core periphery structure Research Articles (Page 2)

In the context of global climate change intensifying typhoon disaster risks, this paper adopts a social-network analysis perspective and uses Typhoon Doksuri (2023) as a typical case to examine the structural characteristics and operating mechanisms of government emergency coordination networks. By collecting publicly available government documents and media reports and applying UCINET and Gephi, we constructed a coordination network model containing 44 departments and 10 working groups. We then carried out an empirical analysis combining centrality analysis, the coreperiphery model, and matrix analysis methods. The results indicate: (1) the Inspection Group, the Integrated Group, and the Relocation & Resettlement Group occupy core positions in the network and undertake cross-departmental coordination and supervisory functions; (2) the Ministry of Emergency Management, water resources departments, and the Flood Control and Drought Relief Command act as core hub nodes, with centrality measures significantly higher than those of other departments; (3) the Safety Alert Group and the Logistics Support Group are located at the networks periphery and their coordination efficiency needs urgent improvement; (4) the network displays a marked coreperiphery structure: coreness (Coreness = 5.429) is significantly higher than periphery (Periphery = 2.000). Based on these findings, we propose a three-dimensional pathway to optimize the coordination network: improve cross-departmental linkage mechanisms, strengthen the emergency capacities of key nodes, and establish a long-term support (guarantee) mechanism providing theoretical basis and practical reference for improving the effectiveness of typhoon disaster emergency governance.

In the context of China’s “double carbon” goals, examining the spatial–temporal characteristics and influencing factors of the synergistic effect of pollution control and carbon reduction (SEPCR) in the Chengdu–Chongqing region (CCR) is crucial for advancing both air pollution (AP) control and carbon emissions (CE) mitigation. This study uses data on AP and CE from 2007 to 2022 and employs the coupling coordination degree (CCD) model, spatial autocorrelation analysis, and kernel density estimation to investigate the spatial–temporal distribution and dynamic evolution of the CCD between AP and CE in the CCR. Additionally, the Tobit regression model is applied to identify the key factors influencing this synergy. The results indicate that (1) during the study period, the air pollutant equivalents (APE) in the CCR showed a declining trend, while CE continued to increase; (2) the overall level of coupling coordination remained low, exhibiting an evolutionary pattern of initial increase, subsequent decrease, and then recovery, with synergistic effects showing slight improvement but significant fluctuations; (3) the SEPCR in the CCR was generally dispersed, exhibiting no significant spatial autocorrelation. A “core–periphery” structure emerged, with Chongqing and Chengdu as the core and peripheral cities forming low-value zones. Low–low clusters indicative of a “synergy poverty trap” also appeared; (4) economic development (PGDP), openness level (OP), and environmental regulation intensity (ER) are significant positive drivers, while urbanization rate (UR), industrial structure upgrading (IS), and energy consumption intensity (EI) exert significant negative impacts.

In response to the coal mining industry's high-risk nature and limitations of traditional accident analysis, this study constructs a multi-factor coupling analysis framework using 481 accident reports. Parsing unstructured text reveals ‘core–periphery’ structural characteristics in accident causation systems. Key contributions of the study are as follows: methodologically, it employs text mining to automate factor extraction and integrates social network analysis (SNA) to quantify node centrality and transmission intensity; theoretically, 18 core causations (e.g., unauthorized risk-taking) are network hubs, while 50 peripheral factors (e.g., latent equipment defects) amplify core risks through linkages, validating ‘minor signals triggering major accidents’ dynamics; and practically, targeted critical node intervention strategies are proposed, aiding a shift from single-factor control to networked management and offering global high-risk industry insights.

