Identification of key recovering node for spatial networks

Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.

Structural characteristics and influencing factors of spatial correlation network for regional high-quality development in China

On the basis of measuring the regional high-quality development in China from 2011 to 2020,this study uses gravity model to build spatial correlation network,and uses social network analysis method to analyze the structural characteristics and influencing factors of correlation network.The results are shown as follows.First,from 2011 to 2020,the level of regional high-quality development in China is rising gradually,and the discrete characteristics between regions are gradually obvious,showing a step-like distribution structure decreasing from east to west.Second,the network density of regional high-quality development is generally low and tends to decline,but it has strong stability and correlation strength.Third,the spatial correlation network has an obvious core-edge structure.Shanghai is always at the center of the network and plays a significant intermediary role,while Qinghai and Xinjiang are always at the edge of the network.Fourth,the regional high-quality development association network can be divided into four major sectors:main benefit,net benefit,net spillover,and broker,showing the spatial correlation characteristics of inter-plate contact and intra-plate agglomeration.Fifth,the level of economic development,the level of urbanization and geographical proximity have a significant impact on the formation of regional high-quality development correlation network.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

A Spatial-Temporal Network Perspective for the Propagation Dynamics of Air Traffic Delays

Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity.As air traffic is associated with complex air transport systems,delays can be magnified and propagated throughout these systems,resulting in the emergent behavior known as delay propagation.An understanding of delay propagation dynamics is pertinent to modern air traffic management.In this work,we present a complex network perspective of delay propagation dynamics.Specifically,we model air traffic scenarios using spatial–temporal networks with airports as the nodes.To establish the dynamic edges between the nodes,we develop a delay propagation method and apply it to a given set of air traffic schedules.Based on the constructed spatial-temporal networks,we suggest three metrics-magnitude,severity,and speed-to gauge delay propagation dynamics.To validate the effectiveness of the proposed method,we carry out case studies on domestic flights in the Southeastern Asia region(SAR)and the United States.Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR.Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR.The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays,while the situation in the United States is considerably worse,with a corresponding number of around 16.This work provides a potent tool for tracing the evolution of air traffic delays.

Analysis of overload-based cascading failure in multilayer spatial networks

Many complex networks in real life are embedded in space and most infrastructure networks are interdependent,such as the power system and the transport network.In this paper,we construct two cascading failure models on the multilayer spatial network.In our research,the distance l between nodes within the layer obeys the exponential distribution P(l)~exp(-l/ζ),and the length r of dependency link between layers is defined according to node position.An entropy approach is applied to analyze the spatial network structure and reflect the difference degree between nodes.Two metrics,namely dynamic network size and dynamic network entropy,are proposed to evaluate the spatial network robustness and stability.During the cascading failure process,the spatial network evolution is analyzed,and the numbers of failure nodes caused by different reasons are also counted,respectively.Besides,we discuss the factors affecting network robustness.Simulations demonstrate that the larger the values of average degree<k>,the stronger the network robustness.As the length r decreases,the network performs better.When the probability p is small,asζdecreases,the network robustness becomes more reliable.When p is large,the network robustness manifests better performance asζincreases.These results provide insight into enhancing the robustness,maintaining the stability,and adjusting the difference degree between nodes of the embedded spatiality systems.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications

Humanities and Social Sciences(HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics(including RS: Remote Sensing;GIS: Geographical Information System;GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences(SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover(LUCC), Specially Protected Natural Areas(SPNA) analysis, editing behavior analysis of Volunteered Geographic Information(VGI), electricity consumption anomaly detection, First and Last Mile Problem(FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

Spatial network structure of transportation carbon emission efficiency in China and its influencing factors

Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.

Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks

Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Analysis of Reflection Characteristics for Foam Filled Grid Structure

The reflection characteristics of gird structures are calculated by the spatial network method in the case of normal incidence plane electromagnetic wave. The numerical result shows that the grid panels without electromagnetic wave absorbing foams are not ideal. However, the absorbing ability can be achieved as low as -25 dBsm from 8 GHz to 12 GHz when the grid cells are filled with foam absorbers. Also it is noted from computation that the foam filled grid structures with larger cell size, higher and thinner ribs will improve the absorbing abilities, which illustrates that they can be used as the effective light-weight stealth structures for aeronautical application.

Enhancement of scale-free network attack tolerance

Despite the large size of most communication and transportation systems, there are short paths between nodes in these networks which guarantee the efficient information, data and passenger delivery; furthermore these networks have a surprising tolerance under random errors thanks to their inherent scale-free topology. However, their scale-free topology also makes them fragile under intentional attacks, leaving us a challenge on how to improve the network robustness against intentional attacks without losing their strong tolerance under random errors and high message and passenger delivering capacity. Here We propose two methods （SL method and SH method） to enhance scale-free network＇s tolerance under attack in different conditions.

Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling(PLS-SEM)

The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM（PLS-SEM） and geographically-weighted regression（GWR） modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass（AGB）. The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.

Spatio-temporal Evolution of the Agricultural Eco-efficiency Network and Its Multidimensional Proximity Analysis in China

As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.

Optimization of a Network Topology Generation Algorithm Based on Spatial Information Network

Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system.This requires launching more low-Earth orbit satellites.In order to adapt to the increase in the number of near-Earth satellites,the dynamic optimization of space informa-tion network topology between satellites will have research significance.Consid-ering the visibility of satellite networking,the connectivity of satellite nodes,and the number of links connected to the whole network,with the goal of minimizing the end-to-end delay between satellite nodes in the network as the optimization goal,a network topology optimization model that meets multiple constraints is constructed,and the model is solved using greedy algorithm and simulated anneal-ing algorithm.In the process of simulated annealing,the networkflow algorithm is innovatively proposed for neighborhood solution.Experiments show that the simulated annealing hybrid neighborhood algorithm is significantly better than the simulated annealing random neighborhood algorithm.

Optimization of multi-state generation problem based on spatial information network topology

Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology change.With the increasing number of low-orbit satellites,the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information network.In this paper,two common objective functions in inter-satellite link assignment,network observation position and network communication factor are studied,and a multi-objective optimization model is constructed.Depth first search,simulated annealing,NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the model.By comparing the solving efficiency of the model through simulation experiments,the difference of the results caused by the four algorithms is verified.

Dynamic Shortest Path Monitoring in Spatial Networks

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

Multiplex network reconstruction for the coupled spatial diffusion of infodemic and pandemic of COVID-19

The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information.The spreading of neutral and highly accurate reports can guide the public to self-protect and reduce the pandemic.Mis-and dis-information would intrigue panic and high exposure risk to epidemic.Although the infodemic has attracted attentions from the academia,it is still not known to what degree and in which direction the information flows contribute to the COVID-19 pandemic.To fill the gap,we apply network reconstruction techniques to rebuild the hidden multiplex network of information and COVID-19 spreading by which we aim at quantifying the interaction between the propagation of information and the spatial outbreak of COVID-19,and delineate between the positive and negative impact of information on the pandemic.By differentiating the types of media that participated in the information process,we find that in the early stage of COVID-19 pandemic,infodemic does play a critical role to amplify the risk of virus outbreak in China and the risk is even larger for those highly developed regions.Compared to the old-fashion media,the new mobile platforms impose a greater risk to reinforce the positive feedback between infodemic and COVID-19 pandemic.