Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however...Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however,those require improvements in terms of economic feasibility,convenience,and lateral resolution.To address this,this study examined an extraction method to determine spatial autocorrelation velocity dispersion curves for application in near-surface exploration.展开更多
Information on crop acreage is important for formulating national food polices and economic planning. Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information syst...Information on crop acreage is important for formulating national food polices and economic planning. Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system （GIS） technology, provides an efficient way to estimate crop acreage at the regional scale. Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among crop acreage contained in the sampling units. In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic crop map derived from Systeme Probatoire d＇Observation de la Terre （SPOT-5） imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two crop acreages within the sampling units. Moran＇s/, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two crop acreages in this study. The results showed that although the spatial autocorrelation level among maize and rice acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two crop acreages included in the sampling units （Moran＇s / varied from 0.49 to 0.89）, irrespective of the sampling unit size. When the sampling unit size was less than 3000 m, the stratification design that used crop planting intensity （CPI） as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum. Therefore, the traditional sampling methods can be used to estimate the two crop acreages. Compared with CPI, there was still a strong spatial correlation among the two crop acreages included in the sampling units belonging to each stratum when cultivated land fragmentation and ground slope were used as stratification criterion. As far as the selection of stratification criteria and sampling unit size is concerned, this study provides a basis for formulating a reasonable spatial sampling scheme to estimate crop acreage.展开更多
Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation＇s dynamic response to and feedback effects on climate cha...Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation＇s dynamic response to and feedback effects on climate change. For the special geographical location and climatic characteristics of the Qaidam Basin, with the support of traditional and remote sensing data, in this paper a vegetation coverage model was established. The quantitative prediction of vegetation coverage by five environmental factors was initially realized through multiple stepwise regression （MSR） models. However, there is significant multicollinearity among these five environmental factors, which reduces the performance of the MSR model. Then through the introduction of the Moran Index, an indicator that reflects the spatial autocorrelation of vegetation distribution, only two variables of average annual rainfall and local Moran Index were used in the final establishment of the vegetation coverage model. The results show that there is significant spatial autocorrelation in the distribution of vegetation. The role of spatial autocorrelation in the establishment of vegetation coverage model has not only improved the model fitting R2 from 0.608 to 0.656, but also removed the multicollinearity among independents.展开更多
This paper uses a spatial statistics method based on the calculation of spatial autocorrelation as a possible approach for modeling and quantifying the distribution of urban land price in Changzhou City, Jiangsu Provi...This paper uses a spatial statistics method based on the calculation of spatial autocorrelation as a possible approach for modeling and quantifying the distribution of urban land price in Changzhou City, Jiangsu Province. GIS and spatial statistics provide a useful way for describing the distribution of urban land price both spatially and temporally, and have proved to be useful for understanding land price distribution pattern better. In this paper, we apply the statistical analysis method to 8379 urban land price samples collected from Changzhou Land Market, and it is turned out that the proposed approach can effectively identify the spatial clusters and local point patterns in dataset and forms a general method for conceptualizing the land price structure. The results show that land price structure in Changzhou City is very complex and that even where there is a high spatial autocorrelation, the land price is still relatively heterogeneous. Furthermore, lands for different uses have different degrees of spatial autocorrelation. Spatial autocorrelation of commercial lands is more intense than that of residential and industrial lands in regional central district. This means that treating land price as integration of homogeneous units can limit analysis of pattern, over-simplifying the structure of land price, but the methods, just as the autocorrelation approaches, are useful tools for quantifying the variables of land price.展开更多