Rapid urbanization has altered the land use pattern, reducing urban green space and increasing carbon emissions, and it is critical to scientifically examine the interaction mechanism between green space and carbon emissions in order to drive low-carbon urban development. Using Nanjing as an example, this study examined the spatiotemporal evolution characteristics of urban green space patterns and carbon emissions between 2000 and 2020. Carbon emissions at the city and county levels were estimated with great precision using a gray BP neural network model and a downscaling decomposition method. Using urban green space landscape pattern indices and geographic detectors, significant driving factors were discovered and their impact on carbon emissions examined. The results show the following: (1) Carbon emissions are mostly influenced by socioeconomic factors, and the gray BP neural network model (R2 = 0.9619, MAPE = 1.68%) can predict outcomes accurately. (2) Between 2000 and 2020, Nanjing’s overall carbon emissions increased by 118.9%, demonstrating a “core–periphery” pattern of spatial divergence, with significant emissions from industrial districts and emission reductions in the central urban region. (3) The urban green space exhibits “quantity decreasing and quality increasing” characteristics, with the total area falling by 4.84% but the structure optimized to form a networked pattern with huge ecological patches as the backbone. (4) The primary drivers are the LPI, COHESION, and AI. This study reveals the complex relationship mechanism between the spatial configuration of urban green space and carbon emissions and, based on the results, proposes a green space optimization framework with three dimensions, protection of core ecological patches, enhancement of connectivity through ecological corridors, and implementation of low-carbon maintenance measures, which will provide a scientific basis for the planning of urban green space and the construction of low-carbon cities in the Yangtze River Delta region.

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade.

The distribution of urban services reveals critical patterns of human activity and accessibility. Proximity to amenities like restaurants, banks, and hospitals can reduce access barriers, but these services are often unevenly distributed, exacerbating spatial inequalities and socioeconomic disparities. In this paper, we present an accessibility measure based on the spatial distribution of points of interest within cities. Using the radial distribution function from statistical physics, we analyze the dispersion of services across different urban zones, combining local and remote access to services. This approach allows us to identify a city's central core, intermediate areas or secondary cores, and its periphery. Comparing the areas that we find with the resident population distribution highlights clusters of urban services and helps uncover disparities in access to opportunities.

Abstract Against the backdrop of intensified global tourism competition, the perception of destination image has become increasingly influential in tourist decision-making. However, existing research has lacked in-depth exploration of the image perception mechanisms of Japanese tourists in the Hong Kong and Macau regions, particularly the dynamic analysis based on user-generated content (UGC) remains underexplored. Drawing upon online reviews authored by Japanese tourists between 2009 and 2023, this research applies sentiment analysis, high-frequency lexical analysis, and a core–periphery modeling approach to uncover the defining characteristics of their perceptions and their underlying mechanisms. The results indicate that: (1) Japanese tourists predominantly exhibit positive sentiments toward Hong Kong and Macao, primarily driven by natural scenery, historical and cultural attractions, and transportation infrastructure, yet express negative feedback regarding issues such as attraction facilities and service quality. (2) The evolution of the tourists’ destination image perceptions reflects a diversified trend, shifting from singular cultural sightseeing to composite culture-entertainment experiences, with modernized resources and interactive activities emerging as new satisfaction drivers. (3) Tourism resources and activity experiences constitute the core elements of tourist perception, while service quality, transportation facilities, and urban development occupy peripheral positions that play a supportive role in enhancing tourist satisfaction. (4) The synergistic effect between tourism resources and activity experiences exerts the strongest influence, with peripheral factors—service quality, transport infrastructure, and urban growth—indirectly enhancing the central experiential components.

Disentangling the effects of the built environment on urban vitality at the scale of community life circles is crucial for informing precise urban planning and design, particularly in the context of urban renewal. However, studies examining the complex relationships and spatial heterogeneity in these effects remain limited, hindering the identification of built environment characteristics that may generate sustainable benefits. Therefore, this study took Xi’an, a typical high-density city in Northwest China, as an example. The eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) method were utilized to reveal threshold effects and spatial correlations between the built environment and community life circles’ vitality across varying buffer zones. The results show that (1) there is a significant spatial correlation between the built environment and the core–periphery structure of community life circles’ vitality. (2) Indicators, such as facility accessibility, the floor area ratio, intersection density, and the residential land use ratio, contribute significantly to community life circles’ vitality. (3) While the micro-built environment and socio-economic factors show limited contributions, their collaboration with the macro-built environment can enhance their individual effects, highlighting the necessity of taking them into account together. These findings provide new insights into supporting community life circles’ vitality through urban planning and design.

