Regression Quadratic Assignment Procedure Research Articles

Research on the evolution and determinants of knowledge transfer network in patent-intensive industries

PurposeThis study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.Design/methodology/approachIn this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.FindingsThe results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.Originality/valueThe current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

Research on the spatial patterns and evolution trends of the coupling coordination between digital finance and sustainable economic development in the Yellow River Basin, China.

In the current global context, digital finance (DF) and sustainable economic development (SED) are important topics. The synergies between DF and SED have already been proven. However, the measurement and quantitative analysis of the coupling coordination degree (CCD) of DF and SED have not received sufficient attention to date. Based on data from 55 cities in the Yellow River Basin (YRB) from 2011 to 2021, this study constructs an evaluation index system of DF and SED and measures their level, respectively. The proposed CCD model is then used to measure the CCD between the two systems. In addition, kernel density estimation, Markov chain, σ-convergence, β-convergence, and the quadratic assignment procedure (QAP) method are used to study the spatial pattern, distribution dynamic evolution trend, convergence, and influencing factors of the regional differences in the CCD. The results show that: (1) From 2011 to 2021, the CCD level showed a stable upward trend and regional heterogeneity, and the time stage characteristics were more obvious. (2) The center position and change interval of the overall distribution curve of the kernel density estimation gradually shifted to the right. The Markov transfer probability matrix shows that the CCD is more stable among different levels, indicating a phenomenon of "club convergence". (3) A convergence analysis shows that there are significant σ-convergence, absolute β-convergence, and conditional β-convergence. (4) The QAP regression shows that factors such as the regional differences in GDP per capita have a significant impact on the regional differences in the CCD. This study offers a comprehensive structure that can be used to examine the synergistic effects between DF and SED; the research findings can also provide perspectives for other areas.

Analysis of the characteristics and influencing factors of China's embodied energy flow network

Understanding the flow of embodied energy between provinces in China and the factors affecting the network has an important impact on reducing energy consumption in each province and promoting balanced regional development. This paper uses the multi-regional input–output model to construct the interprovincial embodied energy flow network in China and defines the energy flow between provinces under the trade of products and services. Then, it uses the ecological network model to carry out the ascendency and network environ analysis of the interprovincial embodied energy flow network in China and uses the Quadratic Assignment Procedure regression model to calculate the impact of various factors on the flow network. The main research conclusions are as follows: (1) The embodied energy flow in the middle reaches of the Yangtze River and the Yellow River is relatively high. The embodied energy flow value in Ningxia and Qinghai is low. Zhejiang, Guangdong, and Jiangsu often have the highest embodied energy outflow value, while Inner Mongolia, Shanxi, and other provinces have higher inflows. (2) The center of gravity of through flow in the flow system is biased to Jiangsu, Guangdong, and other places; Gansu, Ningxia, Qinghai, and Hainan are at the edge of the system. (3) The differences in the economic development level, population size, and energy structure have a positive impact on the embodied energy flow network between provinces in China; the geographic distance will have a negative impact on the flow network.

Spatial network structure and driving factors of human settlements in three Northeastern provinces of China

