Traditional spatial clustering methods have the disadvantage of ＂hardware division＂, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a gen...Traditional spatial clustering methods have the disadvantage of ＂hardware division＂, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry. Based on infrastructures＇ classification and demarcation in Zhanjiang, a detailed interpretation of clustering results is made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled analysis of residential land prices. General multi-dimensional cloud model reflects the integrated char- acteristics of spatial objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation of cloud model is a specific and challenging task.展开更多
Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying databa...Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.展开更多
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 clusteri...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.展开更多
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 techn...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.展开更多
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 char...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.展开更多
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 discove...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.展开更多
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-scan...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.展开更多
Background:Tuberculosis(TB)is the notifiable infectious disease with the second highest incidence in the Qinghai province,a province with poor primary health care infrastructure.Understanding the spatial distribution ...Background:Tuberculosis(TB)is the notifiable infectious disease with the second highest incidence in the Qinghai province,a province with poor primary health care infrastructure.Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.Methods:Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks,respectively.We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year.A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.Results:The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering(average annual incidence:294/100000)and 17 counties with a significantly low-low spatial clustering(average annual incidence:68/100000)of TB annual incidence within the examined five-year period;the global Moran’s I values ranged from 0.40 to 0.58(all P-values<0.05).The TB incidence was positively associated with the temperature,precipitation,and wind speed(all P-values<0.05),which were confirmed by the spatial panel data model.Each 10°C,2 cm,and 1 m/s increase in temperature,precipitation,and wind speed associated with 9%and 3%decrements and a 7%increment in the TB incidence,respectively.Conclusions:High TB incidence areas were mainly concentrated in south-western Qinghai,while low TB incidence areas clustered in eastern and north-western Qinghai.Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.展开更多
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-p...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.展开更多
HFMD can be caused by a variety of enteroviruses,including Coxsackievirus A16 and enterovirus71.There are no effective therapeutic measures to cure HFMD at present.So,this study aimed to analyze the spatial relativity...HFMD can be caused by a variety of enteroviruses,including Coxsackievirus A16 and enterovirus71.There are no effective therapeutic measures to cure HFMD at present.So,this study aimed to analyze the spatial relativity and the local accumulation type based on the theory of spatial analysis and the spatial autocorrelation analysis module of ArcGIS and Geo Da.We found that there was a seasonal trend in HFMD.The lowest incidence appeared in February,and the peak of the reported incidence was occurred during the period from May to June.However,in most cases,another peak appeared from September to November.The trend of incidence was related to age,too.The overall trend of the reported incidence was a U-shape in north-south orientation and exposed an inverted U-shape in east-west.The correlation between the spatial distribution of HFMD was positive.Hunan,Guangxi and Guangdong were the hot areas,while the cold spots were Jilin,Inner Mongolia,Xinjiang,Gansu and Qinghai.展开更多
Rail transit plays a crucial role in improving urban sustainability and livability.In many Chinese cities,the planning of rail transit routes and stations is focused on facilitating new developments rather than revita...Rail transit plays a crucial role in improving urban sustainability and livability.In many Chinese cities,the planning of rail transit routes and stations is focused on facilitating new developments rather than revitalizing existing built-up areas.This approach reflects the local governments’expectations of substantial growth to reshape the urban structure.However,existing research on transit-oriented development(TOD)rarely explores the spatial interactions between individual transit stations and investigates how they can be integrated to achieve synergistic effects and balanced development.This study proposes that rail transit systems impact urban structure through two“forces”:the provision of additional and reliable carrying capacity and the reduction of travel time between locations.Metro passenger flow is used as a proxy for these forces,and community detection techniques are employed to identify the actual and optimal spatial clusters in Wuhan,China.The results reveal that the planned sub-centers align reasonably well with the optimal spatial clusters in terms of spatial configuration.However,the actual spatial clusters tend to have longer internal travel times compared to the optimal clusters.Further exploration suggests the need for equalizing land use density within planned spatial clusters served by the metro system.Additionally,promoting concentrated,differentiated,and mixed functional arrangements in metro station areas with low passenger flows within the planned clusters could be beneficial.This paper presents a new framework for investigating urban spatial clusters influenced by a metro system.展开更多
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti...Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.展开更多
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 st...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.展开更多
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 recogni...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.展开更多
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 exist...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.展开更多
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
