The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine...The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.展开更多
The research of the spatial heterogeneity of PM2.5 concentration in an area, is of great significance for understanding its regional spatial distribution structure, exploring the transmission relationship between regi...The research of the spatial heterogeneity of PM2.5 concentration in an area, is of great significance for understanding its regional spatial distribution structure, exploring the transmission relationship between regions, in order to formulate joint prevention and control measures within the entire area. Based on the daily monitoring data of PM2.5 concentration in the Central Plains Economic Region in 2019, this paper utilizes cluster analysis to divide the regional PM2.5 concentration into 5 classes, builds their spatial semi-variogram model, and then utilizes interpolation analysis method to study the regional overall distribution characteristics and transmission law. The results show that the PM2.5 concentration in the Central Plains Economic Region has a medium or higher spatial autocorrelation. The critical value of the overall PM2.5 concentration in the area is 150 μg/m3, as the overall PM2.5 concentration less than the value, the PM2.5 in a region mainly comes from local emissions, as the overall PM2.5 concentration higher than the value, the influence of spatial structure on the distribution of PM2.5 concentration is gradually obvious. PM2.5 has a certain degree of spatial transmission, which mainly includes two routes as Puyang-Xingtai and Puyang-Zhengzhou, and the transmission intensity of the former is greater than the latter.展开更多
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used....To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.展开更多
City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordi...City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.展开更多
Introduction:Breast cancer is a leading tumor with a high mortality in women.This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012.Methods:The ...Introduction:Breast cancer is a leading tumor with a high mortality in women.This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012.Methods:The data on breast cancer incidence were obtained from the Shenzhen Cancer Registry System.To describe the temporal trend,the average annual percentage change(AAPC) was analyzed using a pinpoint regression model.Spatial autocorrelation and a retrospective spatio-temporal scan approach were used to detect the spatio-temporal cluster distribution of breast cancer cases.Results:Breast cancer ranked first among different types of cancer in women in Shenzhen between 2007 and 2012 with a crude incidence of 20.0/100,000 population.The age-standardized rate according to the world standard population was 21.1/100,000 in 2012,with an AAPC of 11.3%.The spatial autocorrelation analysis showed a spatial correlation characterized by the presence of a hotspot in south-central Shenzhen,which included the eastern part of Luohu District(Donghu and Liantang Streets) and Yantian District(Shatoujiao,Haishan,and Yantian Streets).Five spatio-temporal cluster areas were detected between 2010 and 2012,one of which was a Class 1 cluster located in southwestern Shenzhen in 2010,which included Yuehai,Nantou,Shahe,Shekou,and Nanshan Streets in Nanshan District with an incidence of 54.1/100,000 and a relative risk of 2.41;the other four were Class 2 clusters located in Yantian,Luohu,Futian,and Longhua Districts with a relative risk ranging from 1.70 to 3.25.Conclusions:This study revealed the spatio-temporal cluster pattern for the incidence of female breast cancer in Shenzhen,which will be useful for a better allocation of health resources in Shenzhen.展开更多
Introduction: Colorectal cancer(CRC) is a common type of neoplasm. This study examined the spatio?temporal distribution of the CRC incidence in Guangzhou during 2010–2014.Methods: Colorectal cancer incidence data wer...Introduction: Colorectal cancer(CRC) is a common type of neoplasm. This study examined the spatio?temporal distribution of the CRC incidence in Guangzhou during 2010–2014.Methods: Colorectal cancer incidence data were obtained from the Guangzhou Cancer Registry System. Spatial autocorrelation analysis and a retrospective spatio?temporal scan were used to assess the spatio?temporal cluster distribution of CRC cases.Results: A total of 14,618 CRC cases were registered in Guangzhou during 2010–2014, with a crude incidence of 35.56/100,000 and an age?standardized rate of incidence by the world standard population(ASRIW) of 23.58/100,000. The crude incidence increased by 19.70% from 2010(32.88/100,000) to 2014(39.36/100,000) with an average annual percentage change(AAPC) of 4.33%. The AAPC of ASRIW was not statistically significant. The spatial autocorrelation analysis revealed a CRC incidence hot spot in central urban areas in Guangzhou City, which included 25 streets in southwestern Baiyun District, northwestern Haizhu District, and the border region between Liwan and Yuexiu Dis?tricts. Three high? and five low?incidence clusters were identified according to spatio?temporal scan of CRC incidence clusters. The high?incidence clusters were located in central urban areas including the border regions between Bai?yun, Haizhu, Liwan, and Yuexiu Districts.Conclusions: This study revealed the spatio?temporal cluster pattern of the incidence of CRC in Guangzhou. This information can inform allocation of health resources for CRC screening.展开更多