IntroductionUrban human settlements' spatial network structures have emerged as crucial determinants impacting their health and sustainability. Understanding the influencing factors is pivotal for enhancing these settlements. This study focuses on 34 prefecture-level cities in Northeastern China from 2005 to 2020. It employs a modified gravitational model to establish spatial relationships among urban human settlements. Social network analysis techniques, including modularity and the quadratic assignment procedure (QAP) regression model, are introduced to analyze the network's characteristics and driving factors.MethodsA modified gravitational model is applied to create the spatial association network of urban human settlements. Social network analysis tools, along with modularity and the QAP regression model, are utilized to investigate the network's attributes and influencing elements. The study evaluates the evolution of spatial correlation, network cohesion, hierarchy, and efficiency.ResultsThroughout the study period, spatial correlation among urban human settlements in Northeastern China progressively intensified. However, the network exhibited relatively low density (0.217675), implying limited interconnectivity among cities. The average network hierarchy was 0.178225, indicating the need for optimization, while the average network efficiency was 0.714025, reflecting fewer redundant relationships. The analysis reveals the emergence of a polycentric network pattern with core and sub-core cities like Shenyang, Dalian, Changchun, Daqing, and Harbin. The urban network configuration has largely stabilized. The spatial association network showcases the intertwining of "small groups" and community organizations. Geographic proximity and merit-based linkages govern feature flow. Measures such as breaking administrative barriers, reducing flow time and distance, boosting resident income, and increasing government investment are identified to foster balanced network development and structural optimization.DiscussionThe research underscores the increasing spatial correlation and evolving network pattern among urban human settlements in Northeastern China. Despite the observed strengthening correlation, challenges related to network cohesion and hierarchy persist. The formation of a polycentric network signifies positive progress in urban development. The study highlights the importance of proximity and merit-based connections for feature flow. The proposed measures offer pathways to enhance network development and optimize structure, promoting holistic urban settlement growth and sustainability.

Food Trade Network and Food Security: From the Perspective of Belt and Road Initiative

In recent times, there has been a growing interest in the role of food trade in achieving food security. This study presents a comprehensive analysis of the relationship between food trade and food security within the context of the Belt and Road Initiative (BRI). This study employs complex networks alongside the quadratic assignment procedure (QAP) regression method on trade data of 46 countries under BRI for the period 2008 to 2018. The resulting complex network showed an enhancement in the food trade relationships and food security levels after BRI, indicating a mutually positive effect. In addition, the closeness centrality measure showed a strong correlation with food security, indicating that food trade has positive spillovers on food security in the proximate countries. Using geographical distance as an instrumental variable, the QAP regression model proved the positive effects of food trade on food security. More investment in trade infrastructure is recommended to reduce the negative effect of geographical distance on food security.

Evaluating the barrier of typical production factor flow in the Chengdu-Chongqing Urban Agglomeration based on multi-source big data

Promoting the free flow of production factors and improving the efficiency of resource allocation is a necessary requirement for China to achieve high-quality development. Therefore, it is significant to evaluate the barrier of production factors flow and analyze its influencing factors. This study, based on the flow space theory, takes the Chengdu-Chongqing urban agglomeration as an example, constructs the factor flow barrier index (FFBI) with multi-source big data (Baidu Migration data, investment data of listed companies and patent transfer data) and statistical data to evaluate the barriers of three typical product factors flow (labor flow, technology flow and capital flow). Moreover, quadratic assignment procedure regression model is used to analyze how system, economic, society, culture, policy and facility factors affect the barrier of the three typical production factors flow. The results demonstrate that: 1) The intensity of the three typical production factors flow of Chengdu-Chongqing are the highest, and the intensity of three typical production factors flow between the two cities and other cities are higher than that between other 14 cities. 2) In 120 city pairs, 87, 100 and 106 city pairs have positive FFBI of labor flow, capital flow and technology flow, respectively. The FFBI of Chongqing between other cities are mostly positive, indicating the three typical production factors flow are hindered to some extent. Labor flow is less hindered than capital flow and technology flow. 3) Administrative division relationships and administrative level differences have important impact on the barriers of the three typical production factors flow. City pairs belonging to different provincial administrative regions or within the same administrative level have larger FFBI. The barrier of labor and capital are positively affected by the transportation cost and the similarity of the industrial structure, respectively. The framework and findings are of great significance for revealing the formation mechanism of the barrier of production factors flow and provide some guidance for promoting the free flow of production factors and forming a new pattern of high-quality development in the Chengdu-Chongqing Urban Agglomeration.

Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China

Exploring the spatial network structure of tourism flow and its influencing factors is of great significance to the transmission of characteristic culture and the sustainable development of tourism in tourist destinations, especially in backward rural areas. Taking Qiandongnan Miao and Dong Autonomous Prefecture (hereinafter referred to as Qiandongnan Prefecture) as an example, this paper adopts social network analysis and Quadratic Assignment Procedure regression analysis to study the network structural characteristics and influencing factors of tourism flow using online travel blog data. The results show that: (1) There are seasonal changes in tourism flow, but the attractions that tourists pay attention to do not change with the seasons. (2) The tightness of the tourism flow network structure is poor. The core nodes are unevenly distributed, and there are obvious structural holes. (3) The density of the tourism flow network is low. There is a clear core–periphery structure in the network, and the core area has a weak driving effect on the periphery area. There are more cohesive subgroups in the network, but the degree of connectedness between the subgroups varies greatly. (4) Geographical adjacency, transportation accessibility, and tourism resource endowment influence tourism flow network structure. The study found that the influencing factors of tourism flow in rural areas are different from those in urban areas. These results provide useful information for the marketing and development of tourism management departments in rural areas.

Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method

Excessive carbon emissions seriously threaten the sustainable development of society and the environment and have attracted the attention of the international community. The Yellow River Basin is an important ecological barrier and economic development zone in China. Studying the influencing factors of carbon emissions in the Yellow River Basin is of great significance to help China achieve carbon peaking. In this study, quadratic assignment procedure regression analysis was used to analyze the factors influencing carbon emissions in the Yellow River Basin from the perspective of regional differences. Accurate carbon emission prediction models can guide the formulation of emission reduction policies. We propose a machine learning prediction model, namely, the long short-term memory network optimized by the sparrow search algorithm, and apply it to carbon emission prediction in the Yellow River Basin. The results show an increasing trend in carbon emissions in the Yellow River Basin, with significant inter-provincial differences. The carbon emission intensity of the Yellow River Basin decreased from 5.187 t/10,000 RMB in 2000 to 1.672 t/10,000 RMB in 2019, showing a gradually decreasing trend. The carbon emissions of Qinghai are less than one-tenth of those in Shandong, the highest carbon emitter. The main factor contributing to carbon emissions in the Yellow River Basin from 2000 to 2010 was GDP per capita; after 2010, the main factor was population. Compared to the single long short-term memory network, the mean absolute percentage error of the proposed model is reduced by 44.38%.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

The Structure and Evolution of the Tourism Economic Network of the Tibetan Plateau and Its Driving Factors

Tourism is one of the core industries driving the economy on the Tibetan Plateau towards green development. Adopting the improved gravity model, social network analysis, and quadratic assignment procedure, this paper explores the structure and evolution characteristics of the tourism economic network on the Tibetan Plateau from 2015 to 2019. The results are as follows: (1) the tourism economic strength increased over time, but it was generally higher in Qinghai Province and lower in Tibet. (2) The network density exhibited an upwards trend overall. (3) The structure of the tourism economic network presented a significant core–periphery distribution pattern. Xining, Haixi, and Lhasa acted as a bridge between the spatial network structure of the tourism economy and had a structural hole advantage. However, some remote cities, such as Ali, Naqu, and Guoluo, had low network centricity. (4) The quadratic assignment procedure regression analysis showed that the A-level attractions and star-rated hotels significantly advanced the formation of the spatial association. This study concludes with recommendations for promoting tourism economic associations for policy-makers.

Structural characteristics and determinants of an international green technological collaboration network

Green technologies are key factors in mitigating climate change and reducing carbon emissions, but, compared to other technologies, they are more complex and require close cooperation between countries. This research applies social network analysis and a quadratic assignment procedure (QAP) to explore the structural characteristics and determinants of a global green technological collaboration network from 2000 to 2018, based on 456,945 patents. The results show that the number of countries involved in green technology cooperation is rapidly growing; however, the cooperative network is far from complete or regular and presents obvious core–periphery structural features. The United States and Canada, Germany and France, and China and Japan are, respectively, at the center of the Americas, Europe, and Asia. Block model results indicate that the pattern of global green technological cooperation centers on core countries and is led by core countries and strong semi-peripheral countries. QAP regression analysis shows that economic proximity, technological proximity, social proximity, and strict environment regulations are key factors that promote international green technological collaboration. The determinants of green technology cooperation within each continent are also heterogeneous. Lastly, policy recommendations are provided based on the results.

