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
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
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
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
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
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
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
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
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 characteri...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.展开更多
Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Sheng...Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Shengqiu county,Henan province,China.Spatial autocorrelation analysis was employed to detect significant differences from a random spatial distribution of liver cancer incidence.Spatial scan statistics were used to detect and evaluate the clusters of liver cancer cases.Spatial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
As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms ...As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).展开更多
The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transf...The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.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
In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCI...In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.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
Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire act...Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire activity in Mexico using georeferenced fire records for the period of 2005–2015 and examined its spatial and temporal relationships with a multiscalar drought index, the Standardized Precipitation-Evapotranspiration Index(SPEI). A total of 47975 fire counts were recorded in the 11-year long study period, with the peak in fire frequency occurring in 2011. We identified four fire clusters, i.e., regions where there is a high density of fire records in Mexico using the Getis-Ord G spatial statistic. Then, we examined fire frequency data in the clustered regions and assessed how fire activity related to the SPEI for the entire study period and also for the year 2011. Associations between the SPEI and fire frequency varied across Mexico and fire-SPEI relationships also varied across the months of major fire occurrence and related SPEI temporal scales. In particular, in the two fire clusters located in northern Mexico(Chihuahua, northern Baja California), drier conditions over the previous 5 months triggered fire occurrence. In contrast, we did not observe a significant relationship between drought severity and fire frequency in the central Mexico cluster, which exhibited the highest fire frequency. We also found moderate fire-drought associations in the cluster situated in the tropical southern Chiapas where agriculture activities are the main causes of forest fire occurrence. These results are useful for improving our understanding of the spatiotemporal patterns of fire occurrence as related to drought severity in megadiverse countries hosting many forest types as Mexico.展开更多
针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行...针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。展开更多
Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services （ES） conservation. However, little research has identified and tested the compactness and effic...Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services （ES） conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi＊ statistics, we quantified the variation of annual soil conservation services （SC） and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1） areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2） SC hotspots and coldspots showed an obvious spatial differentiation--the hotspots were mainly spatially ag- gregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3） the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi＊ statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.展开更多
The comprehensive regionalization of Chinese human geography is based on the rules governing regional differentiation of Chinese physical geographic factors.Based on regional differences and similarities in human fact...The comprehensive regionalization of Chinese human geography is based on the rules governing regional differentiation of Chinese physical geographic factors.Based on regional differences and similarities in human factors,this study divides the whole country into two levels of relatively independent,complete and organically linked human geographic units.As a fundamental,comprehensive,cutting-edge,practical and important task,the comprehensive regionalization of human geography highlights the characteristics,regional and sub-regional features,complexity and variety of spatial differences between factors of Chinese human geography.It is capable of promoting the development of human geography based on local conditions,providing basic scientific support to national and local development strategies,such as the Belt and Road Strategy,new urbanization and environmental awareness,and creating a sound geopolitical environment in key areas.Using results from existing physical and human geographic zoning studies,and in accordance with the principles of synthesis,dominant factors,the relative consistency of the natural environment,the relative consistency of social and economic development,the consistency of the regional cultural landscape,the continuity of spatial distribution and the integrity of county-level administrative divisions,and taking as its basis the division of human geography into 10 major factors(nature,economy,population,culture,ethnicity,agriculture,transportation,urbanization,the settlement landscape and administrative divisions),this paper constructs an index system for the comprehensive regionalization of Chinese human geography through a combination of top-down and bottom-up zoning and spatial clustering analysis.In this study,Chinese human geography is divided into eight regions and 66 sub-regions.The eight human geography regions are(Ⅰ)Northeast China,(Ⅱ)North China,(Ⅲ)East China,(Ⅳ)Central China,(Ⅴ)South China,(Ⅵ)Northwest China,(Ⅶ)Southwest China,and(Ⅷ)Qinghai and Tibet.This zoning proposal fills gaps in studies involving the non-comprehensive regionalization of Chinese human geography.Each human geography region and sub-region has different topographical climatic,ecological,population,urbanization,economic development,settlement landscape,regional cultural and ethno-religious attributes.This proposal on the comprehensive regionalization of Chinese human geography dovetails closely with previous studies on comprehensive regionalization in Chinese physical geography,Chinese economic zoning,and Chinese agricultural zoning.It shows that,under the dual roles of nature and humans,there are certain rules of regional differentiation that govern the comprehensive regionalization of Chinese human geography.展开更多
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.
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.
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.
基金Under the auspices of National Social Science Foundation of China (No.21BJY202)。
文摘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.
基金supported by research grants form 12th five years plan of National Science and Technology Infrastructure Program(2013BAI12B03)11th five years plan of National Science and Technology Infrastructure Program(2006BAI19B03)
文摘Liver cancer is a common and leading cause of cancer death in China.We used the cancer registry data collected from 2009 to 2011 to describe the spatial distribution of liver cancer incidence at village level in Shengqiu county,Henan province,China.Spatial autocorrelation analysis was employed to detect significant differences from a random spatial distribution of liver cancer incidence.Spatial scan statistics were used to detect and evaluate the clusters of liver cancer cases.Spatial
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.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No.2021R1F1A1049387).
文摘As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).
文摘The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.
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the National Natural Science Foundation of China(Nos.42176188,42176192)the Hainan Provincial Natural Science Foundation of China(No.421CXTD442)+2 种基金the Stable Supporting Fund of Acoustic Science and Technology Laboratory(No.JCKYS2024604SSJS007)the Fundamental Research Funds for the Central Universities(No.3072024CFJ0504)the Harbin Engineering University Doctoral Research and Innovation Fund(No.XK2050021034)。
文摘In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.
