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This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in the pre-pandemic period but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. In the spatial distribution network of the tourism economy in Fujian Province, cities have different positions and roles, so development strategies should be differentiated according to the characteristics of each city. For example, more supportive policies should be introduced to help the tourism development of cities with disadvantages. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level. [ABSTRACT FROM AUTHOR]

With the deceleration of urban expansion, the adaptive reuse of industrial heritage buildings has emerged as a novel area of research. In previous times, the majority of approaches to adapting industrial heritage buildings relied on experiential knowledge, which lacked the ability to objectively assess the relationship between spaces and engage in rational planning. However, the social network analysis method offers an objective and comprehensive means of perceiving the spatial structure and analyzing its issues from a detached perspective. This study presents a proposal for addressing three spatial challenges encountered during the conversion of industrial heritage buildings into public buildings. It also suggests spatial optimization strategies to overcome these challenges. The Sanlinqiao Thermal Bottle Factory is selected as the research subject, and a spatial network structure model is constructed to analyze the existing issues using the social network analysis method. The proposed spatial optimization strategies are then applied, and the optimized space is evaluated through a re-analysis of the spatial layout. The spatial utilization rate has been significantly improved, leading to an effective enhancement of the spatial vitality of the site. This study presents a spatial strategy aimed at converting industrial heritage buildings into public buildings, thereby offering valuable insights for similar projects involving the transformation of industrial heritage sites. [ABSTRACT FROM AUTHOR]

Spatial competition considerations are important in hotel location selection. This study proposes and demonstrates a method of colocation network analysis to quantify the potential encroaching effect of spatial inter- and intra-competition between units of business brands that seek expansion. The environmental context of the study is a network of the top five budget hotel brands in the Beijing hotel market. The results reveal that brands implement different strategies in their hotel placement, which are subject to different levels of the encroaching effect. However, the method is applicable in a variety of hospitality settings, specifically in those that involve the development strategies of chain brands. The simulation capability of the method can assist hospitality brands in assessing the outcomes of a proposed development and, thus, aid hoteliers in the spatial allocation of new units with the least adverse effects on their existing business networks. [ABSTRACT FROM AUTHOR]

Constructing digital models of public spaces of social networks found in traditional villages helps us explore the logic behind the interactions that occur within various relationships as well as achieve the optimization of spatial layouts and the equalization of the different use needs that social groups have. However, this analytical method has not been fully studied in the field focusing on the rationality of public spaces in traditional villages. This paper takes the traditional village protection demonstration area in Mentougou District, Beijing, China, as the object of research and selects three different forms of traditional villages to be analyzed. It tries to excavate the universal laws and unique differences in the models of the different forms of traditional villages and establish models for quantitative research, such as index calculation, so as to increase the depth and accuracy of research and embody the characteristic laws of the spaces studied in terms of the nature of the structures and relationships that are part of these spaces. The results show that the spatial characteristics of the different forms of traditional villages are obviously different from the relevance, equalization, and connectivity of spatial networks. The cluster form of traditional villages tends to focus on the villagers' use demands and the assessment of the spatial status quo by increasing the number of spatial nodes and transforming spatial functions in order to achieve higher equalization. The dispersed form of traditional villages tends to focus on optimizing the relevance of spatial network structures. The linear form of traditional villages tends to establish public spaces so as to increase the depth and accuracy of research. Villages ought to establish direct links between public spaces in order to increase the connectivity of traditional villages. This study provides a rational basis for the differentiated planning decisions of traditional villages and lays the foundation for the promotion of the smooth and sustainable development of regional villages. [ABSTRACT FROM AUTHOR]

Land, as an essential resource for social, economic and ecological activities, is regarded as a key factor in material production. Against the background of rapid social and economic transition, land-use carbon emissions have gradually drawn due attention. However, few studies have been conducted to explore the spatial relationship of land-use carbon emissions at the sub-regional level, especially within Sichuan Province, China. This study is aimed at unpacking the spatial network of land-use carbon emissions in Sichuan Province by employing the panel data from 2006 to 2021 and using the method of Social Network Analysis. The results indicate that the net land-use carbon emissions of various prefecture-level divisions in Sichuan generally showed an inverse and asymmetrical "V-shaped" trend. The network correlation was improved and the stability was enhanced, gradually developing into a multi-centric structure. In addition, the spatial relationship among different clusters in the network undergoes a transition from intra-regional to inter-regional spillover. Based on these findings, the carbon balance zoning policy was discussed to provide references for how to coordinate roles and positions in the network when optimizing land-use carbon emission management policies in sub-regional areas with rapid social and economic development. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta Urban Agglomeration (YRDUA) is the core area for the development of China's digital economy. This paper constructed a five-dimensional evaluation index system and measured the digital economy development level of the YRDUA. A modified gravity model social network analysis was applied to analyze the structure of the digital economy network. The results show that (1) the level of digital economy in the YRDUA has increased during 2011–2021, with Hangzhou having the highest digital economy development level of 0.513 in 2021. (2) The digital economy network density of the YRDUA increased from 0.155 in 2011 to 0.158 in 2016, and then decreased to 0.151 in 2021, showing an "inverted U-shaped" trend of first increasing and then decreasing. (3) Shanghai, Suzhou, Nanjing, Hangzhou, and Wuxi play the role of "bridge" in the digital economy network, and the pattern of linkage development of neighboring cities driven by the central cities of Shanghai, Nanjing, Suzhou, Hangzhou, Ningbo, and Hefei has basically formed. These findings of the study provide data support and reference for the coordinated development of the digital economy in the YRDUA and are of theoretical and practical significance for the synergistic development of cities. [ABSTRACT FROM AUTHOR]

