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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]

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]

The problem of network disintegration, such as suppression of an epidemic spread and destabilization of terrorist networks, possesses extensive applications and has lately been the focus of growing interest. Many real-world complex systems are represented by spatial networks in which nodes and edges are spatially embedded. However, existing disintegration approaches for spatial network disintegration focus on singular aspects such as geospatial information or network topography, with insufficient modeling granularity. In this paper, we propose an effective and computationally efficient virtual node model that essentially integrates the geospatial information and topology of the network by modeling edges as virtual nodes with weights. Moreover, we employ Kernel Density Estimation, a well-known non-parametric technique for estimating the underlying probability density function of samples, to fit all nodes, comprising both network and virtual nodes, to identify the critical region of the spatial network, which is also the circular geographic region where disintegration occurs. Extensive numerical experiments on synthetic and real-world networks demonstrate that our method outperforms existing methods in terms of both effectiveness and efficiency, which provides a fresh perspective for modeling spatial networks. • Introduces a virtual node model integrating geospatial data and topology in spatial network disintegration. • Employs Kernel Density Estimation for identifying critical regions in spatial networks. • Demonstrates superior effectiveness and efficiency over existing methods in real-world applications. • Proposes adaptive method linking disintegration circle radius with method parameters. • Provides new insights for spatial network modeling. [ABSTRACT FROM AUTHOR]

Protein structures are complex spatial systems, formed by the three-dimensional arrangement of amino acids, that interact through atomic contacts. It is fundamental to understand the perturbation mechanisms associated with the amino acid mutations that lead to changes in a protein dynamics, as such mutations can lead to diseases. We present a methodology based on Amino Acid Networks and the use of Induced Perturbation Networks to infer the impact of amino acid mutations on a protein's dynamics and the use of a tiling formalism to model protein aggregation. We apply this methodology to the case study of the L55P Transthyretin (TTR) variant, one of the most pathogenic mutations of TTR, due to its increased tendency to aggregate into amyloid fibers. We show that another pathogenic variant, V30M TTR, produces different results, reflecting a different pathogenic mechanism compared to L55P TTR and that the results differ for pathogenic and non-pathogenic variants (L55P and V30M TTR pathogenic variants versus Tl 19Y and Tl 19M non-pathogenic variants). [ABSTRACT FROM AUTHOR]

Human-induced land modification is a significant contributor to carbon emissions. To facilitate the collective reduction of emissions within city clusters and promote eco-friendly urban development, this paper developed a research framework for analyzing carbon emissions. An empirical study was conducted within the Beijing-Tianjin-Hebei (BTH) region of China, focusing on the spatio-temporal patterns of carbon emissions and network features. The results showed that: (1) Carbon emissions among cities in the BTH region displayed significant variation from 2005 to 2020, with an increasing gap in carbon deficit. (2) Inter-city carbon emission correlation showed a declining trend, accompanied by pronounced spatial spillover effects in the region. (3) The network exhibited a bicentric pattern, centered around BJ and TJ, and the network correlations were more likely to occur between nearby developed cities, with poor regional synergistic emission reduction capacity. The implications of this study are valuable for promoting the establishment of an economic system that fosters green, low-carbon, and circular development. Furthermore, the findings contribute to the effective pursuit of carbon reduction and mitigation of climate change in China. • Distinction in carbon deficit between cities was gradually growing. • Spatial correlation network of carbon emissions had serious spillover effects. • Regional carbon emission correlations broke through geographical proximity. • The network presented a dual-center distribution pattern. • Carbon emission linkages were more likely to occur between nearby developed regions. [ABSTRACT FROM AUTHOR]