为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de...为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。展开更多
Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptom...Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].展开更多
Based on the analysis of its basic characteristics, this article investigated the disparities of Chinese service industry among the three regions （the eastern China, the western China and the middle China） and inter...Based on the analysis of its basic characteristics, this article investigated the disparities of Chinese service industry among the three regions （the eastern China, the western China and the middle China） and inter-provincial disparities of that in the three regions by Theil coefficient and cluster analysis. Then, major factors influencing its spatial disparity were explored by correlation analysis and regression analysis. The conclusions could be drawn as follows. 1） The development of Chinese service industry experienced three phases since the 1980s： rapid growth period, slow growth period, and recovery period. From the proportion of value-added and employment, its development was obviously on the low level. From the composition of industrial structure, traditional service sectors were dominant, but modem service sectors were lagged. Moreover, its spatial disparity was distinct. 2） The level of Chinese service industry was divided into five basic regional ranks： well-developed, developed, relatively-developed, underdeveloped and undeveloped regions, As a whole, the overall structure of spatial disparity was steady in 1990-2005. But there was notable gradient disparity in the interior structure of service industry among different provinces. Furthermore, the overall disparity expanded rapidly in 1990-2005. The inter-provincial disparity of service industry in the three regions, especially in the eastern China, was bigger than the disparity among the three regions. And 3） the level of economic development, the level of urban development, the scale of market capacity, the level of transportation and telecommunication, and the abundance of human resources were major factors influencing the development of Chinese service industry.展开更多
Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters...Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.展开更多
The logistics clusters are the result of concentration, scale and specialization of logistics activities, and their quantitative measurement and development evaluation provide an important foundation for improving the...The logistics clusters are the result of concentration, scale and specialization of logistics activities, and their quantitative measurement and development evaluation provide an important foundation for improving the land use efficiency and achieving economies of scale. Taking 289 cities at prefecture-level and above as research objects, this paper collected macro-statistical data of transport, postal and warehousing industry during 2000–2014, business registration data of more than 290 thousand logistics enterprises, and 170 thousand logistics points of interest（POI）. With the integration of multi-index and multi-source data, the evolution process and spatial pattern of logistics clusters in China were explored with the methods of Location Quotient（LQ）, Horizontal Cluster Location Quotient（HCLQ）, Logistics Employment Density（LED） and modified Logistics Establishments＇ Participation（LEP）. The development levels, types and modes of different logistics clusters were quantified. Several important findings are derived from the study.（1） The logistics clusters are mainly located on the east side of the Hu Huanyong Line, and the accumulative pattern evolves from group to block structure, featuring wide coverage and high concentration. The evolution of logistics clusters has two stages of rapid convergence and stable change, resulting in gradual increase in the development level and efficiency of logistics clusters and in emergence of spillover effect.（2） 21 mature logistics clusters are distributed in the core and sub-cities of the main metropolitan areas of 16 provincial-level administrative divisions, conforming to the government logistics and transport planning. 43 emerging logistics clusters are distributed in 21 provincial administrative divisions, and different types of cities have huge disparities which highlight the differentiation of the market behaviors and government planning among them.（3） The logistics clusters present differentiated development modes with the change of scales. In urban agglomerations scale, the nested ＂center-periphery＂ structures with ＂main nucleus-secondary cores-general nodes＂ are clarified. The polar nuclear development, networked and balanced development, single core and multipoint, multi-core multipoint hub-spoke development patterns are formed in different provincial administrative divisions.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
