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Water and energy consumption and carbon emissions caused by humans have vital impacts on the natural environment. Due to the dramatic global environmental pollution problem, understanding the relationship between these factors is emerging as an important approach to realising sustainable development. However, with the strengthening of interregional trade links, it is difficult to manage and evaluate their relationship. Therefore, from the perspective of regional and industrial sectors, using the multiregional input-output (MRIO) model and social network analysis (SNA), our research explores an innovative analytical methodology to evaluate the characteristics of the energy-water-carbon spatial network, and a scheme is proposed to improve it. The results demonstrate that the characteristics of water scarcity and energy enrichment could lead to a net inflow of the water footprint and a net outflow of the energy and carbon footprints. Moreover, traditional high-energy-consumption industrial sectors contribute significantly to the energy and carbon footprints. The energy-water-carbon spatial network correlation is low and unstable, and it lacks rationalisation and balance in resource-based areas. Network-based energy-water-carbon research provides more insights toward understanding the carbon emission reduction responsibilities of industrial supply chains. Our findings provide a reference for reducing the energy-water-carbon footprint and achieving the carbon reduction goal of China. [ABSTRACT FROM AUTHOR]

Over the last two decades, researchers from different fields have placed increasing attention on the relationship between social networks and space. Despite this growth, these studies mostly failed to consider one of the core elements of spatial analysis: cartographic visualization and interpretation. This study shows how integrating cartographic representation can deepen the understanding of the structure of spatial social networks. It focuses on the case of fire management activities in the Los Tuxtlas Biosphere Reserve in Southern Mexico based on data obtained from diverse local, municipal, state and federal level authorities. First, the paper analyses the connections between actors and their relation to spatial distance through structural social network analysis. Second, a geographic information system integrates the network in maps to revise the relation between its structure and the study area’s geography. It outlines the relevance of considering geographic features, particularly from physical geography, in analyzing social structures. [ABSTRACT FROM AUTHOR]

When considering disintegration of spatial networks, the topological relationships between nodes and their geographical positions are taken into account. In the case of regional attacks, the targeted nodes are determined based on the size of the region and the geographical relationships between nodes. In this paper, a new metric is proposed on top of region centrality (RC) to describe the importance of nodes within local regions in spatial networks. We define the weighted sum of the number of connections between nodes within a local region and the number of connections between nodes within the local region and nodes outside the local region as local connectivity. This metric integrates topological information with geographical information. From a mathematical perspective, RC is a special case of local connectivity. In addition, we propose a hybrid spatial network disintegration strategy based on two metrics, namely local connectivity and RC. From experimental results, our strategy outperforms traditional approaches and demonstrates a stronger ability to completely disintegrate the network. [ABSTRACT FROM AUTHOR]

Purpose: Our study focuses on stress contagion in vocational school classes, examining how students' stress experiences affect their spatial classmates. For this purpose, we apply a novel formal network model that allows us to differentiate between endogenous and exogenous peer effects in the stress contagion process. Using the network model, we investigate effects on students' stress levels, considering the stress and coping experiences of spatial peers as well as didactic-methodological context factors. Methods: We test our statistical model using secondary data collected in a study involving 53 students from two classes at a public German vocational training school. The students' experiences of stress (time pressure, pressure to succeed) and coping strategies (understanding of the subject matter, self-confidence) were captured using the experience sampling method, while classroom characteristics (e.g., teacher instruction, cooperative work) were recorded through video-based analysis of lessons. Utilizing the panel data, we employ maximum likelihood estimation to assess the spatial peer effects model for both classrooms. Results: Among other findings, all model specifications revealed significant peer effects for both stress measures, indicating that the higher the stress experience of immediate peers in the classroom, the higher the individual stress experienced by the students. Concerning the considered context factors, we found, for example, that increased cooperative work leads to higher levels of stress experience. Conclusion: From a substantive perspective, our results underscore the role of peer-to-peer contagion in the vocational classroom and thus suggest a nuanced examination of cooperative practices. From a research methodology perspective, our approach illustrates how various methods (such as experience sampling, video-based classroom observation, and spatial network information) complement and enrich each other, highlighting the added value of our network analytical approach [ABSTRACT FROM AUTHOR]

