing more public service facilities. This methodology elucidates the relationship between spatial elements and structure, enriching community living circle theory and contributing to urban high-quality development.

Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model

Flood susceptibility mapping is crucial for urban disaster management. However, the heterogeneity of urban land use and the complexity of terrain pose challenges to the accuracy and generalizability of flood models. In this paper, we propose a framework named the EMF model for urban flood mapping. Specifically, the social sensing and remote sensing were used for flooding information collection. The XGBoost, Support Vector Classifier (SVC), Multilayer Perceptron (MLP), and Multimodal Deep Learning (MDL), were used as the predictive models, and their results were ensembled using a Random Forest model to produce the final outcome. Results show that the EMF model outperforms the standalone models, with an accuracy of 0.942 on the training set and 0.940 on the testing set. The accuracy of the five models is ranked as EMF > MDL > XGBoost > SVC > MLP. The flood map indicates that flooding has a greater impact on the outskirts and suburbs of the city compared to its central urban areas. Farmland is the most affected land type, making up 54.8 % of the flooded area. Overall, the proposed framework enables rapid and accurate identification of flood-prone areas, providing technical support for managers in formulating effective flood prevention strategies.

Spatio-temporal patterns and population exposure risks of urban heat island in megacity Shanghai, China

Accompanied by global warming and rapid urbanization, the urban heat island (UHI) effect has been intensifying, which leads to an increased risk of population heat exposure, especially in mega-cities. However, there are fewer studies on the identification of UHI ranges and the associated population heat exposure risks, particularly on the fine scale. Therefore, this study took Shanghai as a case study and explored the spatio-temporal evolution characteristics of the UHI ranges and population heat exposure risks, based on five-period MODIS land surface temperature (LST) product, population density distribution data, and land use and cover change data from 2000 to 2020, combined with the temperature attenuation mutation (TAM) method, breakpoint regression model, population heat exposure relative risk assessment method, and spatial autocorrelation analysis. The results showed that: (1) From 2000 to 2020, the UHI range in Shanghai was continuously expanding, and the intensity of the surface UHI was gradually increasing. The overall pattern was characterized by a stable large heat island in the central urban area and new small heat islands that continued to emerge in the peripheral suburbs. (2) The population heat exposure risks in Shanghai intensified from 2000 to 2020, with an increasing number of people exposed to high-intensity UHI ranges, doubling the population coverage in these areas. By the end of 2020, the population exposed to UHI ranges accounted for approximately 81 % of the total population of Shanghai. (3) The spatial pattern of population heat exposure risks from 2000 to 2020 was generally similar. Population heat exposure regions of high risk level were primarily concentrated in the central urban areas, showing typical spatial clustering characteristics, while population heat exposure regions of low risk level were mainly dispersed in the peripheral suburbs, and showed a gradual transformation trend into population heat exposure regions of high risk level. Our findings can provide scientific basis and decision support for precise and refined urban heat risk control and planning management.

Regionalization of environmental and economic performances of rainwater harvesting systems

Study regionBeijing Region Study focusLimited studies have yet been done on regionalization of multi-performances of rainwater harvesting systems (RHS). In this study, the daily rainfall records from 77 stations are used, and a hydro-economic model is adopted to examine regional water saving, stormwater control and economic performances of RHS by proposing a regionalization approach. New hydrological insights for the regionHigher water saving efficiency (WSE) and reliability (R) of RHS are linked with lower water demand, larger tank size and greater rainfall conditions. Differently, higher stormwater capture efficiency (SCE) is related to higher water demand, larger tank size and lower rainfall. The WSE and R of RHS demonstrate substantial regional differences; their maximized obtainable values closely depend on water demand scenarios and range from 9% to 99%, with smaller values in western mountain area but larger values in northeast suburban and central urban areas. The maximum obtainable values of SCE range from 61% to 100%, with higher values in western mountain area, but lower values in northeast suburban and central urban areas. A 10 m3 tank size can provide the highest benefit-cost ratio of RHS across the Beijing region. The regionalization approach proposed is a useful guidance for the implementation of RHS and sustainable urban water management, and thus it has the potential for wide uses in other regions with high rainfall gradients.

Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing

By leveraging a multiscale geographically weighted regression (MGWR) model, this paper delves into the intricate factors that influence housing prices in the prototypical mountainous cityscape of Chongqing’s central urban area. The key findings are as follows: Firstly, the distribution of housing prices in the study region exhibits pronounced spatial heterogeneity, with the core area exhibiting a distinct “high-high” clustering pattern and manifesting characteristics of a multicenter group distribution. Secondly, the MGWR model effectively assigns an individual bandwidth to each feature quantity, allowing for a more nuanced portrayal of the varying influence scales exerted by diverse variables. Lastly, the study reveals that factors such as property cost, greening rate, building age, and proximity to rivers have a notable negative impact on housing prices, whereas, educational facilities exert a marked positive influence. Elevation, floor area ratio, and distance from the Central Business District (CBD) exhibit a more complex influence on housing prices.

Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City’s central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development that emphasizes aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small- and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Urbanization of Chongqing Municipality: Regional Contributions and Influencing Mechanisms

Enhancing the level and quality of urbanization and embarking on a new path toward the sustainable development of urbanization are the forces that are driving the achievement of stable and healthy economic and social development. Based on data from three population censuses and official statistics spanning from 1997 to 2021, this study decomposed the changes in the urbanization rates in Chongqing during this period. We employed a fixed-effects model to conduct a regression analysis on the factors influencing the regional contributions of urbanization and provide a comprehensive analysis of their mechanisms. The study findings are as follows: 1. The central urban area has been the primary region driving the urbanization in Chongqing since the establishment of the Chongqing municipality; however, since 2017, the new urban area has gradually become the new “main force” driving the urbanization in Chongqing. In different stages, dynamic regions have exhibited a pattern of “diversification—centralization—diversification”. 2. Local urbanization is the main factor promoting the urbanization development in Chongqing, whereas remote urbanization has only played a supplementary role. The overall urbanization pattern in Chongqing is characterized by “local urbanization and population inflow”. The central urban area has primarily propelled the urbanization of Chongqing through remote urbanization, whereas the new urban area, as well as the northeastern Chongqing area and southeastern Chongqing area, have predominantly driven the urbanization of Chongqing through local urbanization. In the four major regions and 38 counties, the predominant urbanization type was often “local urbanization and population outflow”. 3. The level of the contribution of different regions to the urbanization development in Chongqing is the combined result of factors such as economic development, local administrative capacity, improvement in public services, and enhancement in transportation infrastructure.

SARS-CoV-2 Pandemic in a Small-Sized Municipality in Ceará State, Brazil: Temporal and Spatial Evolution.

Data on the temporal and spatial evolution of SARS-CoV-2 and local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed the data from case notification systems, epidemiological investigation reports, and municipal decrees in Itapajé, a small municipality in Ceará State in northeast Brazil. For spatial and spatio-temporal analyses, cases and deaths were mapped. There were a total of 3020 cases of COVID-19, recorded between April 2020 and December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5650.3 cases and 252.6 deaths per 100,000 people, respectively. The index case of SARS-CoV-2 in Itapajé was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave peaked in May 2021, with the highest number of deaths in March 2021. According to the spatial analysis, the highest density of cases and deaths occurred in the central urban areas. In these areas, there were also the clusters of highest risk according to the spatio-temporal analyses. The municipal government issued 69 decrees on restriction measures, surveillance, and the maintenance of social isolation as a response to the pandemic. The spread of the SARS-CoV-2 pandemic in Itapajé mirrored the dynamics in large metropolitan regions, going from central neighborhoods of low socio-economic status to the wealthier peripheries.