Based on network analysis and exponential random graph analysis methods (ERGM), the spatial network pattern of gas consumption and its evolution mechanism are investigated using Chinese inter-provincial panel data from 2008 to 2021. This paper shows that the spatial association strength level of natural gas consumption shows an enhanced spatiotemporal evolution. Network density is enhanced. Hierarchy decreases in a stepped way, the network efficiency increases in a fluctuating way, the redundant relation decreases, and the number of its bidirectional spillover relation increases year by year. The eastern seaboard is an economically developed region which is in the "net benefit" position. The provinces of the Tianjin, Hebei, and Shandong are the "two-way spillover" segments. Giving full play to the advantage of the balance between local markets and foreign markets gas consumption. Fujian, Chongqing, Inner Mongolia, Shanxi and Hubei are in the "broker" segment, which tends to concentrate on natural gas consumption outside the segment by their resource endowments. The block of "net spillover" involves the risk of carbon transfer and it should be more responsible for energy saving and carbon reduction; The convergence of per capita economic development, the size of population, industrial structure and urbanization rate leads to selective preferences for inter-provincial natural gas consumption. The large population sizes of provinces have "economies of scale" and "local market effects", which prefer to deploy natural gas. Carbon emission linkage network is the most important for the formation of spatial network patterns of natural gas consumption, followed by the influence of economic linkage networks and land proximity. Less stringent environmental regulations and an open regional environment are conducive to the formation of a network of spatial correlations in natural gas consumption. Accordingly, new ideas and policy recommendations for inter-provincial emission reduction are finally proposed. • Natural gas consumption spatial correlation network correlation has increased and has a star-shaped structure. • Structural barriers in the spatially linked network of natural gas consumption are gradually being broken down. • The eastern region is at risk of carbon transfer and should take on more energy-saving and emission reduction tasks. • Economic, demographic and industrial convergence leads to selective preferences in natural gas consumption. [ABSTRACT FROM AUTHOR]

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities. • A modelling framework is developed for predicting informal settlements. • The model only requires street intersection data as input, making it broadly applicable. • The model is applied to five cities; Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. • By training the model using data from one city, the model can predict informal settlements in another city. • Using ground truth data of planning status for validation, the model achieves accuracy of between 82.1 and 97.6%. [ABSTRACT FROM AUTHOR]

Cities' environmental efficiency (measured in terms of green total factor productivity, GTFP) have been found in the existing literature to correlate spatially. However, the spatial structure of the interrelations between cities' GTFP have not yet been determined. In this study, we attempted to characterize city-level spatial networks of GTFP by investigating China's major three coastal city clusters: Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD), and Pearl River Delta (PRD). We estimated the GTFP of 38 cities within these three coastal city clusters by applying a super-efficiency slack-based measure (SBM) within a data envelopment analysis (DEA) approach, constructed a spatial network of GTFP for each city cluster based on gravity model, traced out the centralities and subgroups of each GTFP network by conducting social network analysis, and then investigated the socioeconomic factors correlating to GTFP interrelation between cities. The results are as follows: First, the average GTFP in the three clusters increased from 2010 to 2021, as did the industrial value added in these regions. In addition, average GTFP increased the most in PRD region relative to the other two clusters. Second, centers of the spatial network of GTFP exist in these three regions. These are as follows: Beijing, Tianjin, and Langfang in JJJ region; Wuxi, Suzhou, and Yangzhou in YRD region; and Guangzhou and Foshan in PRD region. In the JJJ and YRD regions, the composition of the subgroups containing cities with greater closeness of GTFP underwent significant changes between 2010 and 2021, whereas the composition of the subgroup in the PRD region did not change as much. Third, differences in terms of industrial structure and distance between two cities are key underlying factors that influence the interrelation of GTFP between these cities. The characteristics of the spatial networks of GTFP in the three city clusters imply that improving the balance between the central cities and other noncentral cities to alleviate the misallocations between cities and economic resources could result in an increase in the overall efficiency of economic growth and environmental improvement. [ABSTRACT FROM AUTHOR]