Abstract Purpose Our study focuses on stress contagion in vocational school classes, examining how students’ stress experiences affect their spatial classmates. For this purpose, we apply a novel formal network model that allows us to differentiate between endogenous and exogenous peer effects in the stress contagion process. Using the network model, we investigate effects on students’ stress levels, considering the stress and coping experiences of spatial peers as well as didactic-methodological context factors. Methods We test our statistical model using secondary data collected in a study involving 53 students from two classes at a public German vocational training school. The students’ experiences of stress (time pressure, pressure to succeed) and coping strategies (understanding of the subject matter, self-confidence) were captured using the experience sampling method, while classroom characteristics (e.g., teacher instruction, cooperative work) were recorded through video-based analysis of lessons. Utilizing the panel data, we employ maximum likelihood estimation to assess the spatial peer effects model for both classrooms. Results Among other findings, all model specifications revealed significant peer effects for both stress measures, indicating that the higher the stress experience of immediate peers in the classroom, the higher the individual stress experienced by the students. Concerning the considered context factors, we found, for example, that increased cooperative work leads to higher levels of stress experience. Conclusion From a substantive perspective, our results underscore the role of peer-to-peer contagion in the vocational classroom and thus suggest a nuanced examination of cooperative practices. From a research methodology perspective, our approach illustrates how various methods (such as experience sampling, video-based classroom observation, and spatial network information) complement and enrich each other, highlighting the added value of our network analytical approach

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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]

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.

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]

Improving green water use efficiency is based on the synergy of reducing pollutants and carbon emissions, and effectively identifying its spatial development structure is a prerequisite for realizing the green transformation of water management. This paper adopted the slacks-based measure- "energy-environment-economy" (SBM-3E) model with carbon emission and pollution indicators as unanticipated outputs to measure the green water use efficiency of the 11 provinces (cities) in the Yangtze River Economic Belt from 2000 to 2018, then analyzed the efficiency network structure evolution by using the social network analysis method, and finally examined the drivers of efficiency networks. The results show that (1) the value of green water utilization efficiency decreases from 0.78 to 0.51, and its spatial distribution is downstream > middlestream > upstream, with the values of 0.41, 0.61, and 0.86, respectively. (2) The overall spatial network efficiency is gradually interconnected, with the density increasing from 0.32 to 0.6; it has undergone a structural evolution from "flat" to "inverted tower." The core–edge structure of the main body status within the basin has been strengthened. (3) The efficiency network is significantly influenced by water resource endowment, industrial structure, and foreign investment level. The conclusions are expected to provide useful insights for designing the basin's water protection policy and greening the region's development. [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]

Habitat loss and fragmentation are growing global threats to natural habitats and their networks, posing significant challenges to biodiversity conservation. Globally, ponds are sharply declining in number because their small size makes them highly vulnerable to land use changes. While it is generally agreed that connectivity in habitat networks is crucial for sustaining biodiversity, the effect of connectivity on biodiversity patterns over small‐scaled habitat networks has so far received less attention given the general assumption that metacommunities lack spatial structuring on small scales. In this study, we tested whether this holds for multiple passively and actively dispersing organism groups in a well‐delineated pond metacommunity of 54 bomb crater ponds situated within 1 km. We investigated the influence of space and environment on species richness and metacommunity structure in these ponds, which share similar age, size, and shape and are subject to strong environmental gradients, making it an ideal study system. We specifically examined the impact of network centrality on taxonomic richness and eigenvector‐based spatial arrangement on metacommunity structure across different organism groups, including prokaryotes, microeukaryotes, zooplankton, macroinvertebrates, and amphibians. We found that while environmental filtering is the primary driver of community dynamics, there is also a significant spatial signal, particularly for passively dispersing groups, demonstrating the role of the central‐peripheral connectivity gradient. These findings highlight the importance of studying and protecting ponds as parts of a network rather than focusing on individual ponds. [ABSTRACT FROM AUTHOR]