Popular regional inequality indexes such as variation coefficient and Gini coefficient can only reveal overall inequality, and have limited ability in revealing spatial dependence or spatial agglomeration. Recently so...Popular regional inequality indexes such as variation coefficient and Gini coefficient can only reveal overall inequality, and have limited ability in revealing spatial dependence or spatial agglomeration. Recently some methods of exploratory spatial data analysis such as spatial autocorrelation have provided effective tools to analyze spatial agglomeration and cluster, which can reveal the pattern of regional inequality. This article attempts to use spatial autocorrelation at county level to get refined spatial pattern of regional disparity in Chinese northeast economic region over 2000-2006 (2001 absent). The result indicates that the basic trend of regional economy is an increasing concentration of growth among counties in northeast economic region, and there are two geographical clusters of poorer counties including the counties in western Liaoning Province and adjacent counties in Inner Mongolia, poorer counties of Heihe, Qiqihar and Suihua in Heilongjiang Province. This article also reveals that we can use the methods of exploratory spatial data analysis as the supplementary analysis methods in regional economic analysis.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
Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is of...Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is often invalid. In this study, we collected data of two fisheries, squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model(GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's I was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper, we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.展开更多
A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban populatio...A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban population density. The new method and model are applied to Hangzhou City, China, as an example. The average distance of population activities, the auto-correlation coefficient of urban population density, and the auto-regressive function values all show trends of gradual increase from 1964 to 2000, but there always is a sharp first-order cutoff in the partial auto- correlations. These results indicate that urban development is a process of localization. The discovery of urban locality is significant to improve the cellular-automata-based urban simulation of modeling spatial complexity.展开更多
Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation ...Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran＇s heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gi statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.展开更多
According to Statistical Yearbook of Jiangxi Province(2001～2006),We analyze the time-space variation of population distribution of Poyang Lake region from the two points of view.The former is quality of population,wh...According to Statistical Yearbook of Jiangxi Province(2001～2006),We analyze the time-space variation of population distribution of Poyang Lake region from the two points of view.The former is quality of population,which involves culture structure,occupational structure,age structure and sex structure of population.The latter is quantity of population,which only involves the amount of population.Furthermore,we can reveal the internal relations and action mechanism of variation of population distribution by analyzing the regional economic development,population urbanization,land use and ecological landscape of Poyang Lake region.It is important to provide help for region planning,ecological landscape planning and environmental protection by correct understanding the man-land relationship of natural-human ecosystem in Poyang Lake region.展开更多
We analyzed the fine-scale spatial genetic structure of the individuals of Zelkova schneideriana , which were classified by age using the spatial autocorrelation method, to quantify spatial patterns of genetic variati...We analyzed the fine-scale spatial genetic structure of the individuals of Zelkova schneideriana , which were classified by age using the spatial autocorrelation method, to quantify spatial patterns of genetic variation within the population and to explore potential mechanisms that determine genetic variation in population. The spatial autocorrelation coefficient （ r ） at 13 distance classes was determined on the basis of both geographical distance and genetic distance matrix which was derived from co-dominant SSR data using GenAlEx software. The results showed that all the individuals of Z. schneideriana exhibited significantly positive spatial genetic structure at distance less than 40 m （the X -intercept was 53.568）, indicating that the average length of the smallest genetic patch for the same genotype clustering of the Z. schneideriana Mailing population was about 50 m. Limited seed dispersal is the main factor that leads to the spatial genetic variation within populations. The individuals in age Class II showed significantly positive spatial genetic structure at distance less than 30 m （the X -intercept was 47.882）, while the individuals in age Class I and age Class III showed no significant spatial genetic structure in any of the spatial distance classes. Z. schneideriana is a long-lived perennial plant; the self-thinning resulted from the cohort competition between individuals in the growing process may lead to this certain spatial structure in age Class III of Z. schneideriana population.展开更多