The long-term spatial–temporal variation in shoot blight of larch in China has not yet been clearly defined, and the mechanisms behind its long-distance spread remain unknown. This study, based on the historical occurrence dataset of shoot blight of larch in China, used spatial statistical analysis to describe the spatial changes in the disease across five stages since 1973. Subsequently, the study utilized Geo Detector and Random Forest models to investigate the relationship between the spread and occurrence of shoot blight of larch and seven influencing factors. The results revealed the following: (1) The spread of shoot blight of larch in China exhibits significant directionality, with the affected regions distributed along a northeast–southwest axis, and the epicenter of the spread is shifting southwestward; (2) Shandong and Jilin provinces served as the initial introduction points for shoot blight of larch, with most infected counties in other provinces experiencing outbreaks between 1989 and 1996, accompanied by a noticeable spread to neighboring provinces; (3) the occurrence of shoot blight of larch demonstrates a significant positive spatial clustering effect, forming a monocentric “core–periphery” structure centered in Liaoning Province, where kernel density values decrease gradually outward from the core. Geo Detector identified “seedling planting area” as a potential spatial driving factor for the disease. These findings underscore the critical influence of the combined effects of human activities and natural factors in shaping the spatiotemporal distribution patterns of shoot blight of larch.

Coastal countries’ productive capabilities are significantly influenced by the structure, dynamics, and evolution of marine products. In this study, we examined the market development dynamics of marine products across 156 coastal countries from 1995 to 2021, utilizing product space and economic complexity frameworks. We constructed and analyzed the product space of 106 marine products across six major marine industries, investigating the positioning and potential of various coastal countries within this space over three decades. The key findings were as follows: (1) global marine product exports exhibited substantial growth and increased geographical diversification. (2) The marine product space exhibited a core–periphery structure, with high-complexity marine shipbuilding products at the core and resource-based primary products at the periphery. (3) Coastal countries’ positions in the product space could be clustered into four distinct types: (i) complex diversified economies with constrained development potential, (ii) developed and industrialized countries with coexisting complexity and diversification, (iii) small island and developing countries with coexisting specialization and potential, and (iv) single-resource-dependent countries in the Middle East and Gulf of Guinea in Africa. (4) Each type faced unique development opportunities and challenges. We recommend that all economies continually leverage their comparative advantages in complex products. Specifically, type (i) and (ii) countries should focus on strengthening inter-product technological and knowledge networks and developing more integrated industrial chains. Type (iii) and (iv) countries should capitalize on their resources and geographical advantages to develop new, more complex products that align closely with their existing product baskets.

With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure that land development, housing affordability, and school facility investments advance equitable access to quality education. These results provide a novel perspective on compulsory education quality assessment and offer a valuable foundation for guiding education policies and urban development strategies.

Urban agglomeration (UA) is a highly developed spatial form of urban complex, which is one of the important carriers of regional economic cooperation, international industrial division of labor, and flow of capital and information elements. In China, urban agglomerations (UAs) have become the spatial subject of the national new-type urbanization strategy since the early 21st century and have made irreplaceable contributions to China’s urbanization and economic development. The Guanzhong Plain urban agglomeration (GPUA) is an important economic growth pole in northwest China and a key node in China’s open-door pattern. Exploring the spatial and temporal characteristics and driving factors of its economic development will be an important revelation for the promotion of high-quality economic development of the GPUA. This paper characterizes the level of economic development of GPUA with a long series of nighttime light data between 2002 and 2022. The standard deviation ellipse, spatial autocorrelation analysis, the economic difference index, and grey correlation analysis are used to analyze the characteristics of spatio-temporal evolution, internal diversity, and driving factors of economic development of the GPUA. The results show that the economic development level of the GPUA continued to increase from 2002 to 2022. The spatial distribution of the GPUA economy is “northeast-southwest” axial distribution, and the center of gravity of economic development gradually moves westward. The differences in the level of economic development within the GPUA show a typical core–periphery structure, but the degree of difference tends to weaken over time. The internal expansion force and economic promotion force were the dominant factors for the economic development of the GPUA in the early years. However, with the passage of time, scientific and technological support and government support have gradually become the main influencing factors for the economic development of the GPUA nowadays.