The improved gravity model was used to determine the spatial correlation of carbon emission efficiency (CEE) among cities, and then the spatial correlation network of CEE of 284 Chinese cities from 2006 to 2019 was constructed for the first time. Based on the social network analysis method, the structural characteristics and influencing factors of the network were analyzed. It was found that: (1) The spatial correlation of CEE at city level is obvious, the overall correlation intensity is increasing, and the aggregation of correlation is significant. (2) The network shows a core–edge structure, cities with high economic development, such as Shenzhen, Beijing, Nanjing, Guangzhou and Shanghai, occupying the center of the network, while cities with relatively weak development such as Heihe, Jiuquan, Baotou and Baoshan are at the edge. (3) The network can be divided into four plates, with sparse association within the plate and the strong spillover effect between the plates. (4) Spatial adjacency relationship, differences in economic development, industrial development, green technology innovation and energy consumption have significant positive effects on establishing a spatial network, while geographical distance has significant negative effects on it. The results can help governments to clearly realize the correlation of CEE between cities and identify the role that each city can play in improving the overall CEE, so as to provide guidance for governments to further formulate accurate carbon emission reduction policies. [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]

The identification of the technical cooperation mode in the field of hydrogen energy is of great importance in guiding technological innovation. Based on the social network analysis (SNA) and spatial network method, this paper constructs the technical cooperation graph of China's industry-university-research (IUR) tripartites in the field of hydrogen technology. Based on the performed graphs, it shows the game and evolution of hydrogen technology innovation in three stages, respectively 2000–2010, 2011–2015 and 2016–2019. Meanwhile, this paper calculates the changes of network indicators and found that: (1) the network density showed a downward trend, from the initial 0.062 to 0.003. (2) The degree centrality of the network decreased from 14.7% to 5.7%. (3) The average path length and clustering coefficient indicate that the internal connections within the network were relatively close from 2000 to 2010, and had small-world characteristics. From 2016 to 2019, although the clustering coefficient increased, the average path length also increased significantly, indicating that the internal connections of the network did not become closer. From the network characteristics, the collaborative innovation network of China's hydrogen energy industry continues to expand in scale and scope, and the cooperation relationship is stable. However, it shows the characteristics of low network density and loose cooperation relationship, gradually developing into a scale-free network. Developed provinces and cities in North and East China occupy core positions in the industry-university-research network, while other regions are gradually expanding their cooperation efficiency; however, the overall cooperation frequency is relatively low in Northeast China, and most provinces and cities in South and Northwest China are in edge positions with poor collaboration capabilities. • Network analysis sheds light on China's hydrogen energy tech cooperation via patent. • Collaborative innovation in hydrogen technology evolves through three stages. • Tight internal connections with small-world properties seen in network (2000–2010). • Network's internal connections didn't become closer (2016–2019). • Network transforms from a stable and expanding structure into a scale-free network. [ABSTRACT FROM AUTHOR]

Wildlife demography is influenced by behavioural decisions, with sleep being a crucial avian behaviour. Avian species use roost sites to minimize thermoregulation costs, predation risk and enhance foraging efficiency. Sleep locations are often reused, forming networks within the home range. Our study, focusing on female eastern wild turkeys (Meleagris gallopavo silvestris) during the reproductive season, used social network analysis to quantify both roost site selection and network structure. We identified roost networks which were composed of a small percentage of hub roost sites connecting satellite roosts. Hub roosts were characterized by greater values of betweenness (β = 0.62, s.e. = 0.02), closeness (β = 0.59, s.e. = 0.03) and eigenvalue centrality (β = 1.15, s.e. = 0.05), indicating their importance as connectors and proximity to the network’s functional centre. The probability of a roost being a hub increased significantly with greater eigenvalue centrality. Female wild turkeys consistently chose roost sites at lower elevations and with greater topographical ruggedness. Hub roost probability was higher near secondary roads and further from water. Our research highlights well-organized roost site networks around hub roosts, emphasizing the importance of further investigations into how these networks influence conspecific interactions, reproduction and resource utilization in wild turkeys.