This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency ov...This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency over space due to geographic proximity. Usually crash caused congestion on a freeway segment propagates upstream and creates chance of occurring secondary crashes. This phenomenon makes the crash frequency on the contiguous freeway segments correlated. This correlation makes the distributional assumption of independence of crash frequency invalid. The existence of spatial autocorrelation is investigated by using Conditional autoregressive models (CAR models). The models are set up in a Bayesian modeling framework, to include terms which help to identify and quantify residual spatial autocorrelation for neighboring observation units. Models which recognize the presence of spatial dependence help to obtain unbiased estimates of parameters quantifying safety levels since the effects of spatial autocorrelation are accounted for in the modeling process. Based on CAR models, approximately 51% of crash frequencies across contiguous freeway segments are spatially auto-correlated. The incident rate ratios revealed that wider shoulder and weaving segments decreased crash frequency by factors of 0.84 and 0.75 respectively. The marginal impacts graphs showed that an increase in longitudinal space for segments with two lanes decreased crash frequency. However, an increase of facility width above three lanes results in more crashes, which indicates an increase in traffic flows and driving behavior leading to crashes. These results call an important step of analyzing contagious freeway segments simultaneously to account for the existence of spatial autocorrelation.展开更多
Background:The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague,which can be used not only to detect the spatial and temporal distributions of Meriones unguicul...Background:The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague,which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus,but also to reveal its cluster rule.This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014,in order to predict plague outbreaks.Methods:Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils.Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods.The quantity of M.unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention.Results:The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index.High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high.In terms of time series,the area of the epidemic focus gradually increased from 2005 to 2007,declined rapidly in 2008 and 2009,and then decreased slowly and began trending towards stability from 2009 to 2014.For the spatial change,the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007,and then moved from north to south in 2007 and 2008.Conclusions:The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation.The diversity of temporary and spatial distribution is mainly affected by seasonal variation,the human activity and natural factors.展开更多
Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations a...Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations and spatial dependence have been rarely taken into account in the modeling processes.Recently,Joint Species Distribution Models(JSDMs)offer the opportunity to consider both environmental factors and interspecific relationships as well as the role of spatial structures.This study uses the HMSC(Hierarchical Modelling of Species Communities)framework to model the multispecies distribution of a marine fish assemblage,in which spatial associations and spatial dependence is deliberately accounted for.Three HMSC models were implemented with different structures of random effects to address the existence of spatial associations and spatial dependence,and the predictive performances at different levels of sample sizes were analyzed in the assessment.The results showed that the models with random effects could account for a larger proportion of explainable variance(32.8%),and particularly the spatial random effect model provided the best predictive performances(R_(mean)^(2)=0.31),indicating that spatial random effects could substantially influence the results of the joint species distribution.Increasing sample size had a strong effect(R_(mean)^(2)=0.24-0.31)on the predictive accuracy of the spatially-structured model than on the other models,suggesting that optimal model selection should be dependent on sample size.This study highlights the importance of incorporating spatial random effects for JSDM predictions and suggests that the choice of model structures should consider the data quality across species.展开更多
Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of a...Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.展开更多
Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regiona...Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.展开更多
This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of glo...This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.展开更多
The capacity of livestock breeding in China has increased rapidly since 1949,and the total output of meat,poultry and eggs maintains the world's top first in recent 20 years.Livestock emissions and pollution is cl...The capacity of livestock breeding in China has increased rapidly since 1949,and the total output of meat,poultry and eggs maintains the world's top first in recent 20 years.Livestock emissions and pollution is closely associated with its population and spatial distribution.This paper aims to investigate the spatial patterns of livestock and poultry breeding in China.Using statistical yearbook and agricultural survey in 2007,the county-level populations of livestock and poultry are estimated as equivalent standardized pig index(ESP),per cultivated land pig index(PCLP)and per capita pig index(PCP).With the help of spatial data analysis(ESDA)tools in Geoda and ArcGIS software,especially the Moran's I and LISA statistics,the nationwide global and local clustering trends of the three indicators are examined respectively.The Moran's I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally.However,PCLP is clustering locally but not significant globally.Furthermore,the thematic map series(TMS)and related gravity centers curve(GCC)are introduced to explore the spatial patterns of livestock and poultry in China.The indicators are classified into 16 levels,and the GCCs for the three indicators from level 1 to 16 are discussed in detail.For districting purpose,each interval between gravity centers of near levels for all the three indicators is calculated,and the districting types of each indicator are obtained by merging adjacent levels.The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding.As a result,the China's livestock and poultry breeding would be classified into eight types:extremely sparse region,sparse region,relatively sparse region,normally sparse region,normal region,relatively concentrated region,concentrated region and highly concentrated region.It is also found that there exists a clear demarcation line between the concentrated and the sparse regions.The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner,Inner Mongolia Autonomous Region to the west coast of Dongfang County,Hainan Province.展开更多
