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.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors

Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.

Location of Electric Vehicle Charging Station Based on Spatial Clustering and Multi-hierarchical Fuzzy Evaluation

For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.

Spatial correlation-based characterization of acoustic emission signal-cloud in a granite sample by a cube clustering approach

To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the cube-cluster model.First,the complete connection of the fracture network is regarded as a critical state.Then,according to the Hoshen-Kopelman(HK)algorithm,the real-time estimation of fracture con-nection is effectively made and a dichotomy between cube size and pore fraction is suggested to solve such a challenge of the one-to-one match between complete connection and cluster size.After,the 3D cube clusters are decomposed into orthogonal layer clusters,which are then transformed into the ellip-soid models.Correspondingly,the anisotropy evolution of fracture network could be visualized by three orthogonal ellipsoids and quantitatively described by aspect ratio.Besides,the other three quantities of centroid axis length,porosity,and fracture angle are analyzed to evaluate the evolution of cube cluster.The result shows the sample dilatancy is strongly correlated to four quantities of aspect ratio,centroid axis length,and porosity as well as fracture angle.Besides,the cube cluster model shows a potential pos-sibility to predict the evolution of fracture angle.So,the cube cluster model provides an in-depth view of spatial correlation to describe the AE signal-cloud.

A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid(Dosidicus gigas)Offshore Peru

We examined spatially clustered distribution of jumbo flying squid(Dosidicus gigas) in the offshore waters of Peru bounded by 78?–86?W and 8?–20?S under 0.5?×0.5? fishing grid. The study is based on the catch-per-unit-effort(CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003–2004 and 2006–2013. The data for all years as well as the eight years(excluding El Ni?o events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Ni?o, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Ni?o conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region(cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm

To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.

A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.

The spatial coupling characteristics between the construction of Qingzang National Park Cluster and the sustainable development of local communities

The development of Qingzang national park cluster is part of China’s efforts to establish a major ecological civi-lization project in the Qingzang Plateau,in line with global sustainable development goals.Based on preliminary scientific investigation and research in the Qingzang Plateau,the construction of the park cluster will include 21 national parks.To mitigate the conflict between conservation of the national park cluster and the develop-ment of local communities,this study proposes an analysis framework to identify the spatial coupling features between the national park cluster and sustainable development of communities.Four elements were selected to construct the analysis framework,including natural conditions,geographic location,cultural background,and national policies.This framework was applied to the 457 township communities within the 21 national parks.Results show the weak influence of the construction of the national park cluster for approximately 304 township communities,without significant spatial coupling traits,while the remaining 153 communities demonstrated significant spatial coupling features.These latter townships had developed four types of spatial coupling with na-tional parks,including eco-migrants,transportation hubs,characteristic cultures,and border development,which account for 17.4%,35.3%,19.8%,and 27.5%of the 153 townships respectively.A composite type with more than one spatial coupling feature was also found for 14 communities within the 153 townships.This provides a reference for policy making towards four major types of interactive modes between townships and the national park construction for the sustainable development of Qingzang Plateau.

Mining Knowledge from Result Comparison Between Spatial Clustering Themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.

Compression of LiDAR Data Using Spatial Clustering and Optimal Plane-Fitting

With the advancement in geospatial data acquisition technology, large sizes of digital data are being collected for our world. These include air- and space-borne imagery, LiDAR data, sonar data, terrestrial laser-scanning data, etc. LiDAR sensors generate huge datasets of point of multiple returns. Because of its large size, LiDAR data has costly storage and computational requirements. In this article, a LiDAR compression method based on spatial clustering and optimal filtering is presented. The method consists of classification and spatial clustering of the study area image and creation of the optimal planes in the LiDAR dataset through first-order plane-fitting. First-order plane-fitting is equivalent to the Eigen value problem of the covariance matrix. The Eigen value of the covariance matrix represents the spatial variation along the direction of the corresponding eigenvector. The eigenvector of the minimum Eigen value is the estimated normal vector of the surface formed by the LiDAR point and its neighbors. The ratio of the minimum Eigen value and the sum of the Eigen values approximates the change of local curvature, which determines the deviation of the surface formed by a LiDAR point and its neighbors from the tangential plane formed at that neighborhood. If the minimum Eigen value is close to zero for example, then the surface consisting of the point and its neighbors is a plane. The objective of this ongoing research work is basically to develop a LiDAR compression method that can be used in the future at the data acquisition phase to help remove fake returns and redundant points.

Spatial distribution patterns of anorectal atresia/stenosis in China:Use of two-dimensional graph-theoretical clustering

