In this analysis, natural systems are posed to subsystemize in a manner facilitating both structured information/energy sharing and an entropy maximization process projecting a three-dimensional, spatial outcome. Nume...In this analysis, natural systems are posed to subsystemize in a manner facilitating both structured information/energy sharing and an entropy maximization process projecting a three-dimensional, spatial outcome. Numerical simulations were first carried out to determine whether n × n input-output matrices could, once entropy-maximized, project a three-dimensional Euclidean metric. Only 4 × 4 matrices could;a small proportion passed the test. Larger proportions passed when grouped random patterns on and within two- and three-dimensional forms were tested. The pattern of structural zonation within the earth was then tested in analogous fashion using spatial autocorrelation measures, and for three time periods: current, 95 million years b.p. and 200 million years b.p. All expected results were obtained;not only do the geometries of zonation project a three-dimensional structure as anticipated, but also do secondary statistical measures reveal levels of equilibrium among the zones in all three cases that are nearly total, distinguishing them from simulations that do not incorporate a varying-surface zone-width element.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
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
Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tre...Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.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
Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiw...Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.展开更多
Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of...Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.展开更多
Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS a...Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.展开更多
Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such ho...Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.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
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
文摘In this analysis, natural systems are posed to subsystemize in a manner facilitating both structured information/energy sharing and an entropy maximization process projecting a three-dimensional, spatial outcome. Numerical simulations were first carried out to determine whether n × n input-output matrices could, once entropy-maximized, project a three-dimensional Euclidean metric. Only 4 × 4 matrices could;a small proportion passed the test. Larger proportions passed when grouped random patterns on and within two- and three-dimensional forms were tested. The pattern of structural zonation within the earth was then tested in analogous fashion using spatial autocorrelation measures, and for three time periods: current, 95 million years b.p. and 200 million years b.p. All expected results were obtained;not only do the geometries of zonation project a three-dimensional structure as anticipated, but also do secondary statistical measures reveal levels of equilibrium among the zones in all three cases that are nearly total, distinguishing them from simulations that do not incorporate a varying-surface zone-width element.
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.
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Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.
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Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.
文摘Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.
文摘Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.
文摘Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.
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.