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 Universidad Juárez del Estado de Durango,Project PRODEP 2017(No.120418)
文摘Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire activity in Mexico using georeferenced fire records for the period of 2005–2015 and examined its spatial and temporal relationships with a multiscalar drought index, the Standardized Precipitation-Evapotranspiration Index(SPEI). A total of 47975 fire counts were recorded in the 11-year long study period, with the peak in fire frequency occurring in 2011. We identified four fire clusters, i.e., regions where there is a high density of fire records in Mexico using the Getis-Ord G spatial statistic. Then, we examined fire frequency data in the clustered regions and assessed how fire activity related to the SPEI for the entire study period and also for the year 2011. Associations between the SPEI and fire frequency varied across Mexico and fire-SPEI relationships also varied across the months of major fire occurrence and related SPEI temporal scales. In particular, in the two fire clusters located in northern Mexico(Chihuahua, northern Baja California), drier conditions over the previous 5 months triggered fire occurrence. In contrast, we did not observe a significant relationship between drought severity and fire frequency in the central Mexico cluster, which exhibited the highest fire frequency. We also found moderate fire-drought associations in the cluster situated in the tropical southern Chiapas where agriculture activities are the main causes of forest fire occurrence. These results are useful for improving our understanding of the spatiotemporal patterns of fire occurrence as related to drought severity in megadiverse countries hosting many forest types as Mexico.
文摘针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。
基金National Natural Science Foundation of China, No.41601182 National Social Science Foundation of China, No.14AZD094+3 种基金 National Key Research and Development Plan of China, No.2016YFC0501601 China Postdoctoral Science Foundation, No.2016M592743 Fundamental Research Funds for the Central Universities, No.GK201603078 Key Project of the Ministry of Education of China, No. 15JJD790022Acknowledgments We are grateful to the anonymous reviewers for their constructive advice about the paper, and we also thank Chen Guoyong from the Hunan University, who provided important aid in calculating the annual soil conservation of Shaanxi by MATLAB programming.
文摘Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services （ES） conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi＊ statistics, we quantified the variation of annual soil conservation services （SC） and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1） areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2） SC hotspots and coldspots showed an obvious spatial differentiation--the hotspots were mainly spatially ag- gregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3） the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi＊ statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.
基金Major Program of the National Natural Science Foundation of China, No.41590840, No.41590842,Acknowledgements The authors thank the following people for their valuable suggestions and guidance in the course of writing this paper: Professor Zheng Du, Academician of the Chinese Academy of Sciences Professor Li Wenhua, Academician of the Chinese Academy of Engineering+8 种基金 Professor Song Changqing from Beijing Normal University Professor Kong Deyong from the Chinese Academy of Science and Technology for Development Professor Mao Hanying from the International Eurasian Academy of Sciences Professor Cai Yunlong from Peking University Professor Zhou Shangyi from Beijing Normal University Professor Wu Shaohong from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences Professor Zhang Guoyou from the Geographical Society of China Professor He Shujin, Managing Director of the Editorial Office of Acta Geographica Sinica and Professor Shen Yuming from Capital Normal University.
文摘The comprehensive regionalization of Chinese human geography is based on the rules governing regional differentiation of Chinese physical geographic factors.Based on regional differences and similarities in human factors,this study divides the whole country into two levels of relatively independent,complete and organically linked human geographic units.As a fundamental,comprehensive,cutting-edge,practical and important task,the comprehensive regionalization of human geography highlights the characteristics,regional and sub-regional features,complexity and variety of spatial differences between factors of Chinese human geography.It is capable of promoting the development of human geography based on local conditions,providing basic scientific support to national and local development strategies,such as the Belt and Road Strategy,new urbanization and environmental awareness,and creating a sound geopolitical environment in key areas.Using results from existing physical and human geographic zoning studies,and in accordance with the principles of synthesis,dominant factors,the relative consistency of the natural environment,the relative consistency of social and economic development,the consistency of the regional cultural landscape,the continuity of spatial distribution and the integrity of county-level administrative divisions,and taking as its basis the division of human geography into 10 major factors(nature,economy,population,culture,ethnicity,agriculture,transportation,urbanization,the settlement landscape and administrative divisions),this paper constructs an index system for the comprehensive regionalization of Chinese human geography through a combination of top-down and bottom-up zoning and spatial clustering analysis.In this study,Chinese human geography is divided into eight regions and 66 sub-regions.The eight human geography regions are(Ⅰ)Northeast China,(Ⅱ)North China,(Ⅲ)East China,(Ⅳ)Central China,(Ⅴ)South China,(Ⅵ)Northwest China,(Ⅶ)Southwest China,and(Ⅷ)Qinghai and Tibet.This zoning proposal fills gaps in studies involving the non-comprehensive regionalization of Chinese human geography.Each human geography region and sub-region has different topographical climatic,ecological,population,urbanization,economic development,settlement landscape,regional cultural and ethno-religious attributes.This proposal on the comprehensive regionalization of Chinese human geography dovetails closely with previous studies on comprehensive regionalization in Chinese physical geography,Chinese economic zoning,and Chinese agricultural zoning.It shows that,under the dual roles of nature and humans,there are certain rules of regional differentiation that govern the comprehensive regionalization of Chinese human geography.