In order to evaluate whether or not the county units′ economy in the Changsha-Zhuzhou-Xiangtan(Chang-Zhu-Tan) Urban Agglomeration was growing as expected, this study analyzed the spatial economy pattern at county-lev...In order to evaluate whether or not the county units′ economy in the Changsha-Zhuzhou-Xiangtan(Chang-Zhu-Tan) Urban Agglomeration was growing as expected, this study analyzed the spatial economy pattern at county-level by using the Exploratory Spatial Data Analysis(ESDA) method. In this process, the global Moran′s I and local Getis-Ord G*i indexes were employed to analyze indicators including per capita GDP and three industrials(i.e. primary, secondary and tertiary industry) from 2000 to 2010. The results show that: 1) the county units′ economy in the Chang-Zhu-Tan Urban Agglomeration has exhibited a strong spatial autocorrelation and an accelerated integration trend since 2008(Moran′ s I increased from 0.26 to 0.56); 2) there is a significant difference in economy development between the northern and southern county units in the Chang-Zhu-Tan Urban Agglomeration: the hotspot zone with high economic level was formed among the northern county units whereas the coldspot zone with low economic level was located in the southern areas. This difference was caused primarily by the increasingly prominent economic radiation effect of Changsha ′upheaval′; 3) town density, secondary industry, and the integration policy are the major contributors driving the evolution of the spatial economy pattern in the Chang-Zhu-Tan Urban Agglomeration.展开更多
基金supported by the Henan Province science and technology research project(Grant No.242102321031)National Natural Science Foundation of China(grant numbers 42207200).
文摘Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however,those require improvements in terms of economic feasibility,convenience,and lateral resolution.To address this,this study examined an extraction method to determine spatial autocorrelation velocity dispersion curves for application in near-surface exploration.
基金financially supported by the National Natural Science Foundation of China (41471365,41531179)
文摘Information on crop acreage is important for formulating national food polices and economic planning. Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system （GIS） technology, provides an efficient way to estimate crop acreage at the regional scale. Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among crop acreage contained in the sampling units. In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic crop map derived from Systeme Probatoire d＇Observation de la Terre （SPOT-5） imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two crop acreages within the sampling units. Moran＇s/, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two crop acreages in this study. The results showed that although the spatial autocorrelation level among maize and rice acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two crop acreages included in the sampling units （Moran＇s / varied from 0.49 to 0.89）, irrespective of the sampling unit size. When the sampling unit size was less than 3000 m, the stratification design that used crop planting intensity （CPI） as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum. Therefore, the traditional sampling methods can be used to estimate the two crop acreages. Compared with CPI, there was still a strong spatial correlation among the two crop acreages included in the sampling units belonging to each stratum when cultivated land fragmentation and ground slope were used as stratification criterion. As far as the selection of stratification criteria and sampling unit size is concerned, this study provides a basis for formulating a reasonable spatial sampling scheme to estimate crop acreage.
基金National Natural Science Foundation of China, No.90302009 Project of the Ministry of Water Resources of China, No.201101047
文摘Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation＇s dynamic response to and feedback effects on climate change. For the special geographical location and climatic characteristics of the Qaidam Basin, with the support of traditional and remote sensing data, in this paper a vegetation coverage model was established. The quantitative prediction of vegetation coverage by five environmental factors was initially realized through multiple stepwise regression （MSR） models. However, there is significant multicollinearity among these five environmental factors, which reduces the performance of the MSR model. Then through the introduction of the Moran Index, an indicator that reflects the spatial autocorrelation of vegetation distribution, only two variables of average annual rainfall and local Moran Index were used in the final establishment of the vegetation coverage model. The results show that there is significant spatial autocorrelation in the distribution of vegetation. The role of spatial autocorrelation in the establishment of vegetation coverage model has not only improved the model fitting R2 from 0.608 to 0.656, but also removed the multicollinearity among independents.