In certain developing countries, the e-commerce industries of rural areas are subjected to clustering, which provides an entry point through which the following can be observed: the influence of information and communications technology and the digital economy on spatial connections in rural settlements. Hence, Zhejiang Province, China's leading rural e-commerce province, is selected as the study site. The spatial correlation between e-commerce and the spatial landscape pattern of rural settlements and their network connections is analysed. Moreover, with regard to the spatial network of rural settlements, the driving factors of e-commerce are explored. The research findings demonstrate that: (1) e-commerce cluster areas are primarily distributed where the in-degree is higher than the out-degree, and the betweenness centrality is also significant. (2) E-commerce fosters the expansion and integration of land use in rural settlements and enhances the spatial network connections between rural settlements. (3) In regional economies, e-commerce enhances the connectivity and resource circulation efficiency of spatial networks in rural settlements through scale effects, traffic and informatisation levels, and the logistics development environment. The aforementioned findings contribute to the enrichment of the policy toolbox for rural planning and spatial governance, and the exploration of the market potential of e-commerce development in rural areas. • A spatial network model of rural settlements relies on a modified gravity model is proposed. • The spatial correlation between e-commerce and rural settlements is evaluated. • The driving factors of e-commerce on the spatial network of rural settlements are examined. [ABSTRACT FROM AUTHOR]

Coastal ports play a critical role as vital intersections connecting land and sea, and their level of spatial connectivity and integration serves as a crucial indicator of competitiveness. This study employs a gravity model to analyse the spatial network of port performance among 21 major coastal ports in China, investigating the overall characteristics of the network, centrality measures, network vulnerability, and spatial clustering features during the period from 2012 to 2021. The findings reveal a relatively stable spatial network structure of the coastal port performance, with closely intertwined connections between ports. Key ports such as Shanghai Port, Qingdao Port, Xiamen Port, and Guangzhou Port occupy central positions within the network, exerting a dominant influence. Port performance shows more obvious clustering characteristics in the correlation network, with strong intra-plate correlations and significant spatial spillover from the Southeast Coast, Yangtze River Delta, Pearl River Delta, and Southwest Coast. Furthermore, spatial distance, urbanization level, per capita GDP, and marketization level significantly impact the spatial correlation of port performance. The higher the similarity of economic development and marketization levels between ports, the stronger the spatial correlation and spillover of port performance. These research findings contribute to the establishment of collaborative mechanisms for the coordinated development of coastal ports and provide valuable insights for port management decision-making. [ABSTRACT FROM AUTHOR]

Spatial networks are ubiquitous in the real world, with examples including Internet, power grids, neural networks, and so on. In this paper, based on a proposed disk packing, we present a contact graph as an exactly solvable model for spatial networks. We then derive analytically some critical structural properties of the model, including degree distribution, clustering coefficient, and diameter, and show that it displays simultaneously the striking scale-free small-world phenomena observed in most real networks. We also determine all the eigenvalues and their multiplicities of the transition probability matrix for the contact graph, and exploit the eigenvalues to derive closed-form expressions for Kemeny constant of random walks and the number of spanning trees on the contact graph. Finally, we derive closed-form expressions for some interesting quantities about resistance distances, including Kirchhoff index, additive degree-Kirchhoff index, multiplicative degree-Kirchhoff index, as well as average resistance distance. As an application of resistance distances, we study the coherence of the first-order consensus dynamics, which tends to a small constant as the graph grows, implying that the graph is resistant to noise. [ABSTRACT FROM AUTHOR]

This article is the first-ever analysis of cities in relation to maritime transport flows from a relational, or network, perspective. Based on untapped vessel movement data covering the last 120 years, this articles sheds new light about the interdependencies at stake between urban hierarchies and port hierarchies overtime. Main results point to the fading correlation between traffic volume, port centrality, and city size, while the largest cities have maintained their prominence in the global maritime network despite contemporary spatial and functional changes affecting the distribution of transport systems and commodity chains. Such findings help better understanding not only the spatial and functional evolution of port-city relationships and maritime transport; they also confirm the importance of taking into account the long-term dynamics and socio-economic embedding of spatial networks in geography and other disciplines. [ABSTRACT FROM AUTHOR]

This article investigates the degree of overlap among the different layers of circulation composing global maritime flows in recent decades. Mobilizing several methods originating from complex networks allows us to shed new light on specialization and diversification dynamics affecting the evolution of ports and shipping. The principal confirm the strong and path-dependent influence of multiplexity on traffic volume, range of interaction and centrality from various perspectives, such as matrices correlations, homophily, assortativity, and single linkage analysis. While the network grows and concentrates around large hubs over time, traffic distribution is also place-dependent due to the reinforced position of already established nodes. [ABSTRACT FROM AUTHOR]