Analysis Unking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare.Here, we describe a multi-scale analysis using genome-wide SNPs...Analysis Unking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare.Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions.The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia.Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes.The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.展开更多
River Rwizi originates from the Buhweju hills. It is a major source of water for the inhabitants of Mbarara Municipality and surrounding environment. In this study, spatial variation of water quality in River Rwizi se...River Rwizi originates from the Buhweju hills. It is a major source of water for the inhabitants of Mbarara Municipality and surrounding environment. In this study, spatial variation of water quality in River Rwizi section within Mbarara Municipality was determined using cluster analysis. Laboratory analysis was conducted on water samples from five sites along the river section using standard methods for: pH, EC, TSS, TDS, turbidity, temperature, total hardness, alkalinity, salinity, colour, NH3-N, SO2-4, BOD, COD, DO, Ca, Mg, Fe, and Mn. Cluster analysis grouped the study sites into slight pollution (Spencon, GBK), moderate pollution (Katete) and high pollution (BSU, Kakoba) for dry season. For rain season, order was: slight pollution (BSU, Spencon), moderate pollution (GBK) and high pollution (Kakoba, Katete), basing on similarity of water quality variables. These results show that water pollution resulted primarily from domestic waste water, agricultural runoff and industrial effluents. Thus, water from River Rwizi is not suitable for drinking in both dry and wet seasons.展开更多
基金funding from the National Natural Science Foundation of China(No.41572308)。
文摘The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.
文摘The research of the spatial heterogeneity of PM2.5 concentration in an area, is of great significance for understanding its regional spatial distribution structure, exploring the transmission relationship between regions, in order to formulate joint prevention and control measures within the entire area. Based on the daily monitoring data of PM2.5 concentration in the Central Plains Economic Region in 2019, this paper utilizes cluster analysis to divide the regional PM2.5 concentration into 5 classes, builds their spatial semi-variogram model, and then utilizes interpolation analysis method to study the regional overall distribution characteristics and transmission law. The results show that the PM2.5 concentration in the Central Plains Economic Region has a medium or higher spatial autocorrelation. The critical value of the overall PM2.5 concentration in the area is 150 μg/m3, as the overall PM2.5 concentration less than the value, the PM2.5 in a region mainly comes from local emissions, as the overall PM2.5 concentration higher than the value, the influence of spatial structure on the distribution of PM2.5 concentration is gradually obvious. PM2.5 has a certain degree of spatial transmission, which mainly includes two routes as Puyang-Xingtai and Puyang-Zhengzhou, and the transmission intensity of the former is greater than the latter.
基金Projects(41161020,41261026) supported by the National Natural Science Foundation of ChinaProject(BQD2012013) supported by the Research starting Funds for Imported Talents,Ningxia University,China+1 种基金Project(ZR1209) supported by the Natural Science Funds,Ningxia University,ChinaProject(NGY2013005) supported by the Key Science Project of Colleges and Universities in Ningxia,China
文摘To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.
基金Under the auspices of the National Natural Science Foundation of China (No.72273151)。
文摘City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.
文摘Introduction:Breast cancer is a leading tumor with a high mortality in women.This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012.Methods:The data on breast cancer incidence were obtained from the Shenzhen Cancer Registry System.To describe the temporal trend,the average annual percentage change(AAPC) was analyzed using a pinpoint regression model.Spatial autocorrelation and a retrospective spatio-temporal scan approach were used to detect the spatio-temporal cluster distribution of breast cancer cases.Results:Breast cancer ranked first among different types of cancer in women in Shenzhen between 2007 and 2012 with a crude incidence of 20.0/100,000 population.The age-standardized rate according to the world standard population was 21.1/100,000 in 2012,with an AAPC of 11.3%.The spatial autocorrelation analysis showed a spatial correlation characterized by the presence of a hotspot in south-central Shenzhen,which included the eastern part of Luohu District(Donghu and Liantang Streets) and Yantian District(Shatoujiao,Haishan,and Yantian Streets).Five spatio-temporal cluster areas were detected between 2010 and 2012,one of which was a Class 1 cluster located in southwestern Shenzhen in 2010,which included Yuehai,Nantou,Shahe,Shekou,and Nanshan Streets in Nanshan District with an incidence of 54.1/100,000 and a relative risk of 2.41;the other four were Class 2 clusters located in Yantian,Luohu,Futian,and Longhua Districts with a relative risk ranging from 1.70 to 3.25.Conclusions:This study revealed the spatio-temporal cluster pattern for the incidence of female breast cancer in Shenzhen,which will be useful for a better allocation of health resources in Shenzhen.
