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1.
A Conditional Dependence Adjusted Weights of Evidence Model   总被引：3，自引：0，他引：3  
One of the key assumptions in weights of evidence (WE) modelling is that the predictor patterns have to be conditionally independent. When this assumption is violated, WE posterior probability estimates are likely to be biased upwards. In this paper, a formal expression for the bias of the contrasts will be derived. It will be shown that this bias has an intuitive and convenient interpretation. A modified WE model will then be developed, where the bias is corrected using the correlation structure of the predictor patterns. The new model is termed the conditional dependence adjusted weights of evidence (CDAWE) model. It will be demonstrated via a simulation study that the CDAWE model significantly outperforms the existing WE model when conditional independence is violated, and it is on par with logistic regression, which does not assume conditional independence. Furthermore, it will be argued that, in the presence of conditional dependence between predictor patterns, weights variance estimates from WE are likely to understate the true level of uncertainty. It will be argued that weights variance estimates from CDAWE, which are also bias-corrected, can properly address this issue.  相似文献   

2.
In binary spatial pattern recognition, there are many situations where the researcher could be interested in a number of dependent variables that are themselves correlated. For instance, different types of crime often coexist in the same area, or different species could share the same habitat. In cases like these, a natural correlation exists amongst the dependent variables of interest and is informative for spatial probability mapping. Weights of evidence (WE) modelling is a popular Bayesian probability method for binary pattern recognition, but it only deals with one single dependent variable at a time and ignores the correlation between the dependent variables. In this article, a multiple dependent variable weights of evidence (MDVWE) model will be developed. It will be shown that the new MDVWE model can be viewed as a restricted version of the conditional dependence-adjusted weights of evidence (CDAWE) model of Deng (Nat Resour Res 18(4):249–258, 2009). The MDVWE model is easy to program and implement. By means of a simulation study, it will be shown that the MDVWE model outperforms the traditional WE model both in terms of in-sample fit and out-of-sample prediction accuracy.  相似文献   

3.
用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。  相似文献   

4.
Improving solar radiation models is critical for supporting the increase in solar energy usage and modeling ecosystem dynamics. However, coarse spatial resolutions of solar radiation models overlook the impacts resulting from spatial variability of clouds at meso- and micro-scales. To address this problem, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud climatology developed by the National Severe Storms Laboratory was used to relate cloudiness to surface solar radiation observations. We developed a linear regression model between the surface solar radiation and MODIS cloud climatology and used the model to estimate average radiation across Oklahoma. Furthermore, the study compared the average error and coefficient of determination to measured ground radiation. Error analysis of the regression model showed that the differences between observed radiation and estimated radiation were spatially autocorrelated for the Aqua MODIS satellite scan. This suggests cloudiness alone is not sufficient to predict surface solar radiation. This study found that simple cloud datasets alone can account for approximately 50% of the variation in observed solar radiation at 250-m spatial resolution, but additional datasets such as optical depth, elevation, and slope are needed to accurately explain spatial distributions of incoming shortwave radiation.  相似文献   

5.
基于空间滤波方法的中国省际人口迁移驱动因素   总被引：9，自引：5，他引：4  
人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波（ESF）负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明：① 省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。② 省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。③ 网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素：区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的“拉力”因素。④ 与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。  相似文献   

6.
Most forest fires in Korea are spatially concentrated in certain areas and are highly related to human activities. These site-specific characteristics of forest fires are analyzed by spatial regression analysis using the R-module generalized linear mixed model (GLMM), which can consider spatial autocorrelation. We examined the quantitative effect of topology, human accessibility, and forest cover without and with spatial autocorrelation. Under the assumption that slope, elevation, aspect, population density, distance from road, and forest cover are related to forest fire occurrence, the explanatory variables of each of these factors were prepared using a Geographic Information System-based process. First, we tried to test the influence of fixed effects on the occurrence of forest fires using a generalized linear model (GLM) with Poisson distribution. In addition, the overdispersion of the response data was also detected, and variogram analysis was performed using the standardized residuals of GLM. Second, GLMM was applied to consider the obvious residual autocorrelation structure. The fitted models were validated and compared using the multiple correlation and root mean square error (RMSE). Results showed that slope, elevation, aspect index, population density, and distance from road were significant factors capable of explaining the forest fire occurrence. Positive spatial autocorrelation was estimated up to a distance of 32 km. The kriging predictions based on GLMM were smoother than those of the GLM. Finally, a forest fire occurrence map was prepared using the results from both models. The fire risk decreases with increasing distance to areas with high population densities, and increasing elevation showed a suppressing effect on fire occurrence. Both variables are in accordance with the significance tests.  相似文献   