License Plate Recognition (LPR) is one of the tough tasks in the field of computer vision. Although it has been around for quite a while, there still lies the challenges when we have to deal with; the harsh environmental conditions like snowy, rainfall, windy, low light conditions etc. as well as the condition of the plates which includes the bent, rotated, broken plates. The performance of the recognition and detection frameworks take a significant hit when it is concerned with these conditional effects on the license plate. In this paper, we introduced a model to improve our accuracy based on the Chinese Car Parking Dataset (CCPD) using 2 separate convolutional neural networks. The first CNN will be able to detect the bounding boxes for the license plate detection using Non-Maximus Suppression (NMS) to find the most probable bounding area whereas the second one will take these bounding boxes and use the spatial attenuation network and character recognition model to successfully recognize the license plate. First, we train the CNN to detect the license plates, then use the second CNN to recognize the characters. The overall recognition accuracy was found to be 89% in the CCPD dataset. [ABSTRACT FROM AUTHOR]

This study employs the 2012 to 2016 commuting flows of Florida to reexamine excess commuting (EC) through multiscale complex network analysis. The results reveal significant discrepancies in urban connectivity, hierarchy, and labor market geography between the actual (ACN) and optimal commuting networks (OCN). Compared to ACN, OCN illustrates an overly simplified and isolated polycentric commuting system. This is evident through reduced overall network connectivity, a restructured centrality distribution pattern, and fragmented community divisions. Consequently, these results underscore the importance of delving into the economic implications of commuting within the conventional EC framework. [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]

Understanding how individuals within populations are connected genetically and through shared space-use is critical to understanding the demographic patterns of at-risk populations. In recent years, non-invasive genetic sampling methods have allowed us to begin addressing these questions. Using a network analysis framework, we examined the spatial co-occurrence and genetic relatedness of boreal and central mountain caribou (Rangifer tarandus) in populations varying in sizes and trend from across western Canada. Using 15 microsatellites loci and a sex-specific marker, we developed pedigree networks for each population, including first to third order relationships. We constructed networks of individual spatial co-occurrence, and using multilayer network analysis, we determined the degree to which caribou that co-occurred spatially were related. Caribou populations varied in the extent and classes of familial relationships which co-occurred in space. In most populations, spatial co-occurrences were greater than expected only for parent-offspring relationships. A sex-specific analysis of the longest sampled population revealed that this was driven by a significant overlap between mothers and their offspring. Whereas in populations where very few parent-offspring relationship were detected, no significant overlap was found between any relationship categories. The lack of overlap in these populations may be an indication of poor calf survival. On the other hand, the higher degree of overlap with more distant relations (i.e., grandparents) seen in some populations may indicate a population that lacks mobility. Our research presents a new approach to explore both spatial and genetic overlap at the individual level in support of the conservation of threatened populations.

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]

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. On the basis of it, we also develop the GTS+ extension to support similarity metrics that involve both spatial and temporal information. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches. [ABSTRACT FROM AUTHOR]

Abstract This paper focuses on the character of multi-level pedestrian accessibility and walkability within the public and quasi-public realm in Hong Kong, using the particular case of the redevelopment of Taikoo Place in Quarry Bay. In an era when the value of public space as a space for public activity and debate is coming under increasing scrutiny, both in Hong Kong and internationally, this paper explores multi-level interior and exterior public and quasi-public realm, as a hybrid form of interior open and transit space in a commercial setting that has becomes a central part of the city’s planning, designed to both serve and profit from the high level of pedestrian traffic. This paper draws on empirical research of pedestrian behaviour patterns to interrogate the value and significance of linked-spaces within commercial districts, both to the users and the real estate travelled through, using the city of Hong Kong as a reference and the specific case of Taikoo Place.