文摘Introduction: Colorectal cancer(CRC) is a common type of neoplasm. This study examined the spatio?temporal distribution of the CRC incidence in Guangzhou during 2010–2014.Methods: Colorectal cancer incidence data were obtained from the Guangzhou Cancer Registry System. Spatial autocorrelation analysis and a retrospective spatio?temporal scan were used to assess the spatio?temporal cluster distribution of CRC cases.Results: A total of 14,618 CRC cases were registered in Guangzhou during 2010–2014, with a crude incidence of 35.56/100,000 and an age?standardized rate of incidence by the world standard population(ASRIW) of 23.58/100,000. The crude incidence increased by 19.70% from 2010(32.88/100,000) to 2014(39.36/100,000) with an average annual percentage change(AAPC) of 4.33%. The AAPC of ASRIW was not statistically significant. The spatial autocorrelation analysis revealed a CRC incidence hot spot in central urban areas in Guangzhou City, which included 25 streets in southwestern Baiyun District, northwestern Haizhu District, and the border region between Liwan and Yuexiu Dis?tricts. Three high? and five low?incidence clusters were identified according to spatio?temporal scan of CRC incidence clusters. The high?incidence clusters were located in central urban areas including the border regions between Bai?yun, Haizhu, Liwan, and Yuexiu Districts.Conclusions: This study revealed the spatio?temporal cluster pattern of the incidence of CRC in Guangzhou. This information can inform allocation of health resources for CRC screening.
文摘为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。
基金Daniel Shafiee Kermany,Ju Young Ahn,Matthew Vasquez,Lin Wang,Kai Liu,Raksha Raghunathan,Jianting Sheng,Hong Zhao,and Stephen Tin Chi Wong are supported by NCI U01CA252553,NCI R01CA238727,NCI R01CA177909,NCI R01CA244413John S.Dunn Research Foundation,and Ting Tsung and Wei Fong Chao Foundation+3 种基金Xiang Hong-Fei Zhang,Zhan Xu,Xiaoxin Hao,Weijie Zhang are supported by US Department of Defense DAMD W81XWH-16-1-0073(Era of Hope Scholarship)NCI R01CA183878,NCI R01CA251950,NCI U01CA252553,DAMD W81XWH-20-1-0375Breast Cancer Research Foundation,and McNair Medical Institute.Vahid Afshar-Kharghan,Min Soon Cho,Wendolyn Carlos-AlcaldeHani Lee are supported by NCI R01CA177909,NCI R01CA016672,NCI R01CA275762,and NCI P50CA217685.
文摘Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].