Core–periphery detection aims to separate the nodes of a complex network into two subsets: a core that is densely connected to the entire network and a periphery that is densely connected to the core but sparsely connected internally. The definition of core–periphery structure in multiplex networks that record different types of interactions between the same set of nodes on different layers is non-trivial since a node may belong to the core in some layers and to the periphery in others. We propose a nonlinear spectral method for multiplex networks that simultaneously optimizes a node and a layer coreness vector by maximizing a suitable non-convex homogeneous objective function by a provably convergent alternating fixed-point iteration. We derive a quantitative measure for the quality of a given multiplex core–periphery structure that allows the determination of the optimal core size. Numerical experiments on synthetic and real-world networks illustrate that our approach is robust against noisy layers and significantly outperforms baseline methods while improving the latter with our novel optimized layer coreness weights. As the runtime of our method depends linearly on the number of edges of the network, it is scalable to large-scale multiplex networks.

The co-agglomeration of the digital economy industry and manufacturing is significant for addressing issues such as being “large but not strong” and “comprehensive but not refined” in China’s manufacturing sector. This study uses 269 cities in China from 2006 to 2022 as the research sample, innovatively employing data from digital economy enterprises and manufacturing enterprises to measure industrial co-agglomeration, and comprehensively analyzes the mechanism of how the inflow of digital talents influences the co-agglomeration of the digital economy industry and manufacturing. The findings are as follows: (1) From 2006 to 2022, the inflow of digital talents and the level of co-agglomeration between the digital economy industry and manufacturing in Chinese cities have consistently risen, generally moving towards higher inflow and higher levels of co-agglomeration. However, the inflow of digital talent in the central and western regions is relatively low, with most cities still facing difficulties due to inadequate policy support and resource investment. Industrial co-agglomeration exhibits characteristics of “core–periphery”, “multi-core agglomeration”, and “gradient diffusion” coexisting. (2) The flow of digital talents can significantly promote the co-agglomeration of the digital economy industry and manufacturing, and this conclusion remains valid after robustness testing. The flow of digital talents drives the co-agglomeration of the digital economy industry and manufacturing by enhancing the level of digital technology innovation, promoting the spillover and flow of digital knowledge, increasing the entrepreneurial activity of urban digital economy enterprises, and upgrading industrial structures. Furthermore, digital economy policies play a regulatory role in this process. (3) The promotion effect of digital talent inflow is more pronounced in low- and mid-end manufacturing, high-grade cities, well-developed digital infrastructure, and non-resource-based cities. By contrast, this effect is relatively weaker in high-end manufacturing and low-grade cities. In cities with weak digital infrastructure and resource-based cities, this effect is not significant. (4) The inflow of digital talents and the co-agglomeration of digital economy industry and manufacturing have a significant promotion effect on cities with similar economic development levels and adjacent geographical locations, demonstrating a positive diffusion effect.

A theory of value pertinent to the contemporary iPhone era focuses on formal and informal labour circuits. This study extends this framework by examining a labour dispute in an iPhone factory near Bangalore, delving into its dissemination through media and the broader critical political economy surrounding the recent iPhone production in India. Furthermore, it incorporates a geographical perspective into the circuit framework to illustrate the movement of capital and labour in Bangalore, rekindling discussions on core–periphery dynamics in the context of capital and labour migration. Further, this research builds upon the typography of worker-generated content by illustrating a specific category of such content within the iPhone labour dispute. Utilising a critical political economy of media approach, this article aims to assess the broader implications of the updated framework and to open new avenues for research within the emerging field of information communication technologies, cultural production and labour.