Reconstructing regional to hemispheric-scale climate variability requires the application of spatially representative and climatically sensitive proxy archives. Large spatial networks of dendrochronologies have facilitated the reconstruction of atmospheric variability and inferred variability in the Atlantic Ocean system. However, the marine environment has hitherto lacked the direct application of the spatial network approach because of the small number of individual absolutely-dated marine archives. In this study we present the first analyses of a network of absolutely-dated annually-resolved growth increment width chronologies from the marine bivalves Glycymeris glycymeris and Arctica islandica . The network contains eight chronologies spanning > 500 km along the western British continental shelf from the southern Irish Sea to North West Scotland. Correlation analysis of the individual chronologies and a suite of climate indices, including the Atlantic Multidecadal Oscillation (AMO), Central England surface air temperature (CET), northeast Atlantic sea surface temperatures (SST's) and the winter North Atlantic Oscillation (wNAO), demonstrates that, despite the large geographical distances been sites and the heterogeneous nature of the marine environment, the increment width variability in these series contains an element of coherence likely driven by a common response to changing environmental forcing. A nested Principal component analysis (PCA) was used to construct five composite series which explain between 31% and 74% of the variance across the individual chronologies. Linear regression analyses indicate that the composite series explain up to 41% of the variance in Northeast Atlantic SSTs over the calibration period (1975–2000). Calibration verification (reduction of error [RE] and coefficient of efficiency [CE]) statistics indicate that the composite series contains significant skill at reconstructing multi-decadal northeast Atlantic SST variability over the past two centuries (1805–2010). These data suggest that composite series derived from sclerochronology networks can facilitate the robust reconstruction of marine climate over past centuries to millennia providing invaluable baseline records of natural oceanographic variability. [ABSTRACT FROM AUTHOR]

We investigate random walk with a bias toward a target node in spatially embedded networks with total cost restriction introduced by Li et al. (2010). Precisely, The network is built from a two-dimension regular lattice to be improved by adding long-range shortcuts with probability P ( r i j ) ∼ r i j − α , where r i j is the Manhattan distance between sites i and j , and α is a variable exponent, the total length of the long-range connections is restricted. Bias is represented as a probability p of the packet or particle to travel at every hop toward the node which has the smallest Manhattan distance to the target node. By studying the mean first passage time (MFPT) for different exponent log 〈 l 〉 , we find that the best transportation condition is obtained with an exponent α = d + 1 ( d = 2 ) for all p . The special phenomena can be possibly explained by the theory of information entropy, we find that when α = d + 1 ( d = 2 ) , the spatial network with total cost restriction becomes an optimal network which has a maximum information entropy. In addition, the scaling of the MFPT with the size of the network is also investigated, and finds that the scaling of the MFPT with L follows a linear distribution for all p > 0 . [ABSTRACT FROM AUTHOR]

The dynamics of transportation in networks is a universal phenomenon in many real complex systems. Previous studies on transportation dynamics in spatially embedded networks have mainly focused on the average transport time from source to target. However, previous studies have rarely considered the transport cost from source to target in spatially embedded networks, a factor that is essential if geographical distance is to be accounted for. In this paper, we aim to minimize transport cost, a problem essentially different from that of minimizing transport time. We propose a novel hybrid algorithm that integrates information of both geographical distance and the direction of movement. The proposed hybrid algorithm can reduce the chance of zigzag motion in the network, and has the potential to yield lower transport cost than Kleinberg's algorithm. Ultimately, we find that average transport cost scales linearly with system size for both Kleinberg's algorithm and our hybrid algorithm, but grows more slowly for our algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The network topology has lately proved to be critical to the appearance of traffic congestion, with scale-free networks being the less affected at high volumes of traffic. Here, the congestion dynamics are investigated for a class of networks that has experienced a resurgence of interest, the networks based on the gravity model. In addition, supplementary to the standard paradigm of uniform traffic volumes between randomly interacting node pairs, more realistic gravity traffic patterns are used to simulate the flows in the network. Results indicate that depending on the traffic pattern, the networks have different tolerance to congestion. Experiment simulation shows that the topologies created on the basis of the gravity model suffer less from congestion than the random, the scale-free or the Jackson–Rogers ones under both random and gravity traffic patterns. The congestion level is found to be approximately correlated with the network clustering coefficient in the case of random traffic, whereas in the case of gravity traffic such a correlation is not a trivial one. Other basic network properties such as the average shortest path and the diameter are seen to correlate fairly well with the congestion level. Further investigation on the adjustment of the gravity model parameters indicates particular sensitivity to network congestion. This work may have practical implications for designing traffic networks with both reasonable budget and good performance. [Copyright &y& Elsevier]