Spatial Gradient Differences in the Cooling Island Effect and Influencing Factors of Urban Park Green Spaces in Beijing

Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and optimizing the structure and resource allocation of park green spaces in a scientifically rational manner. This study focuses on parks within the central urban area of Beijing, utilizing remote sensing image interpretation and land surface temperature (LST) inversion to extract relevant characteristics of park green spaces and the park cool island intensity (PCI) index. Various mathematical and statistical methods including correlation analysis, regression analysis, and cluster analysis are employed to conduct comparative studies across three gradients: within the 3rd ring road, between the 3rd and 5th ring roads, and outside the 5th ring road. The analysis reveals that both park green space characteristics and urban heat island effects exhibit spatial gradient differences, collectively influencing the cool island effects of urban park green spaces. PCI gradually decreases across the three spatial gradients. Cluster analysis identifies four distinct types of parks with different cool island effect characteristics, highlighting the need for optimization and improvement in over half of the parks. Various indicators of park green space characteristics show different correlations with PCI, with variations in correlation strength and thresholds across gradients. The fitting effects of regression equations for each characteristic indicator and PCI gradually worsen from within the 3rd ring road to outside the 5th ring road, with different factors playing important roles across gradients. This study enhances our understanding of the cool island effects of urban park green spaces and facilitates the proposition of differentiated optimization management strategies for urban park green space planning and system construction in different regions.

Impacts of land use change on surface infiltration capacity and urban flood risk in a representative karst mountain city over the last two decades

Urban areas are undergoing rapid expansion, resulting in diminished vegetation cover and an escalating urban flood problem, and posing substantial threats to aquatic ecosystems, human safety, and socioeconomic structures. However, research on urban flood risk using methods like the Analytic Hierarchy Process (AHP), further exploration of spatiotemporal variations is needed, especially within the context of karst mountainous urban clusters. This study focuses on Guiyang city within a karst mountainous urban cluster, investigating fractional vegetation cover (FVC), infiltration capacity, and urban flood risk. Methodologies encompass slope trend analysis, the utilization of the Google Earth Engine, and geographic detectors to assess the dynamics of FVC and infiltration capacity. AHP is employed to assign weights to infiltration capacity, land use and land cover (LULC), FVC, and elevation, culminating in the generation of flood risk maps through linear weighted overlay. The results showed that: (1) The average FVC in Guiyang City was 0.7440 in 21 years, with relatively low levels in the central and southern regions. The FVC increased by 0.21% per year from 2000 to 2020, driven mainly by land use change and human activities. (2) The infiltration capacity of the city of Guiyang has increased as a whole, mainly due to reforestation efforts in specific areas, while the infiltration capacity of impervious areas tends to decrease. (3) The central urban areas exhibit greater vulnerability, with an estimated ten-year average growth rate of 73.36% in very high flood risk areas by 2030, affecting 7.38% of Guiyang's total area. These urban flood risk maps serve as valuable tools for local governments to enhance urban management practices.

Examining the scale effect of nearby residential green space on residents’ BMI: A case study of Guangzhou, China

Green spaces are closely related to the quality of the urban environment and public health. However, studies have rarely paid attention to nearby residential green space which is more closely connected to residents’ daily lives. Does nearby residential green space have a scale effect on the body mass index (BMI) of residents? What type of green space has a more significant effect? Does this effect differ for residents living in central urban areas and suburban areas? To answer these questions, this study first measured three scales of nearby residential green space accessibility of 1398 samples of 18 neighborhoods’ survey data in Guangzhou, China, combined with the application of the TIQS (Travel O-D point Intelligent Query System). And then, multilevel mixed-effects models were developed to examine the scale effect of nearby residential green space accessibility on residents’ BMI. The results show that nearby residential green space has a scale effect on individual BMI and the effect of the 10-minute walking scale is the most significant. Within a 10-minute walking distance from the residence, the larger the green space and the more accessible it is, the lower the individual’s BMI. Parks are the green spaces that are most correlated to residents’ BMI, but their effect is somewhat different between the central urban areas and suburban areas. These findings give us a better understanding of the association between nearby residential green space and public health, as well as providing implications for the improvement of the urban green space system and policy development for healthy cities.