New mobility technologies related to autonomous, connected, and shared vehicles have prompted the entry of new players into the automotive industry, which has influenced the industry's traditional configuration of regional status. Under the global value chain (GVC) approach, this research proposes a new framework for defining a ‘core–periphery’ spatial model of the automotive industry. Under that model, based mainly on the key variables of domestic firms linked to new mobility technologies, analysis is made of the comparative status of regions of the European automotive industry traditionally regarded as peripheral (Portugal) and semi-peripheral (Spain). Results indicate that domestic firms located in each of those two regions do not differ in terms of decision-making power, first-level supply positioning, added value of activities, and technological innovation. This implies that the two regions now share the same status within the new (autonomous, connected, and shared) mobility value chain. This has relevant implications for public policies throughout the European automotive industry. Policies should focus on innovation in new mobility technologies and on the creation of an ecosystem adequate to develop strong domestic capabilities around these new mobility technologies, in order to ensure more favourable regional status in the spatial model of this competitive industry.

We develop a core–periphery model with financial frictions and cross-border banking to assess the magnitude of regulatory spillovers and the gains from macroprudential policy coordination. A core global bank lends to its affiliates in the periphery and banks in both regions are subject to risk-sensitive capital regulation. Following an expansionary monetary policy in the core, a countercyclical response in capital requirements in that region induces the global bank to increase cross-border lending. We calculate welfare gains associated with countercyclical capital buffers under a range of policy regimes, including independent policymaking, full coordination, and reciprocity—a regime in which capital ratios set in the core are imposed on the global bank’s affiliates abroad. One of our key results is that, even when regulatory spillovers are strong, reciprocity can make all parties better off if regulators attach a sufficient weight to financial stability considerations. With a standard, utility-based welfare criterion, reciprocity may also perform better than independent policymaking when regulatory spillovers are weak.

Abstract Objective: IDH-wildtype glioblastoma (GBM) is the most aggressive brain tumor in adults. Notable for substantial inter- and intra-tumoral heterogeneity, GBM is highly resistant to standard-of-care treatment. Emerging spatial omics technologies, such as GeoMx Digital Spatial Profiling (DSP), offer novel approaches to interrogate the molecular features of regionally defined cellular ecosystems in cancer. Here, we leverage DSP to study whole-transcriptome gene expression signatures in a spatially defined manner across five IDH-wildtype GBMs. Methods: Formalin-fixed paraffin-embedded tumors were assessed histologically and three spatially distinct areas from each tumor were used to construct a custom “spatial” tissue microarray. From each set of three areas, we identified nine spatially distinct regions of interest (ROIs) for bulk transcriptomic analysis according to two histopathological features: distribution (cellular tumor core or tumor periphery) and vascular proximity (near or far). We evaluated normalized target expression data statistically using linear mixed modeling (LMM) with Benjamini-Hochberg correction. Results: 44 profiled ROIs and 8531 transcriptomic targets were analyzed following quality control trimming. 3D principal component analysis and unsupervised hierarchical clustering analysis showed clustering according to tumor specimen as well as distribution (core versus periphery), thereby substantiating the presence of inter- and intra-tumoral transcriptional heterogeneity. LMM-based differential gene expression analysis identified distinct molecular signatures when comparing core and periphery. Specifically, we observed elevated log2-transformed expression of migration and proliferation markers including LAMC1 (p=0.00297) and FABP7 (p=0.00434) in core ROIs and neurodevelopmental markers including NTRK2 (p=0.00734) and DAAM2 (p=0.00263) in periphery ROIs. Additionally, reactome analysis demonstrated considerable enrichment for the aberrant programmed cell death pathway (R-HSA-9645723, coverage=71.84%) in core ROIs and the MHC class II antigen presentation pathway (R-HSA-2132295, coverage=69.11%) in periphery ROIs. Interestingly, differential gene expression analysis did not reveal any biologically significant targets when subsetting for vascular proximity within cellular tumor core ROIs. However, reactome analysis of this regional subset demonstrated upregulation of various cellular proliferation pathways (G2/M transition, APC/C activity, DNA synthesis, etc.) within vessel-near ROIs when compared with vessel-far ROIs. Conclusions: We preliminarily characterize the molecular heterogeneity of GBM using a spatially guided approach. Ongoing analyses are focused on dataset validation, mixed cell deconvolution, further regional genotyping, and potential biomarkers. Citation Format: Adam Luo, Kelly Casella, Shivang Sharma, Ezra Baraban, Calixto-Hope Lucas, Eugene Shenderov. Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3766.

Core–periphery and nested networks emerging from a simple model of network formation

Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China