Highlights: [•] A new approach to smoothing location measures in spatially embedded graphs. [•] It smoothes the graph around a location and then calculates its graph measure. [•] It helps discover true patterns that conventional smoothing approaches often miss. [•] It helps detect patterns in sparse graphs such as migration of a specific age group. [•] It is a generic framework that can be used to smooth various graph measures. [ABSTRACT FROM AUTHOR]

Abstract: This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities. [Copyright &y& Elsevier]

Abstract: The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensors have imaged emitted light from Earth''s surface since the 1970''s. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years . The resulting image time series captures a spatiotemporal signature of human settlement growth and evolution. We use temporal Empirical Orthogonal Function (EOF) analysis to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOFs provides a basis for distinguishing between deterministic temporal trends and stochastic year to year variance. The low order EOFs and Principal Components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict deterministic decadal trends. The most pronounced changes occur in Asia. Throughout Asia a variety of different patterns of brightness increase are visible in tri-temporal brightness composites – as well as some conspicuous areas of apparently decreasing background luminance and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution imagery reveals multiple phases of urban growth in several cities, numerous instances of highway construction, extensive terracing networks and hydroelectric dam construction . Lights also allow us to quantify the size distribution and connectedness of different intensities of development. Over a wide range of brightnesses, size distributions of spatially contiguous lighted area are well-fit by power laws with exponents near -1 as predicted by Zipf''s Law. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development. [Copyright &y& Elsevier]

Abstract: Recently, spatial networks have attracted much attention. The spatial network is constructed from a regular lattice by adding long-range edges (shortcuts) with probability , where is the geographical distance between the two ends of the edge. Also, a cost constraint on the total length of the additional edges is introduced (). It has been pointed out that such networks have optimal exponents for the average shortest path, traffic dynamics and navigation. However, when is large, too many generated long-range edges will be added to the network. In this scenario, the total cost constraint cannot be satisfied. In this paper, we propose a distance coarse graining procedure to solve this problem. We find that the optimal exponents for the traffic process, navigation and synchronization indeed result from the trade-off between the probability density function of long-range edges and the total cost constraint, but the optimal exponent for percolation is actually due to dissatisfying the total cost constraint. On the other hand, because the distance coarse graining procedure widely exists in the real world, our work is also meaningful in this aspect. [Copyright &y& Elsevier]

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed. [ABSTRACT FROM AUTHOR]

Dockless bike sharing (DBS) provides a sustainable and green travel mode, which also enhances the connections with other travel modes. Understanding the travel mobility and demand of DBS become an urgent task for government and operators to provide better service. In this paper, we propose a network-based method to detect the travel mobility of DBS users based on the actual trip data. The studied area is divided by square grid with same size. The grids with trips are considered as nodes and the connections between nodes are considered as edges. To gain the dynamic characteristics of DBS travel mobility, we construct several networks according to different time periods in a weekday. We build a data-driven framework to analyze DBS network including accessibility, spatial inequality, spatial autocorrelation and network-based indicators. The relationship between flow strength and point-of-interest (POI) is discussed. The results show that travel demands of DBS are higher in morning peak and evening peak on weekdays. The DBS networks are inequality, connections are concentrated on center area. From the network view, the DBS network are assortative and positive autocorrelated with evident communities. The results imply that the number of residence and transport facility have strong correlations with flow strength. • The spatial-temporal characteristics of bike-sharing was explored. • Complex network of bike-sharing was constructed based on grid divisions. • The relationship between travel demand and POI was tested. • DBS networks are assortative and positive autocorrelated with evident communities. • The number of residence and transport facility have strong correlations with flow strength. [ABSTRACT FROM AUTHOR]