基金Under the auspices of the National Natural Science Foundation of China (No. 40371091), Land Monitoring Project ofthe Ministry of Land and Resources of P. R. China (No. 2005-6.1-6)
文摘This paper uses a spatial statistics method based on the calculation of spatial autocorrelation as a possible approach for modeling and quantifying the distribution of urban land price in Changzhou City, Jiangsu Province. GIS and spatial statistics provide a useful way for describing the distribution of urban land price both spatially and temporally, and have proved to be useful for understanding land price distribution pattern better. In this paper, we apply the statistical analysis method to 8379 urban land price samples collected from Changzhou Land Market, and it is turned out that the proposed approach can effectively identify the spatial clusters and local point patterns in dataset and forms a general method for conceptualizing the land price structure. The results show that land price structure in Changzhou City is very complex and that even where there is a high spatial autocorrelation, the land price is still relatively heterogeneous. Furthermore, lands for different uses have different degrees of spatial autocorrelation. Spatial autocorrelation of commercial lands is more intense than that of residential and industrial lands in regional central district. This means that treating land price as integration of homogeneous units can limit analysis of pattern, over-simplifying the structure of land price, but the methods, just as the autocorrelation approaches, are useful tools for quantifying the variables of land price.
基金supported by the National Natural Science Foundation for Distinguished Young Scholar of China (Grant No.40225004)
文摘Popular regional inequality indexes such as variation coefficient and Gini coefficient can only reveal overall inequality, and have limited ability in revealing spatial dependence or spatial agglomeration. Recently some methods of exploratory spatial data analysis such as spatial autocorrelation have provided effective tools to analyze spatial agglomeration and cluster, which can reveal the pattern of regional inequality. This article attempts to use spatial autocorrelation at county level to get refined spatial pattern of regional disparity in Chinese northeast economic region over 2000-2006 (2001 absent). The result indicates that the basic trend of regional economy is an increasing concentration of growth among counties in northeast economic region, and there are two geographical clusters of poorer counties including the counties in western Liaoning Province and adjacent counties in Inner Mongolia, poorer counties of Heihe, Qiqihar and Suihua in Heilongjiang Province. This article also reveals that we can use the methods of exploratory spatial data analysis as the supplementary analysis methods in regional economic analysis.
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Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA092303)the Public Science and Technology Research Funds Projects of Ocean(No.20155014)+2 种基金the Shanghai Leading Academic Discipline Projectthe Funding Program for Outstanding Dissertation in Shanghai Ocean UniversitySupported by SHOU International Center for Marine Studies and Shanghai 1000 Talent Program
文摘Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is often invalid. In this study, we collected data of two fisheries, squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model(GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's I was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper, we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.
基金Under the auspices of the National Natural Science Foundation of China (No. 40371039)
文摘A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban population density. The new method and model are applied to Hangzhou City, China, as an example. The average distance of population activities, the auto-correlation coefficient of urban population density, and the auto-regressive function values all show trends of gradual increase from 1964 to 2000, but there always is a sharp first-order cutoff in the partial auto- correlations. These results indicate that urban development is a process of localization. The discovery of urban locality is significant to improve the cellular-automata-based urban simulation of modeling spatial complexity.
文摘Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran＇s heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gi statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.
文摘According to Statistical Yearbook of Jiangxi Province(2001～2006),We analyze the time-space variation of population distribution of Poyang Lake region from the two points of view.The former is quality of population,which involves culture structure,occupational structure,age structure and sex structure of population.The latter is quantity of population,which only involves the amount of population.Furthermore,we can reveal the internal relations and action mechanism of variation of population distribution by analyzing the regional economic development,population urbanization,land use and ecological landscape of Poyang Lake region.It is important to provide help for region planning,ecological landscape planning and environmental protection by correct understanding the man-land relationship of natural-human ecosystem in Poyang Lake region.