基金Under the auspices of National Natural Science Foundation of China (No 40871069)Megaproject of Science and Technology Research for the 11th Five-Year Plan of China (No 2006BAJ05A06)Natural Science Foundation of Beijing City (No 9072002)
文摘Based on the analysis of its basic characteristics, this article investigated the disparities of Chinese service industry among the three regions （the eastern China, the western China and the middle China） and inter-provincial disparities of that in the three regions by Theil coefficient and cluster analysis. Then, major factors influencing its spatial disparity were explored by correlation analysis and regression analysis. The conclusions could be drawn as follows. 1） The development of Chinese service industry experienced three phases since the 1980s： rapid growth period, slow growth period, and recovery period. From the proportion of value-added and employment, its development was obviously on the low level. From the composition of industrial structure, traditional service sectors were dominant, but modem service sectors were lagged. Moreover, its spatial disparity was distinct. 2） The level of Chinese service industry was divided into five basic regional ranks： well-developed, developed, relatively-developed, underdeveloped and undeveloped regions, As a whole, the overall structure of spatial disparity was steady in 1990-2005. But there was notable gradient disparity in the interior structure of service industry among different provinces. Furthermore, the overall disparity expanded rapidly in 1990-2005. The inter-provincial disparity of service industry in the three regions, especially in the eastern China, was bigger than the disparity among the three regions. And 3） the level of economic development, the level of urban development, the scale of market capacity, the level of transportation and telecommunication, and the abundance of human resources were major factors influencing the development of Chinese service industry.
基金Project supported by the National Basic Research Program (973) of China(No. 2005CB724205)China Scholarship Programs of the Ministry ofEducation of China (No. 2006100766).
文摘Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.
基金National Natural Science Foundation of China,No.71603219,No.41501123
文摘The logistics clusters are the result of concentration, scale and specialization of logistics activities, and their quantitative measurement and development evaluation provide an important foundation for improving the land use efficiency and achieving economies of scale. Taking 289 cities at prefecture-level and above as research objects, this paper collected macro-statistical data of transport, postal and warehousing industry during 2000–2014, business registration data of more than 290 thousand logistics enterprises, and 170 thousand logistics points of interest（POI）. With the integration of multi-index and multi-source data, the evolution process and spatial pattern of logistics clusters in China were explored with the methods of Location Quotient（LQ）, Horizontal Cluster Location Quotient（HCLQ）, Logistics Employment Density（LED） and modified Logistics Establishments＇ Participation（LEP）. The development levels, types and modes of different logistics clusters were quantified. Several important findings are derived from the study.（1） The logistics clusters are mainly located on the east side of the Hu Huanyong Line, and the accumulative pattern evolves from group to block structure, featuring wide coverage and high concentration. The evolution of logistics clusters has two stages of rapid convergence and stable change, resulting in gradual increase in the development level and efficiency of logistics clusters and in emergence of spillover effect.（2） 21 mature logistics clusters are distributed in the core and sub-cities of the main metropolitan areas of 16 provincial-level administrative divisions, conforming to the government logistics and transport planning. 43 emerging logistics clusters are distributed in 21 provincial administrative divisions, and different types of cities have huge disparities which highlight the differentiation of the market behaviors and government planning among them.（3） The logistics clusters present differentiated development modes with the change of scales. In urban agglomerations scale, the nested ＂center-periphery＂ structures with ＂main nucleus-secondary cores-general nodes＂ are clarified. The polar nuclear development, networked and balanced development, single core and multipoint, multi-core multipoint hub-spoke development patterns are formed in different provincial administrative divisions.
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.
文摘Analysis Unking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare.Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions.The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia.Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes.The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.
文摘River Rwizi originates from the Buhweju hills. It is a major source of water for the inhabitants of Mbarara Municipality and surrounding environment. In this study, spatial variation of water quality in River Rwizi section within Mbarara Municipality was determined using cluster analysis. Laboratory analysis was conducted on water samples from five sites along the river section using standard methods for: pH, EC, TSS, TDS, turbidity, temperature, total hardness, alkalinity, salinity, colour, NH3-N, SO2-4, BOD, COD, DO, Ca, Mg, Fe, and Mn. Cluster analysis grouped the study sites into slight pollution (Spencon, GBK), moderate pollution (Katete) and high pollution (BSU, Kakoba) for dry season. For rain season, order was: slight pollution (BSU, Spencon), moderate pollution (GBK) and high pollution (Kakoba, Katete), basing on similarity of water quality variables. These results show that water pollution resulted primarily from domestic waste water, agricultural runoff and industrial effluents. Thus, water from River Rwizi is not suitable for drinking in both dry and wet seasons.