Abstract: Illicit wildlife trafficking poses a threat to the conservation of species and ecosystems, and represents a fundamental source of biodiversity loss, alongside climate change and large-scale land degradation. Despite the seriousness of this issue, little is known about various socio-cultural demand sources underlying trafficking networks, for example the forthright consumption of endangered species on different cultural contexts. Our study illustrates how wildlife trafficking represents a wicked problem at the intersection of criminal enforcement, cultural heritage and environmental systems management. As with similar network-based crimes, institutions are frequently ineffective at curbing wildlife trafficking, partly due to the lack of information detailing activities within illicit trading networks. To address this shortcoming, we leverage official government records documenting the illegal trade of reptiles in Colombia. As such, our study contributes to the understanding of how and why wildlife trafficking persists across robust trafficking networks, which are conduits for a broader range of black-market goods. Leveraging geo-spatial data, we construct a multiplex representation of wildlife trafficking networks, which facilitates identifying network properties that are signatures of strategic trafficker behavior. In particular, our results indicate that traffickers’ actions are constrained by spatial and market customs, a result which is apparent only within an integrated multiplex representation. Characteristic levels of sub-network coupling further indicate that traffickers strategically leverage knowledge of the entire system. We argue that this multiplex representation is essential for prioritizing crime enforcement strategies aimed at disrupting robust trade networks, thereby enhancing the effectiveness and resources allocation of institutions charged with curbing illicit trafficking. We develop a generalizable model of multiplex criminal trade networks suitable for communicating with policy makers and practitioners, thereby facilitating rapid translation into public policy and environmental conservation efforts.

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.

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]

Abstract The ERASMUS program is the most extensive cooperation network of European higher education institutions. The network involves 90% of European universities and hundreds of thousands of students. The allocated money and number of travelers in the program are growing yearly. By considering the interconnection of institutions, the study asks how the program’s budget performs, whether the program can achieve its expected goals, and how the program contributes to the development of a European identity, interactions among young people from different countries and learning among cultures. Our goal was to review and explore the elements of network structures that can be used to understand the complexity of the whole ERASMUS student mobility network at the institutional level. The results suggest some socioeconomic and individual behavioral factors underpinning the emergence of the network. While the nodes are spatially distributed, geographical distance does not play a role in the network’s structure, although parallel travelling strategies exist, i.e., in terms of preference of short- and long-distance. The European regions of home and host countries also affect the network. One of the most considerable driving forces of edge formation between institutions are the subject areas represented by participating institutions. The study finds that faculties of institutions are connected rather than institutions, and multilayer network model suggested to explore the mechanisms of those connections. The results indicate that the information uncovered by the study is helpful to scholars and policymakers.

This paper focuses on the character of multi-level pedestrian accessibility and walkability within the public and quasi-public realm in Hong Kong, using the particular case of the redevelopment of Taikoo Place in Quarry Bay. In an era when the value of public space as a space for public activity and debate is coming under increasing scrutiny, both in Hong Kong and internationally, this paper explores multi-level interior and exterior public and quasi-public realm, as a hybrid form of interior open and transit space in a commercial setting that has becomes a central part of the city's planning, designed to both serve and profit from the high level of pedestrian traffic. This paper draws on empirical research of pedestrian behaviour patterns to interrogate the value and significance of linked-spaces within commercial districts, both to the users and the real estate travelled through, using the city of Hong Kong as a reference and the specific case of Taikoo Place. [ABSTRACT FROM AUTHOR]

Due to the 2019 new coronavirus disease (COVID-19) pandemic, tourism is undergoing fundamental changes that are affecting tourism research. This situation calls for in-depth analyses of tourism research. Scholars have already published review studies on COVID-19-related research within the tourism field; however, these studies do not connect findings, such as the research focus, research methodology and target group, to form a research profile, and the geographical patterns of the findings are not identified. study, COVID-19-related tourism studies were collected and analyzed in depth following the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) method. In addition, data-driven methods, such as spatial multilayer networks, frequent patterns and content-based analyses, were applied to identify research profiles and their geographic patterns. This study pointed out the role of geographic patterns in tourism research, going beyond the research of the authors. Moreover, topics, focus destinations, applied methodologies and employed data sources have relevant geographic patterns. Four dominant research profiles that show that a shift can be observed in tourism research toward data sources and research methods were identified. Due to COVID-19, the strengthening of the application of quantitative methods and employment of secondary data sources are needed. [ABSTRACT FROM AUTHOR]