Railway carbon emissions reduction is of great significance. In this study, carbon emission efficiency in railway transportation in China's 31 provinces is measured for 2006–2019 based on an unexpected output slack-based measure (SBM) model. A gravity matrix of the spatial correlation network for carbon emission efficiency is constructed using the modified gravity model, the spatial network structure is explored using social network analysis, and the factors influencing the spatial network are analyzed using the quadratic assignment procedure (QAP) model. Based on the results, several conclusions can be drawn: (1) the carbon emissions efficiency of railway transportation in China increased periodically during the study period, but there are still great differences between regions. (2) The carbon emission efficiency in railway transportation shows significant characteristics of spatial correlation networks. (3) The inter-provincial associations gradually increased, while there are still large regional differences in the spatial correlation network. (4) Differences in spatial adjacency, economic development and scientific and technological advancement have significant positive impacts on the spatial correlation network. This research will help policy makers formulate relevant policies to promote the regional coordinated development of low-carbon railway transportation. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system. [ABSTRACT FROM AUTHOR]

This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions. [ABSTRACT FROM AUTHOR]

Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008-2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold-hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB.

A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the 'low-frequency city' and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city's intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the 'low-frequency city' to the so-called 'high-frequency city' has been introduced, but hardly any research investigated methods to estimate a city's frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling a disorder ensemble of quantum dots (QDs) as a special kind of Random Geometric Graphs (RGG) with weighted links. We compute any link weight as the overlap integral (or electron probability amplitude) between the QDs (=nodes) involved. This naturally leads to a weighted adjacency matrix, a Laplacian matrix, and a time evolution operator that have meaning in Quantum Mechanics. The model prohibits the existence of long-range links (shortcuts) between distant nodes because the electron cannot tunnel between two QDs that are too far away in the array. The spatial network generated by the proposed model captures inner properties of the QD system, which cannot be deduced from the simple interactions of their isolated components. It predicts the system quantum state, its time evolution, and the emergence of quantum transport when the network becomes connected. [ABSTRACT FROM AUTHOR]

Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China's health economy, and then find evidence that affects patient mobility., Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books . The gravitational value of China's health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis., Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects., Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces' positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.

Based on the carbon emission data in the Beijing–Tianjin–Hebei urban agglomeration from 2007 to 2016, this paper used the method of social network analysis (SNA) to investigate the spatial correlation network structure of the carbon emission. Then, by constructing the synergetic abatement effect model, we calculated the synergetic abatement effect in the cities and we empirically examined the influence of the spatial network characteristics on the synergetic abatement effect. The results show that the network density first increased from 0.205 in 2007 to 0.263 in 2014 and then decreased to 0.205 in 2016; the network hierarchy fluctuated around 0.710, and the minimum value of the network efficiency was 0.561, which indicates that the network hierarchy structure is stern and the network has good stability. Beijing and Tianjin are in the center of the carbon emission spatial network and play important "intermediary" and "bridge" roles that can have better control over other carbon emission spatial spillover relations between the cities, thus the spatial network of carbon emissions presents a typical "center–periphery" structure. The synergetic abatement effect increased from −2.449 in 2007 to 0.800 in 2011 and then decreased to −1.653 in 2016; the average synergetic effect was −0.550. This means that the overall synergetic level has a lot of room to grow. The carbon emission spatial network has a significant influence on the synergetic abatement effect, while increasing the network density and the network hierarchy. Decreasing the network efficiency will significantly enhance the synergetic abatement effect. [ABSTRACT FROM AUTHOR]

Existing spatial community detection algorithms are usually modularity based. Motivated by different applications, these algorithms build appropriate spatial null models to describe spatial effects on the connection of nodes. Then, by choosing certain modularity maximizing strategies, they try to find interesting community structures hidden behind the null models. In this paper, a novel structural similarity-based spatial network community is defined, which is based on the shared neighbors of nodes. In addition, there are two other special node roles defined: the spatial hub and outlier. Then, a density and adjacency expansion-based spatial structural community detection algorithm for networks (DASSCAN) is proposed for mining these communities, hubs and outliers. DASSCAN uses structural similarity to measure the relationship between nodes, and then, structurally similar and spatially adjacent nodes are merged into communities using a density-based clustering method and spatial adjacency expansion strategy. Comparative experiments on two kinds of Chinese train line networks clarified the accuracy and efficiency of DASSCAN in finding the spatial structural communities, spatial hubs and outliers. The communities found can be used to uncover more interesting spatial structural patterns, and the hubs and outliers are more accurate and have more valuable meanings. [ABSTRACT FROM AUTHOR]