This study focuses on spatial autocorrelation and the spatial distribution of urban land prices from a regional perspective.Taking Hubei province,China,as a case study area,spatial autocorrelation degree,spatial autoc...This study focuses on spatial autocorrelation and the spatial distribution of urban land prices from a regional perspective.Taking Hubei province,China,as a case study area,spatial autocorrelation degree,spatial autocorrelation pattern,and the mechanism of its formation were discussed.The study employs Moran’s I,local Moran’s I,and Moran’s I correlogram to analyze spatial autocorrelation degree and its change along with contiguity order.Some local clustering hot spots are found.This paper uses semi-variance statistic for land price based on route distance to find the spatial autocorrelation scale.We also adopt spatial clustering based on a kind of composite distance to probe into the clustering characteristic of land prices.By Moran’s I and Moran’s I correlogram,we find that datum price of the cities in Hubei province has faint spatial autocorrelation degree at the first and the second-order contiguity.Spatial variance hints that the scale of the autocorrelation is about 200 km in route distance.Spatial clustering result indicates that the spatial distribution of city land price is a kind of hierarchy structure similar to administrative regions.From principal factors analysis and stepwise linear regression,we find that the value added of city secondary and tertiary industry and the urban population are two of the most influential factors to urban datum land price.The value added of city secondary and tertiary industry has higher spatial autocorrelation than urban datum land price and has a bigger autocorrelation scale.But urban population has little spatial autocorrelation.It can be inferred that the spatial autocorrelation of urban land price is mainly caused by economic spatial autocorrelation.But its spatial autocorrelation degree is lower than economic factors because urban datum land price is also influenced by other special local factors,such as population,city infrastructure,land supply,etc.展开更多
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...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.展开更多
As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural sp...As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural space and tourism experience.In this case,it is necessary to explore the spatial characteristics and influencing factors of physical bookstores.This study uses Density-Based Spatial Clustering of Applications with Noise(DBSCAN),spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality(hereafter using cities)in western China.Based on spatial data,population density,road density and other data,this study constructed a data set of the influencing factors of physical bookstores,consisting of 11 factors along 6 dimensions for 3 national central cities in western China.The results are as follows:first,the spatial distribution pattern of physical bookstores in Xi’an,Chengdu,and Chongqing is unbalanced.The spatial distribution of physical bookstores in Xi’an and Chongqing is from southwest to northeast and are relatively clustered,while those in Chengdu are relatively discrete.Second,the spatial distribution pattern of physical bookstores has been formed under the influence of different factors.The intensity and significance of influencing factors differ in the case cities.However,in general,the social factor,business factor,the density of research facilities,tourism factor and road density are the main driving factors in the three cities.There is a synergistic relationship between public libraries and physical bookstores.Third,the explanatory power becomes stronger after the interaction between various factors.In Xi’an and Chengdu,the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors.However,in Chongqing,the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors.The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public.展开更多
Using data from the Economic Advisory Center of the State Information Center(SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions o...Using data from the Economic Advisory Center of the State Information Center(SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers(foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.展开更多
基金National Natural Science Foundation of China, N0.40971102 Knowledge Innovation Project of the Chinese Academy of Sciences, No. KZCX2-YW-322 Special Grant for Postgraduates＇ Scientific Innovation and So- cial Practice in 2008
文摘Traditional spatial clustering methods have the disadvantage of ＂hardware division＂, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry. Based on infrastructures＇ classification and demarcation in Zhanjiang, a detailed interpretation of clustering results is made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled analysis of residential land prices. General multi-dimensional cloud model reflects the integrated char- acteristics of spatial objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation of cloud model is a specific and challenging task.
基金Supported by the National Natural Science Foundation of China （No.60502028, No. 90204008）.
文摘Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(No.51575047)
文摘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.
文摘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.
文摘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.
基金This study was supported by the Qinghai Center for Disease Control and Prevention(CDC).
文摘Background:Tuberculosis(TB)is the notifiable infectious disease with the second highest incidence in the Qinghai province,a province with poor primary health care infrastructure.Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.Methods:Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks,respectively.We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year.A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.Results:The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering(average annual incidence:294/100000)and 17 counties with a significantly low-low spatial clustering(average annual incidence:68/100000)of TB annual incidence within the examined five-year period;the global Moran’s I values ranged from 0.40 to 0.58(all P-values<0.05).The TB incidence was positively associated with the temperature,precipitation,and wind speed(all P-values<0.05),which were confirmed by the spatial panel data model.Each 10°C,2 cm,and 1 m/s increase in temperature,precipitation,and wind speed associated with 9%and 3%decrements and a 7%increment in the TB incidence,respectively.Conclusions:High TB incidence areas were mainly concentrated in south-western Qinghai,while low TB incidence areas clustered in eastern and north-western Qinghai.Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.
基金supported by the National Natural Science Foundation of China(41406146 and 41476129)Shanghai Universities First-class Disciplines Project Fisheries(A)
文摘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.
基金the National Natural Social Science Found of China(Grant Nos.17AJY008)
文摘HFMD can be caused by a variety of enteroviruses,including Coxsackievirus A16 and enterovirus71.There are no effective therapeutic measures to cure HFMD at present.So,this study aimed to analyze the spatial relativity and the local accumulation type based on the theory of spatial analysis and the spatial autocorrelation analysis module of ArcGIS and Geo Da.We found that there was a seasonal trend in HFMD.The lowest incidence appeared in February,and the peak of the reported incidence was occurred during the period from May to June.However,in most cases,another peak appeared from September to November.The trend of incidence was related to age,too.The overall trend of the reported incidence was a U-shape in north-south orientation and exposed an inverted U-shape in east-west.The correlation between the spatial distribution of HFMD was positive.Hunan,Guangxi and Guangdong were the hot areas,while the cold spots were Jilin,Inner Mongolia,Xinjiang,Gansu and Qinghai.