Explaining the directionality of gang violence with court records

PurposeStudies of gang violence typically use police reports to investigate the structure of gang conflict, but overreliance on a singular data source could impede crime control efforts. Extending networked criminology, this study aims to explore what court records reveal about the directionality of gang conflicts.Design/methodology/approachControlling for the presence of a civil gang injunction (CGI), the authors use multivariate quadratic assignment procedure regression models to disentangle factors thought to account for structural patterns of gang violence mapped from 933 prosecutions involving 307 gangs associated with violent conflict affecting the City of Los Angeles (1998–2013). Specifically, the authors compare competitive advantage to the explanatory power of turf proximity.FindingsOne measure of turf proximity outperforms all other explanatory factors – gangs with turf centrally positioned in a turf adjacency matrix are significantly more likely to launch attacks, be victimized and exhibit the highest levels of imbalance in their violent involvements. Regarding competitive advantage, the number of cliques and level of internal conflict are significant. Finally, being subject to a CGI is associated with initiating violence.Originality/valueCourt cases offer a feasible alternative to police data when investigating patterns of intergroup street gang violence.

Network effects in influenza spread: The impact of mobility and socio-economic factors

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016–2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

Analysis on the influencing factors of spatial correlation of haze pollution in North China

Based on the AQI daily data of Northern China from January 1, 2016 to December 31, 2018, this paper uses nonlinear Granger causality test technology to identify the spatial correlation of haze pollution, and from the perspective of relational data, adopt Quadratic Assignment Procedure (QAP) to investigate the influencing factors of the spatial correlation of haze pollution in northern China. This study finds that：(1) QAP correlation analysis shows that geographical distance matrix, difference in the development level of the tertiary industry and difference in the green space area of urban construction are correlated with haze pollution. (2) QAP regression analysis results show that geographic distance matrix, economic development level and environmental regulation level can explain why the adjustment factor of spatial correlation of haze pollution is 2.6%. The influence of geographical distance matrix on the spatial correlation network of haze pollution within the range of 0~300km has passed the significance test, which indicates that haze pollution has broken through the geographical proximity effect, and more distant cities also have spatial correlation of haze pollution.

Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework

This paper empirically estimates the spatial correlation relationship of volatility spillovers and its influencing factors across G20 stock market. We apply GARCH-BEKK model to estimate volatility spillover and construct dynamic volatility networks. The connectedness analysis shows that the spatial linkage of volatility spillover is time varying and has obvious multiple superposition phenomena. As somewhat innovation results, we use the factor analysis method to obtain centrality comprehensive indicators that can clearly depict the risk contagion intensity and risk acceptance intensity. In general, the developed markets are more influential than the emerging markets during periods of turbulence, and the emerging markets are more sensitive to volatility shocks than developed markets during any period. Finally, this paper introduces quadratic assignment procedure (QAP) method to identify the major factors that influence the spatial linkage of volatility spillovers. Results show that geography influences the volatility spatial correlation differently across economic cycles, and the centrality structure factors have greater impact on the spatial correlation than the external economic factors. The QAP regression analysis shows that these influencing factors can explain about 50% of the spatial correlation variation of international financial markets' volatility spillovers.