The spatial network has numerous nodes and complex links, which makes it more suitable for the Multipath Transmission Control Protocol (MPTCP). As an extension of the traditional TCP protocol, this protocol can aggregate the bandwidth of multiple paths to improve transmission performance. However, the traditional network architecture has limited support for MPTCP, making it impossible to select a good transmission path for the subflow according to the current global network status, so it is easy to cause subflow collision. By separating the control plane and data plane, the Software Defined Network (SDN) architecture can obtain the global network status and better manage the network. The introduction of SDN can solve the problem that traditional networks cannot optimize subflow transmission based on the global network status. However, the existing SDN-based solutions mainly optimize the terrestrial network and take the available bandwidth of the network as the key reference factor, which is not suitable for the network environment with a large scale of time and space such as the spatial network. Besides, due to the high-speed movement of satellite nodes, frequent link switching will cause the selected path to fail and affect the subflow transmission on the path. For this reason, a dynamic subflow allocation strategy for multi-path transmission in SDN-based spatial networks is proposed in this paper, which uses the SDN controller to monitor and analyze the network status, and selects multiple optimal and disjoint transmission paths for the subflow based on the delay and bandwidth of the path to improve the link utilization and load balancing. Then, the strategy uses the predictable characteristics of the spatial network topology to predict the link to be disconnected, reselects the optimal path for the subflow, and solves the problem that the subflow cannot be continuously transmitted due to link switching. The experimental results show that the strategy in this paper is more suitable for the spatial network, because the selected path has lower delay and higher bandwidth. It solves the collision problem of subflows, and makes the subflow transmission have better stability in the highly dynamic spatial network. [ABSTRACT FROM AUTHOR]

We developed a novel spatial stream network geographically weighted regression (SSN-GWR) by incorporating stream-distance metrics into GWR. The model was tested for predicting seasonal total nitrogen (TN) and total suspended solids (TSS) concentrations in relation to watershed characteristics for 108 sites in the Han River Basin, South Korea. The SSN-GWR model was run with the average seasonal water quality parameters from 2012 through 2016 and was validated with the data from 2017 through 2021. The model fit among ordinary least square regression, standard GWR (STD-GWR), and stream distance weighted SSN-GWR were compared based on their ability to explain the variation of seasonal water quality parameters. We also compared residual spatial autocorrelations as well as various error parameters from these models. Compared to the STD-GWR model, the SSN-GWR model generally provided better model fit, reduced residual spatial autocorrelation, and lessened overall modeling errors. Results show that the spatial patterns of model fit, as well as various coefficients from the upstream distance weighted regressions, capture local patterns as a product of upstream–downstream relations. We demonstrate that a successful model could be developed by integrating stream distance into the GWR, which not only improves model fit but also reveals realistic hydrological processes that relate watershed characteristics to water quality along with the stream network. The local variations in model fit derived from this work can be used to devise fine-scale interventions for water quality improvements in a spatially heterogeneous complex river basin. [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.

Searching for neighbors for a query node in a spatial network is a fundamental problem and has been extensively investigated. However, most existing works focus only on the node level when conducting such a query and rarely pay attention to the social relations among the neighbors. We argue that a user, in some cases, is more likely to engage in some activities collectively, i.e., going to the bar with friends rather than alone. For this reason, we consider the neighbor searching problem at a community level in this paper and examine a new problem: Neighboring Geosocial Communities Search (NGCS) over large spatial graphs. Specifically, given a parameter $n$ and query node $q$ , we aim to find the top- $n$ nearest communities for $q$ . Moreover, in each returned community, nodes have cohesive relations with each other and are covered by a minimum covering circle (MCC) whose radius is less than $r$ . It is obvious that the NGCS problem finds its standard applications in marketing and other scenarios but it is very challenging for large spatial graphs because it requires detecting all qualified cohesive user communities. Therefore, in this paper, we adopt a local search approach to reduce the difficulty. The introduced algorithm finds the top- $n$ neighboring geo-social communities through a progressive search in the graph without thoroughly examining the graph. Analyses show that the complexity of the algorithm is decreased by an order of magnitude. Extensive experiments on real social networks confirm the superiority and effectiveness of our solutions.

