Coastal spaces are shaped by human activity. Approaching their urban spaces allows us to analyse the concepts of structure, growth, and management. Highlighting the problems associated with these concepts can lead to intensive scientific analysis and provide solid research methods. This paper focuses on the study of how the process of territorial occupation takes place and the urban forms it generates on the Spanish Mediterranean coast. It is based on the consideration that the process of territorial occupation is deficient in its territorial adequacy. To analyse this, a methodology is proposed that addresses processes of analysis at different scales: dynamics of changes in land use, the study of the morphologies of urban development with spatial analysis tools, and the adaptation of urban processes to the characteristics of the spaces that support them with multi-criteria evaluation techniques and GIS (Geographical Information Systems). The results are specified in five degrees of suitability of the occupation of the territory. Two conclusions can be observed: first, urban planning gives rise to forms of occupation that follow a similar pattern in the twenty areas studied, and second, the suitability of the urban process is not governed by planning based on precepts of suitability and environmental logic but by a weakness of the adapted planning methods.

Spatial Analysis Model for the Evaluation of the Territorial Adequacy of the Urban Process in Coastal Areas

The urban agglomeration plays a significant role in enhancing integrated regional development. Nevertheless, the expansion of urban agglomerations has demonstrated a lackluster ability to attract cities. Presently, finding solutions to stabilize the existing urban strength and effectively extend attraction to neighboring cities has become a crucial matter. This study adopts the enhanced comprehensive attraction model, fracture point model, and radiation radius model to examine the level of city attraction, intensity of radiation, and range of radiation in the northern and southern Xinjiang city clusters between 2010 and 2020. Based on the analysis, the following conclusions are drawn: (1) the comprehensive strength and city attraction of cities in the northern Xinjiang region is higher than that of the southern Xinjiang region; (2) the intensity of spatial connection between cities in the northern and southern Xinjiang regions is gradually increasing, and the intensity of spatial connection of cities in the northern Xinjiang region is significantly greater than that in the southern Xinjiang region. The intensity of spatial connection between cities in the northern and southern Xinjiang regions is significantly greater than that in the southern Xinjiang region; (3) the central role of the central cities in the northern and southern Xinjiang regions is weakening, the development of cities in the region is gradually becoming unified and coordinated, and regional integration is gradually being strengthened. This study reveals the similarities and differences in urban development in the north and south of Xinjiang and provides important theoretical reference value for regional urban development. 

Analysis of Spatiotemporal Changes in the Gravitational Structure of Urban Agglomerations in Northern and Southern Xinjiang Based on a Gravitational Model

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China. 

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

ABSTRACT Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective

Abstract With the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's I index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data. 

A global polycenter identification method with single‐source data: The integration of local multisource data recognition

The urban spatial structure is a key feature of the distribution of social and economic resources. The spatial structure of an urban agglomeration is an abstract relationship expression of urbanization. Urban agglomerations develop for multiple reasons, including urban planning and natural evolution. To date, most research related to urban agglomeration has been based on single data source, which is a limitation. This research aims to propose a spatial structure identification method for urban agglomerations via a complex network based on nighttime light data and railway data. Firstly, we extracted the urban built-up area using defense meteorological satellite program/operational line scanner (DMSP/OLS) data, and divided it into urban objects to obtain the nighttime light urban network (NLUN) by borough. Secondly, we aggregated railway stations at municipal level using railway operation data to obtain the railway urban network (RUN). Following this, we established a composite urban network (CUN) consisting of the NLUN and the RUN based on the composite adjacency matrix. Finally, the Louvain algorithm and the comprehensive strength index (CSI) were used to detect the communities and central nodes of the CUN and obtain the urban agglomerations and core cities. The results show that urban agglomeration identification based on the CUN has the best accuracy, which is 5.72% and 15.94% higher than that of the NLUN and RUN, respectively. Core cities in the urban agglomeration identified by the CSI in the CUN are at least 3.04% higher than those in the single-source urban network. In addition, the distribution pattern of Chinese urban agglomerations in the study area is expressed as “three vertical”, and the development level of urban agglomeration shows an unbalanced trend.

/pdf/a-structure-identification-method-for-urban-agglomeration-23qdyf42.pdf

A Structure Identification Method for Urban Agglomeration Based on Nighttime Light Data and Railway Data

How to quickly and accurately identify the bridge rubber bearing deterioration plays an important role in ensuring the bridge structure and road safety. This paper selects the common rubber bearings of domestic bridges as the research object, and proposes an improved YOLOv4-based bridge rubber bearing deterioration detection algorithm to address the reasons for the difficulty in detecting bridge rubber bearing deterioration due to large scale variations and small sample data sets. An image dataset (named HRBD) with annotations is constructed from real inspection scenarios, and the data is expanded by image processing means such as rotation, translation and brightness transformation, so that this dataset has sufficient data complexity and solves the problem of overfitting due to insufficient samples for network training. The anchor applicable to this dataset was regained by the K-means++ clustering algorithm, and then the CA module was inserted into the YOLOv4 backbone network for more accurate anchor localization. The improved YOLOv4 network was used for migration learning to train the dataset, and finally the trained network model was used for detection on the test set. The experimental results show that the improved YOLOv4 bridge rubber bearing deterioration detection and identification network can effectively identify and locate bridge rubber bearings and their deterioration types (crack damage, shear deformation, bearing void).

Study on deterioration identification method of rubber bearings for bridges based on YOLOv4 deep learning algorithm

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A Structure Identification Method for Urban Agglomeration Based on Nighttime Light Data and Railway Data

Study on deterioration identification method of rubber bearings for bridges based on YOLOv4 deep learning algorithm