基金Supported by the Scientific Research and Technological Development Project of Guangxi(1598025-42)the Guangxi Youth Fund Project(2013GXNSFBA019093)+1 种基金the National Forestry Public Welfare Industry Research Project(200904011)the Open Project for Guangxi Key Laboratory of Superior Timber Trees Resource Cultivation(15-B-03-01)
文摘We analyzed the fine-scale spatial genetic structure of the individuals of Zelkova schneideriana , which were classified by age using the spatial autocorrelation method, to quantify spatial patterns of genetic variation within the population and to explore potential mechanisms that determine genetic variation in population. The spatial autocorrelation coefficient （ r ） at 13 distance classes was determined on the basis of both geographical distance and genetic distance matrix which was derived from co-dominant SSR data using GenAlEx software. The results showed that all the individuals of Z. schneideriana exhibited significantly positive spatial genetic structure at distance less than 40 m （the X -intercept was 53.568）, indicating that the average length of the smallest genetic patch for the same genotype clustering of the Z. schneideriana Mailing population was about 50 m. Limited seed dispersal is the main factor that leads to the spatial genetic variation within populations. The individuals in age Class II showed significantly positive spatial genetic structure at distance less than 30 m （the X -intercept was 47.882）, while the individuals in age Class I and age Class III showed no significant spatial genetic structure in any of the spatial distance classes. Z. schneideriana is a long-lived perennial plant; the self-thinning resulted from the cohort competition between individuals in the growing process may lead to this certain spatial structure in age Class III of Z. schneideriana population.
文摘This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency over space due to geographic proximity. Usually crash caused congestion on a freeway segment propagates upstream and creates chance of occurring secondary crashes. This phenomenon makes the crash frequency on the contiguous freeway segments correlated. This correlation makes the distributional assumption of independence of crash frequency invalid. The existence of spatial autocorrelation is investigated by using Conditional autoregressive models (CAR models). The models are set up in a Bayesian modeling framework, to include terms which help to identify and quantify residual spatial autocorrelation for neighboring observation units. Models which recognize the presence of spatial dependence help to obtain unbiased estimates of parameters quantifying safety levels since the effects of spatial autocorrelation are accounted for in the modeling process. Based on CAR models, approximately 51% of crash frequencies across contiguous freeway segments are spatially auto-correlated. The incident rate ratios revealed that wider shoulder and weaving segments decreased crash frequency by factors of 0.84 and 0.75 respectively. The marginal impacts graphs showed that an increase in longitudinal space for segments with two lanes decreased crash frequency. However, an increase of facility width above three lanes results in more crashes, which indicates an increase in traffic flows and driving behavior leading to crashes. These results call an important step of analyzing contagious freeway segments simultaneously to account for the existence of spatial autocorrelation.
基金The research was supported by the Innovation Project of State Key Laboratory of Resources and Environment Information System(Grant No.088RA90BYA)the Ministry of Science and Technology of China(Grant No.2016YFC1201301).
文摘Background:The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague,which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus,but also to reveal its cluster rule.This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014,in order to predict plague outbreaks.Methods:Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils.Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods.The quantity of M.unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention.Results:The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index.High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high.In terms of time series,the area of the epidemic focus gradually increased from 2005 to 2007,declined rapidly in 2008 and 2009,and then decreased slowly and began trending towards stability from 2009 to 2014.For the spatial change,the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007,and then moved from north to south in 2007 and 2008.Conclusions:The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation.The diversity of temporary and spatial distribution is mainly affected by seasonal variation,the human activity and natural factors.
基金supported by the National Key R&D Program of China(No.2022YFD2401301)。
文摘Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations and spatial dependence have been rarely taken into account in the modeling processes.Recently,Joint Species Distribution Models(JSDMs)offer the opportunity to consider both environmental factors and interspecific relationships as well as the role of spatial structures.This study uses the HMSC(Hierarchical Modelling of Species Communities)framework to model the multispecies distribution of a marine fish assemblage,in which spatial associations and spatial dependence is deliberately accounted for.Three HMSC models were implemented with different structures of random effects to address the existence of spatial associations and spatial dependence,and the predictive performances at different levels of sample sizes were analyzed in the assessment.The results showed that the models with random effects could account for a larger proportion of explainable variance(32.8%),and particularly the spatial random effect model provided the best predictive performances(R_(mean)^(2)=0.31),indicating that spatial random effects could substantially influence the results of the joint species distribution.Increasing sample size had a strong effect(R_(mean)^(2)=0.24-0.31)on the predictive accuracy of the spatially-structured model than on the other models,suggesting that optimal model selection should be dependent on sample size.This study highlights the importance of incorporating spatial random effects for JSDM predictions and suggests that the choice of model structures should consider the data quality across species.