Human movements and interactions with cities are characterized by urban mobility networks. Many studies that address urban mobility are inspired by complex networks. The models of complex networks require a large amount of empirical data. However, current works relied on traditional survey data and were unable to take full advantage of the capabilities offered by complex networks; thus, the possibility of quantifying urban mobility networks by considering individual travel patterns has not yet been addressed. This study presents a data-driven approach for characterizing urban mobility networks based on massive-scale mobile phone tracking data. Individual travel motifs are first extracted using a graph-based approach. The global urban mobility network (G-UMN) and the motif-dependent urban mobility subnetworks (MD-UMN s) are then constructed. Next, network properties, including statistical measures and scaling relations between the basic measures, are proposed for characterizing mobility networks. We have conducted experiments focusing on Shenzhen, China. The results demonstrated that (1) the individual travel motifs are structurally and spatially heterogeneous, (2) the G-UMN exhibits a evolutionary hierarchical structure, and (3) the MD-UMN s show many behavioral differences in their spatial and topological properties, reflecting the impacts of the heterogeneity of the individual travel motifs. These results bridge the gap between complex network properties and urban mobility patterns and provide crucial implications and policies for data-informed urban planning. • Urban mobility networks are resolved using mobile phone tracking data • Nine individual travel motifs are extracted, presenting structurally and spatially heterogeneous • The global and the motif-dependent mobility networks are characterized by complex-network measures and scaling relations • The global urban mobility network is neither purely scale-free nor completely random, exhibits a hierarchical structure • The motif-dependent urban mobility networks show differences in their spatial and topological properties [ABSTRACT FROM AUTHOR]

This research aims to review the total collection of literature on the 'residential satisfaction' concept used in social research from 1961 to 2020, and provides a complete overview on how social research concerning 'residential satisfaction' has emerged and developed in the last 60 years. We follow the theoretical framework of bibliometric analysis and use the bibliometric datasets retrieved from Web of Science and Scopus's online resources. After compiling and processing the datasets, we used a final dataset containing 877 documents on residential satisfaction. The open-source statistical and visualisation software packages R and VOSviewer were used mainly for processing and analysing datasets. The results visualise information on the top authors, the most influential documents by citation, the most productive countries, and other criteria. The lexical network analysis shows that residential satisfaction has a strong co-occurrence association with the two frequently occurring terms 'neighbourhood' and 'satisfaction'. The majority of research on residential satisfaction has been conducted in the United States, China, and the United Kingdom. The top three most influential journals for residential satisfaction research are 'Social Indicators Research', 'Environment and Development', and 'Habitat International'. This bibliometric analysis frames the future research demand for residential satisfaction in social research. • This paper presents how the 'residential satisfaction (RS)' concept used in social science since its inception in 1961. • Bibliometric issues, e.g. lexical, temporal and spatial network of residential satisfaction concepts are discussed. • Bibliometric methodology is employed in open-source statistical and visualisation software packages R and VOSviewer. • 'Neighbourhood' and 'satisfaction' are the two frequently occurring terms in co-occurring association. • Majority of research on RS has been conducted in the United States, China, and the United Kingdom. • Top journals for RS research are 'Social Indicators Research', 'Environment and Development', and 'Habitat International'. [ABSTRACT FROM AUTHOR]