A network effect on the decoupling of industrial waste gas emissions and industrial added value: A case study of China

With the rapid development of China's industrialization, the extensive development of industrial models has led to increasing environmental pressure. In this paper, we use China as an example, combined with the Tapio elastic analysis method (Tapio model), modified gravity model, social network analysis (SNA), quadratic assignment procedure (QAP) regression and logit model, to explore a network effect on the decoupling of industrial waste gas emissions and industrial added value (industrial GDP). We use the special network structure of industrial waste gas emissions and industrial GDP to capture the network effect. The results show the following: (1) For industrial sulfur dioxide (SO2), nitrogen oxide (NOx) and soot (dust) (SD), most provinces in China show obvious strong decoupling, and provinces with faster economic growth, such as the eastern coastal areas, have a larger proportion of strong decoupling than provinces with poor economic development. (2) According to the network analysis of the industrial GDP, SO2, NOx and SD, economically developed provinces such as Shanghai, Jiangsu, Zhejiang, Beijing and Guangdong are generally located at the core of the network and have a strong spatial spillover effect on other provinces; (3) The QAP regression results show that the spillover effects of industrial SO2, NOx and SD are largely influenced by industry structural similarities, geographic proximity, and the development of economy. (4) The logit regression results show that the decoupling of industrial waste gas emissions will be greatly affected by the spatial network structure of industrial waste gas emissions and industrial GDP. The provinces at the core of the network are more likely to undergo strong decoupling. Finally, based on the research results, this paper provides some recommendations.

The spatial correlation network of capital flows in China: Evidence from China's High-Value Payment System

Using data from China's High-Value Payment System, this paper directly and precisely measures interregional capital flows in China and analyzes their spatial correlation. Using social network analysis methods, this study finds a spatial correlation network between interregional capital flows. The closeness of the network is strong and has a high stability. Different provinces exhibit different positions in the network. Beijing, Jiangsu, Shandong, Shanghai, and Zhejiang are at the relative core, while Qinghai, Xinjiang, Guangxi, Anhui, and Tibet are at the periphery. According to a block model, the eastern provinces belong to a bidirectional spillover block, the middle provinces are in a primary beneficial block, and the western provinces are in a net beneficial block. Based on a quadratic assignment procedure regression analysis, the paper concludes that geographical distance and differences in provinces' fixed asset investments, foreign direct investments, and marketization levels are important factors that impact the spatial correlation of capital flows. The conclusions of this paper are significant for the formulation of a regional coordinated strategy in China.

Social network cohesion and the retreat from Southern vowels in Raleigh

AbstractNetwork research in sociolinguistics suggests that integration in a local community network promotes speakers' retention of local linguistic variants in the context of pressure from external or standard dialects. In most sociolinguistic network research, a speaker is assigned a single score along an index representing the aggregate of several network and other social features. We propose that contemporary network methods in adjacent disciplines can profitably apply to sociolinguistics, thereby facilitating not only more generalizable quantitative analysis but also new questions about therelationalnature of linguistic variables. Two network analysis methods—cohesive blocking and Quadratic Assignment Procedure regression—are used to evaluate the social network factors shaping the retreat from the Southern Vowel Shift (SVS) in Raleigh, North Carolina. The data come from a 160-speaker subset of a conversational corpus. Significant network effects indicate that network proximity to Raleigh's urban core promotes retention of SVS features, and that network similarity between speakers corresponds to linguistic similarity. Contemporary social-network methods can contribute to linguistic analysis by providing a holistic picture of the community's structure. (Networks, sociophonetics, Southern Vowel Shift, dialect contact)*

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members.

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Explaining criminal networks: Strategies and potential pitfalls

This article offers some remarks on a few critical issues related to explanation in criminal network research. It first discusses two distinct perspectives on networks, namely a substantive approach that views networks as a distinct form of organisation, and an instrumental one that interprets networks as a collection of nodes and attributes. The latter stands at the basis of Social Network Analysis. This work contends that the instrumental approach is better suited to test hypotheses, as it does not assume any structure a priori, but derives it from the data. Moreover, social network techniques can be applied to investigate criminal networks while rejecting the notion of networks as a distinct form of organisation. Next, the article discusses some potential pitfalls associated with the instrumental approach and cautions against an over-reliance on structural measures alone when interpreting real-world networks. It then stresses the need to complement these measures with additional qualitative evidence. Finally, the article discusses the use of Quadratic Assignment Procedure regression models as a viable strategy to test hypotheses based on criminal network data.