The prevalence of poverty and inequality in megacities often manifests as high levels of energy poverty and disparities in household energy consumption. A comprehensive survey of 2054 households in Beijing was conducted in 2023 to examine the dynamics of energy poverty within such an urban settings. As one of the world's most influential megacities, Beijing offers a distinct and insightful case study for understanding energy poverty in a rapidly urbanizing global context. Utilizing the 10 % and low-cost/high-income (LIHC) methods, we estimated that the energy poverty rate in Beijing ranges between 16.07 % and 17.53 %, with the highest rates observed in suburban areas. In addition, we explored the spatial differences in energy poverty and the root causes. Our research reveals a significant correlation between economic and geographical factors and the spatial distribution of energy poverty. Furthermore, our findings suggest that larger populations tend to be associated with reduced disparities in energy poverty across different areas. Therefore, we suggest promoting the use of energy-efficient installations, implementing time-sensitive heating systems, and providing electricity and hygienic cooking facilities. • We surveyed 2054 households to measure energy poverty in Beijing, resulting in a range of 16.07–17.53 %. • The spatial structure of energy poverty is closely related to economic and geographical distance. • Energy expenditure, floor area, and heating methods significantly influence spatial differences in Beijing's energy poverty. • As Population size increases, household energy usage is shared, reducing spatial differences in energy poverty. [ABSTRACT FROM AUTHOR]

A metropolitan area refers to the central urban area and its surrounding hinterland. The area plays a strategic role in the development of its regional tourism economy. As a unique form and organization of regional spatial structure, tourist flows in metropolitan areas present different spatial structures and characteristics. However, the existing research on tourist flow is rarely explained from the perspective of metropolitan areas,. Therefore, this study examined the trip strategy data published by tourists, analyzed the spatial correlation characteristics of the tourist flow network in the Shanghai Metropolitan Area (SMA) from the node and network levels by employingsocial network theory and GIS spatial analysis methods and technologies, The results revealed the following: (1) the tourism nodes in SMAdisplayed obvious hierarchical structural characteristics, and the elite scenic spots occupied an important core position in the tourist flow network. Simultaneously, according to the comparative analysis of degree centrality and intermediary centrality, Shanghai tourism nodes, t could be categorized into four types: agglomeration, diffusion, equilibrium, and intermediary. (2) SMA presented a "single-core radial" spatial distribution pattern with Huangpu District at its center, and Huangpu District as the spatial diffusion source, with the mainstream diffusion directions of southwest (central Huangpu District-Songjiang District), southeast (central Huangpu District-Pudong New Area), northwest (central Huangpu District-Jiading District), and a few tourist flows spreading from central Huangpu District to Baoshan District and Chongming County in the north. (3) The topological structure of the Shanghai urban tourist flow network was characterized by low connection, strong diffusion, and balanced distribution. There were five condensed subgroups with high internal and low external correlations. The core-edge division of the network was significant, and the driving effect of the core area on the edge was limited. (4) There was an exponential relationship between tourist flow and the distance between scenic spots inSMA, which had an evident distance attenuation law. However, the attenuation degree in each distance segment was different, and the distance attenuation law of tourist flow was the most significant at 0–20km. Bycomparing the conclusions of this study with other types of domestic tourist flow networks, it was found that various types of domestic tourist flow networks had the same core-edge structure, but the difference lied in that other types of tourism flow networks showed the characteristics of a multi-center balanced structure, and the core nodes were usually high-grade scenic spots, while the tourist flow network in Shanghai was a "single-core radial" spatial structure with the central city as its core, and the core nodes were high-profile scenic spots without grades in the central city. This study provides an example for exploring analyzing the spatial characteristics of tourist flow networks by using the data of network travel notes and optimizing the spatial structure of tourism destinations in metropolitan areas.

With the rapid increase of Electric vehicle (EV) penetration, EV charging network become an important infrastructure. This paper studies the configuration of EV charging network from a safety perspective. It is found that cascading failures may happen if one charging station is out of work and its load has to be redistributed to its neighboring stations. The cascading failure model borrowed from network science area is used in the study, and the results show the effects of system parameters on the network robustness. The results indicate that in order to achieve a good performance, it is better to assign extra load based on neighbors’ degree when a node is overloaded. On the other hand, the extra load redistribution should not consider the distance between the overloaded node and its neighbors.

The spatial difference in agricultural product prices is a crucial factor affecting the benefits of related stakeholders. This study aims to analyze the mechanisms of spatial price transmission. In this paper, taking garlic as an example, we present a vector autoregression model analyzing relations of the price transmission between producing and selling cities. The partial Granger causality test is used to determine the direction and path of price transmission between the main producing areas and the main consuming areas. We find that the prices in different areas have a complex transmission network and fluctuate in correlation with each other. The results reveal the characteristics of agricultural product price transmission in China and provide reasons and evidence for market regulation.