• Random Forest model is used to predict annual commodity and virtual water flows. • Random Forest predictions consistently outperform those from a gravity model. • Model performance improves as commodities/regions are aggregated into coarser groups. • Model predicts both network connections and flows without comprising performance. • The modeled data could be beneficial for managing local and regional water scarcity. Virtual water flows are used to determine the indirect water requirements of a region or product, making them an indispensable tool for water sustainability analysis and assessment. Commodity flows are a key data needed to compute virtual water but are typically available every 5 years in the United States (US). The lack of continuous, annual commodity flow data severely limits our ability to study and understand the drivers, evolution, and alterations of virtual water in the US. We build and evaluate a machine learning model using Random Forest (RF) to predict annual commodity and virtual water flow networks. The model is used to perform several modeling experiments and illustrate the prediction of annual virtual water flows in the US during 2013–2018. We show that the RF predictions consistently outperform those from a gravity model. The overall performance of the RF algorithm improves as commodities or regions are aggregated into coarser groups. Likewise, the inclusion of past commodity flows as an additional explanatory variable enhances the RF performance. The combination of RF classification and regression allows predicting both network connections and flows without comprising performance. Based on our RF predictions for 2013–2018, we find that temporal variations in virtual water flows can be large for some regions in the US, underscoring the need addressed by this study of reconstructing domestic virtual water changes over time. By capturing inter-regional water consumption interactions in space and time, such reconstructed data could be beneficial in the future for anticipating and managing local and regional water scarcity. [ABSTRACT FROM AUTHOR]

• An individual-based model of amphibian dispersal between wetlands was developed. • The model was calibrated for the endangered tiger salamander in western Canada. • Spatial network analysis showed the importance of ephemeral wetlands for connectivity. Habitat loss and fragmentation continue to drive declines of amphibian populations. Maintaining connectivity between aquatic and terrestrial habitats and across wetlands is critical to amphibian conservation, particularly in regions where climate change may exacerbate habitat loss. Our objective was to study the impact of climate- and human-driven losses in ephemeral wetlands on functional connectivity for amphibians, using the endangered western tiger salamander (Ambystoma mavortium) in British Columbia, Canada as a case study. Land use data and dispersal characteristics for the western tiger salamander were used to develop a spatially-explicit individual-based model of amphibian movement. The model was configured to explore connectivity under three climate scenarios (a wet, average, and dry year), and a land management scenario in which only known breeding sites are conserved. We used a spatial network analysis to identify key ephemeral wetlands that connect otherwise isolated sub-networks across the landscape and probable movement paths between wetlands and the upland terrestrial habitat. Our results illustrate the importance of conserving ephemeral wetlands as stepping stones. We argue that a landscape approach to wetland conservation, rather than just conserving known breeding sites, is essential for retaining viable amphibian populations in a context of human induced habitat loss and climate change. [ABSTRACT FROM AUTHOR]

In urban areas, the spatial configuration of urban elements is vital to the sustainable development of cities. Due to the inherent spatial and networked characteristics of UBNs, research on the spatial configuration of UBNs requires a comprehensive approach involving the integration of spatial and network analysis methods. We propose a methodological framework for geospatial network analysis that combines spatial and network analysis to analyse the spatial configuration of UBNs. This framework is based on the construction of an urban bus spatial network (UBSN) in which the network nodes are spatial division units of urban space with more realistic geographical significance. Then, the framework analyses the spatial configuration of the UBN from the identification of macroscopic statistical characteristics to reveal the explicit roles played by microscopic elements such as individual nodes and to further identify the mesoscopic structural organization. The importance of nodes has spatial heterogeneity and directional imbalance. The community detection results demonstrate the UBSN presents obvious spatial agglomeration features, and the overlapping nodes are mostly distributed in the suburbs. With a novel viewpoint and detailed description of the spatial configuration of UBNs, this study provides meaningful insights for policy makers and planners seeking to optimize traffic infrastructure planning. • Proposing a methodological framework of geospatial network analysis to analyse the spatial configuration of UBNs • The role or importance of nodes has spatial heterogeneity and directional imbalance • Both disjoint and overlapping community structures are detected to reflect the hidden spatial clustering • Presenting a construction method for geospatial networks based on spatial division units [ABSTRACT FROM AUTHOR]