AIM:To investigate the spatial distribution patterns of anorectal atresia/stenosis in China.METHODS:Data were collected from the Chinese Birth Defects Monitoring Network(CBDMN),a hospital-based congenital malformations registry system.All fetuses more than 28 wk of gestation and neonates up to 7 d of age in hospitals within the monitoring sites of the CBDMN were monitored from 2001 to 2005.Two-dimensional graph-theoretical clustering was used to divide monitoring sites of the CBDMN into different clusters according to the average incidences of anorectal atresia/stenosis in the different monitoring sites.RESULTS:The overall average incidence of anorectal atresia/stenosis in China was 3.17 per 10000 from 2001 to 2005.The areas with the highest average incidences of anorectal atresia/stenosis were almost always focused in Eastern China.The monitoring sites were grouped into 6 clusters of areas.Cluster 1 comprised the monitoring sites in Heilongjiang Province,Jilin Province,and Liaoning Province;Cluster 2 was composed of those in Fujian Province,Guangdong Province,Hainan Province,Guangxi Zhuang Autonomous Region,south Hunan Province,and south Jiangxi Province;Cluster 3 consisted of those in Beijing Municipal City,Tianjin Municipal City,Hebei Province,Shandong Province,north Jiangsu Province,and north Anhui Province;Cluster 4 was made up of those in Zhejiang Province,Shanghai Municipal City,south Anhui Province,south Jiangsu Province,north Hunan Province,north Jiangxi Province,Hubei Province,Henan Province,Shanxi Province and Inner Mongolia Autonomous Region;Cluster 5 consisted of those in Ningxia Hui Autonomous Region,Gansu Province and Qinghai Province;and Cluster 6 included those in Shaanxi Province,Sichuan Province,Chongqing Municipal City,Yunnan Province,Guizhou Province,Xinjiang Uygur Autonomous Province and Tibet Autonomous Region.CONCLUSION:The fi ndings in this research allow the display of the spatial distribution patterns of anorectal atresia/stenosis in China.These will have important guiding significance for further analysis of relevant environmental factors regarding anorectal atresia/ stenosis and for achieving regional monitoring for anorectal atresia/stenosis.

Residential Differentiation Based on Reachability and Spatial Clustering : A Case Study of the Main Urban Area of Wuhan City

The differentiation of urban residential space is a key and hot topic in urban research, which has very important theoretical significance for urban development and residential choice. In this paper, web crawler technology is used to collect urban big data. Using spatial analysis and clustering, the differentiation law of residential space in the main urban area of Wuhan is revealed. The residential differentiation is divided into five types: "Garden" community, "Guozi" community, "Wangjiangshan" community, "Yashe" community, and "Shuxin" community. The "Garden" community is aimed at the elderly, with good medical accessibility and open space around the community. The "Guozi Community" is aimed at young people, and the community has accessibility to good educational and commercial facilities. The "Wangjiangshan" community is oriented towards the social elite group, with beautiful natural living environment, close to the city core, and convenient transportation. The "Yashe" community is aimed at the general income group, and its location is characterized by being adjacent to commercial districts and convenient transportation. The "Shuxin" community is aimed at the middle and lower income groups, far from the city center, and the living environment quality is not high.

A recovery method using recently updated record information in shared-nothing spatial database cluster

A shared nothing spatial database cluster is system that provides continuous service even if some system failure happens in any node. So, an efficient recovery of system failure is very important. Generally, the existing method recovers the failed node by using both cluster log and local log. This method, however, cause several problems that increase communication cost and size of cluster log. This paper proposes novel recovery method using recently updated record information in shared nothing spatial database cluster. The proposed technique utilizes update information of records and pointers of actual data. This makes a reduction of log size and communication cost. Consequently, this reduces recovery time of failed node due to less processing of update operations.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Basic Characteristics,Spatial Disparity and Its Major Influencing Factors of Service Industry in China

Based on the analysis of its basic characteristics, this article investigated the disparities of Chinese service industry among the three regions （the eastern China, the western China and the middle China） and inter-provincial disparities of that in the three regions by Theil coefficient and cluster analysis. Then, major factors influencing its spatial disparity were explored by correlation analysis and regression analysis. The conclusions could be drawn as follows. 1） The development of Chinese service industry experienced three phases since the 1980s： rapid growth period, slow growth period, and recovery period. From the proportion of value-added and employment, its development was obviously on the low level. From the composition of industrial structure, traditional service sectors were dominant, but modem service sectors were lagged. Moreover, its spatial disparity was distinct. 2） The level of Chinese service industry was divided into five basic regional ranks： well-developed, developed, relatively-developed, underdeveloped and undeveloped regions, As a whole, the overall structure of spatial disparity was steady in 1990-2005. But there was notable gradient disparity in the interior structure of service industry among different provinces. Furthermore, the overall disparity expanded rapidly in 1990-2005. The inter-provincial disparity of service industry in the three regions, especially in the eastern China, was bigger than the disparity among the three regions. And 3） the level of economic development, the level of urban development, the scale of market capacity, the level of transportation and telecommunication, and the abundance of human resources were major factors influencing the development of Chinese service industry.

Extracting spatial patterns of ocean fishery using GIS and RS —— Taking the East China Sea as an example

The ocean fishery and the corresponding environment are highly interrelated according tothe production experiences of ocean fishing population. The spatial cluster patterns are constructed using the remote sensed data and long-time series fishery production data under the uniform coordinate based on GIS techniques. Thus, the hidden information of distribution regularities between ocean-hydrologic factors and central fishing ground can be extracted from these patterns. It is important to forecast the ocean fishery production.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

Rotating soliton clusters in nonlocal nonlinear media

From the study of the dynamics for the ring-like soliton clusters, we find that there exists a critical value of the ring radius, dcr, for the stationary rotation of the clusters with respect to the beam centre even in the presence of the relatively strong noise, and that the soliton clusters will not rotate but only undergo periodic collisions in the form of simple harmonic oscillator if the ring radius is large enough. We also show that the direction of the rotation can be opposite to the direction of phase gradient when the relative phase difference is within the domain 0 〈 ｜θ｜ 〈 π, while along the direction of phase gradient when the relative phase difference is within the domain π 〈｜θ｜ 〈 2π