The naïve algorithm for generating nearest-neighbour models determines the distance between every pair of nodes, resulting in quadratic running time. Such time complexity is common among spatial problems and impedes the generation of larger spatial models. In this article, an improved algorithm for the Mocnik model, an example of nearest-neighbour models, is introduced. Instead of solving k nearest-neighbour problems for each node ( k dynamic in the sense that it varies among the nodes), the improved algorithm presented exploits the notion of locality through introducing a corresponding spatial index, resulting in a linear average-case time complexity. This makes possible to generate very large prototypical spatial networks, which can serve as testbeds to evaluate and improve spatial algorithms, in particular, with respect to the optimization of algorithms towards big geospatial data. [ABSTRACT FROM AUTHOR]

The space layout problem encompasses challenges that rely on a diverse range of contexts regarding urban planning and architectural design, during the traditional design phases which require immense effort and time for the evaluation of the spatial elements' characteristic needs. In order to eliminate the burden of considering all multidimensional design aspects at the same time, this research presents a three-bodied computational method for locating the spaces of the given architectural design program in a project site, according to the defined list of design objectives and criteria. Besides the determination of the layout according to the requirements of the spatial elements, this research proposes an integration of the space syntax theory's analytical compounds in terms of Justified Graph Analysis and Integration Values as the fitness criteria for the multi-objective evolutionary optimization in the computational model. To satisfy the integrity levels of each various characterized element within site organization, that are implied inherently by the architectural design program and generate a sustainable space network layout for the project site. [ABSTRACT FROM AUTHOR]

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City systems are characterized by the functional organization of cities on a regional or country scale. While there is a relatively good empirical and theoretical understanding of city size distributions, insights about their spatial organization remain on a conceptual level. Here, we analyze empirically the correlations between the sizes of cities (in terms of area) across long distances. Therefore, we (i) define city clusters, (ii) obtain the neighborhood network from Voronoi cells, and (iii) apply a fluctuation analysis along all shortest paths. We find that most European countries exhibit long-range correlations but in several cases these are anti-correlations. In an analogous way, we study a model inspired by Central Places Theory and find that it leads to positive long-range correlations, unless there is strong additional spatial disorder-contrary to intuition. We conclude that the interactions between cities extend over large distances reaching the country scale. Our findings have policy relevance as urban development or decline can affect cities at a considerable distance., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

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.

We demonstrate how sophisticated graph properties, such as small distances and scale-free degree distributions, arise naturally from a reinforcement mechanism on layered graphs. Every node is assigned an a-priori i.i.d. fitness with max-stable distribution. The fitness determines the node attractiveness w.r.t. incoming edges as well as the spatial range for outgoing edges. For max-stable fitness distributions, we thus obtain a complex spatial network, which we coin extremal linkage network. [ABSTRACT FROM AUTHOR]

The interurban network of criminal collaboration in Canada is described, and possible explanations for its structure are explored. The data include all police-reported co-offences in the 32 major cities of Canada during 2006–09. Component analysis and graph drawings in network space and in geospace elucidate the structure of the network. Quadratic assignment procedure multiple regressions, repeated separately on the networks of instrumental and noninstrumental co-offences, test hypotheses about possible determinants of the network structure. The cities form one connected component, containing two clusters connected by a link between Toronto and Vancouver. One cluster, centred on the triad of Toronto, Montreal, and Ottawa, comprises the cities in Ontario and Quebec, with weak links to cities in the Atlantic provinces. The other cluster, centred on Vancouver, comprises the cities in the four western provinces. The structure is strongly correlated with the residential mobility of the general population, which in turn is strongly correlated with intercity distances. The correlation with mobility is less strong for instrumental than for noninstrumental crimes. The structure of this co-offending network can be explained by criminals' routine activities, namely ordinary residential mobility, but the alternative explanation of purposive interurban criminal collaboration is more plausible for instrumental crime. [ABSTRACT FROM AUTHOR]