基金China Institute of Water Resources and HydropowerResearch(IWHR)Innovative Team for Theoreticaland Technological Research on EcologicalLandscape Construction of Urban and Rural WaterSystems,Grant/Award Number:WE0145B042021。
文摘Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.
基金Foundation： National Social Science Foundation of China, No.10ZD＆M030 Non-profit Industry Financial Program of Ministry of Land and Resources of China, No.200811033+2 种基金 A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China, No.40801063 No.40971104
文摘Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.
文摘This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.
基金Key Program of Special Science Research in Environmental Protection Public Welfare Industry,No.201009017Research Plan of LREIS,No.088RA900KAKey Project for the Strategic Plan in IGSNRR,CAS,No.2012ZD010
文摘The capacity of livestock breeding in China has increased rapidly since 1949,and the total output of meat,poultry and eggs maintains the world's top first in recent 20 years.Livestock emissions and pollution is closely associated with its population and spatial distribution.This paper aims to investigate the spatial patterns of livestock and poultry breeding in China.Using statistical yearbook and agricultural survey in 2007,the county-level populations of livestock and poultry are estimated as equivalent standardized pig index(ESP),per cultivated land pig index(PCLP)and per capita pig index(PCP).With the help of spatial data analysis(ESDA)tools in Geoda and ArcGIS software,especially the Moran's I and LISA statistics,the nationwide global and local clustering trends of the three indicators are examined respectively.The Moran's I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally.However,PCLP is clustering locally but not significant globally.Furthermore,the thematic map series(TMS)and related gravity centers curve(GCC)are introduced to explore the spatial patterns of livestock and poultry in China.The indicators are classified into 16 levels,and the GCCs for the three indicators from level 1 to 16 are discussed in detail.For districting purpose,each interval between gravity centers of near levels for all the three indicators is calculated,and the districting types of each indicator are obtained by merging adjacent levels.The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding.As a result,the China's livestock and poultry breeding would be classified into eight types:extremely sparse region,sparse region,relatively sparse region,normally sparse region,normal region,relatively concentrated region,concentrated region and highly concentrated region.It is also found that there exists a clear demarcation line between the concentrated and the sparse regions.The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner,Inner Mongolia Autonomous Region to the west coast of Dongfang County,Hainan Province.
基金Under the auspices of National Natural Science Foundation of China(No.41201384)Hunan Provincial Natural Science Foundation(No.12JJ3034)State Key Laboratory of Resources and Environmental Information System,Nieying Talent Program of Central South University(No.7601110176)
文摘In order to evaluate whether or not the county units′ economy in the Changsha-Zhuzhou-Xiangtan(Chang-Zhu-Tan) Urban Agglomeration was growing as expected, this study analyzed the spatial economy pattern at county-level by using the Exploratory Spatial Data Analysis(ESDA) method. In this process, the global Moran′s I and local Getis-Ord G*i indexes were employed to analyze indicators including per capita GDP and three industrials(i.e. primary, secondary and tertiary industry) from 2000 to 2010. The results show that: 1) the county units′ economy in the Chang-Zhu-Tan Urban Agglomeration has exhibited a strong spatial autocorrelation and an accelerated integration trend since 2008(Moran′ s I increased from 0.26 to 0.56); 2) there is a significant difference in economy development between the northern and southern county units in the Chang-Zhu-Tan Urban Agglomeration: the hotspot zone with high economic level was formed among the northern county units whereas the coldspot zone with low economic level was located in the southern areas. This difference was caused primarily by the increasingly prominent economic radiation effect of Changsha ′upheaval′; 3) town density, secondary industry, and the integration policy are the major contributors driving the evolution of the spatial economy pattern in the Chang-Zhu-Tan Urban Agglomeration.