文摘Rail transit plays a crucial role in improving urban sustainability and livability.In many Chinese cities,the planning of rail transit routes and stations is focused on facilitating new developments rather than revitalizing existing built-up areas.This approach reflects the local governments’expectations of substantial growth to reshape the urban structure.However,existing research on transit-oriented development(TOD)rarely explores the spatial interactions between individual transit stations and investigates how they can be integrated to achieve synergistic effects and balanced development.This study proposes that rail transit systems impact urban structure through two“forces”:the provision of additional and reliable carrying capacity and the reduction of travel time between locations.Metro passenger flow is used as a proxy for these forces,and community detection techniques are employed to identify the actual and optimal spatial clusters in Wuhan,China.The results reveal that the planned sub-centers align reasonably well with the optimal spatial clusters in terms of spatial configuration.However,the actual spatial clusters tend to have longer internal travel times compared to the optimal clusters.Further exploration suggests the need for equalizing land use density within planned spatial clusters served by the metro system.Additionally,promoting concentrated,differentiated,and mixed functional arrangements in metro station areas with low passenger flows within the planned clusters could be beneficial.This paper presents a new framework for investigating urban spatial clusters influenced by a metro system.
基金supported in part by Boeing Company and Nanjing University of Aeronautics and Astronautics(NUAA)through the Research on Decision Support Technology of Air Traffic Operation Management in Convective Weather under Project 2022-GT-129in part by the Postgraduate Research and Practice Innovation Program of NUAA(No.xcxjh20240709)。
文摘Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.
基金Under the auspices of National Natural Science Foundation of China(No.41771537)Fundamental Research Funds for the Central Universities
文摘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.
基金Supported by the Open Researches Fund Program of L IESMARS(WKL(0 0 ) 0 30 2 )
文摘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.
基金This work is supported by University IT Research Center ProjectKorea.
文摘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.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
基金This research was funded by the National Natural Science Foundation of China(Nos.41171312 and 40901188).
文摘This study focuses on spatial autocorrelation and the spatial distribution of urban land prices from a regional perspective.Taking Hubei province,China,as a case study area,spatial autocorrelation degree,spatial autocorrelation pattern,and the mechanism of its formation were discussed.The study employs Moran’s I,local Moran’s I,and Moran’s I correlogram to analyze spatial autocorrelation degree and its change along with contiguity order.Some local clustering hot spots are found.This paper uses semi-variance statistic for land price based on route distance to find the spatial autocorrelation scale.We also adopt spatial clustering based on a kind of composite distance to probe into the clustering characteristic of land prices.By Moran’s I and Moran’s I correlogram,we find that datum price of the cities in Hubei province has faint spatial autocorrelation degree at the first and the second-order contiguity.Spatial variance hints that the scale of the autocorrelation is about 200 km in route distance.Spatial clustering result indicates that the spatial distribution of city land price is a kind of hierarchy structure similar to administrative regions.From principal factors analysis and stepwise linear regression,we find that the value added of city secondary and tertiary industry and the urban population are two of the most influential factors to urban datum land price.The value added of city secondary and tertiary industry has higher spatial autocorrelation than urban datum land price and has a bigger autocorrelation scale.But urban population has little spatial autocorrelation.It can be inferred that the spatial autocorrelation of urban land price is mainly caused by economic spatial autocorrelation.But its spatial autocorrelation degree is lower than economic factors because urban datum land price is also influenced by other special local factors,such as population,city infrastructure,land supply,etc.
基金This research was partially supported by the National Hi-technology Program of China under contract No.2001AA633010,No.2001AA639080 and No.2002AA639460.
文摘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.
基金Under the auspices of National Natural Science Foundation of China(No.41271179)。
文摘As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural space and tourism experience.In this case,it is necessary to explore the spatial characteristics and influencing factors of physical bookstores.This study uses Density-Based Spatial Clustering of Applications with Noise(DBSCAN),spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality(hereafter using cities)in western China.Based on spatial data,population density,road density and other data,this study constructed a data set of the influencing factors of physical bookstores,consisting of 11 factors along 6 dimensions for 3 national central cities in western China.The results are as follows:first,the spatial distribution pattern of physical bookstores in Xi’an,Chengdu,and Chongqing is unbalanced.The spatial distribution of physical bookstores in Xi’an and Chongqing is from southwest to northeast and are relatively clustered,while those in Chengdu are relatively discrete.Second,the spatial distribution pattern of physical bookstores has been formed under the influence of different factors.The intensity and significance of influencing factors differ in the case cities.However,in general,the social factor,business factor,the density of research facilities,tourism factor and road density are the main driving factors in the three cities.There is a synergistic relationship between public libraries and physical bookstores.Third,the explanatory power becomes stronger after the interaction between various factors.In Xi’an and Chengdu,the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors.However,in Chongqing,the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors.The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public.
基金Under the auspices of National Natural Science Foundation of China(No.41301143)
文摘Using data from the Economic Advisory Center of the State Information Center(SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers(foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.