7.
Wang  Xueqin  Liu  Shenghe  Qi  Wei 《地理学报(英文版)》2020,30(7):1060-1082
As a special outcome of urbanization, mega-towns not only play an important role in the process of socio-economic development, but also are important contributors to urbanization. Based on a spatial database of mega-towns in China, this paper explores the spatial distribution features and growth mechanisms of China's 238 mega-towns using the nearest neighbour distance method, kernel density estimation, regression analysis, global autocorrelation, local autocorrelation and other spatial analysis methods. Results of spatial distribution features show that:(1) on the national scale, the existing 238 mega-towns mainly gathered in the southeast coastal areas of China; they formed two spatial core agglomerations, several secondary ones and a southeast coastal agglomeration belt;(2) on the regional scale, each economic region's index was less than 1, indicating that mega-towns in each region tended to be spatially agglomerated due to the close relationship with regional development level and their number;(3) on the provincial scale, 68% of provincial-level units in China tended to be a spatial agglomeration of mega-towns; only one province had a random distribution; the number of mega-towns in those evenly-distributed provinces was generally small. The growth of mega-towns was determined by a combination of various natural and humanistic factors, including topography, location, economy, population, traffic, and national policy. This paper chose digital elevation model(DEM), location advantage, economic density, population density, and highway density distribution as corresponding indicators as quantitative factors. By combining their local autocorrelation analysis, these factors all showed certain influence on the spatial growth of mega-towns and together scheduled it. In the future, provinces and cities should make full use of the mega-town functions to promote their socioeconomic development, especially the central and western regions in China.  相似文献   

8.
犯罪地理国际研究进展   总被引：3，自引：2，他引：1  
严小兵  焦华富 《地理科学进展》2012,31(10):1390-1398
犯罪地理学是犯罪学和地理学研究的交叉学科。近30 年来, 随着GIS 技术的发展, 国外犯罪地理学在研究内容和研究方法上发生了许多转变。从5 个方面简述了国外犯罪地理研究的发展:①解释理论, 简述了不同理论的不足及发展;②犯罪活动空间分布, 全局关系和局域关系上犯罪活动空间分布的判别和可视化;③计量模型, 空间依赖和空间异质效应的计量;④虚拟犯罪, 犯罪活动预测的地理计算;⑤研究尺度, 从大尺度到小尺度的转变。最后总结了地理学对犯罪学研究的贡献, 并提出了国外的发展对国内研究的借鉴。  相似文献   

9.
土地利用/ 土地覆被变化(LUCC) 是当前研究全球变化的重要内容, 而区域土地利用 格局模拟是LUCC 研究的核心内容之一。以张家界市永定区为研究单元, 根据由2005 年土地 利用现状图和数字高程模型数据源得到的土地利用、地形、河流以及道路等空间数据, 对区 域土地利用类型空间格局的空间自相关性特征进行了建模研究, 并通过在传统Logistic 模型 中引入描述空间自相关性的成份, 实现了能够考虑自相关性因素的回归分析模型 (AutoLogistic 模型), 同时应用该模型对区域土地利用格局进行了模拟和分析。结果显示, 通 过与没有考虑空间自相关性的回归模型(传统Logistic 模型) 相比较, 该模型显示了更好的拟 合优度和更高的拟合准确率(耕地、林地、建设用地及未利用地的ROC 值分别从0.851、 0.913、0.877 和0.852 提高到0.893、0.940、0.907 和0.863)。研究结果说明了基于 AutoLogistic 方法的土地利用格局的相关性建模在一定意义上是合理的。同时研究结果也可以 为永定区及其相似地区的土地利用规划决策提供更为科学的依据。  相似文献   

10.
Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R2, lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.  相似文献   

11.
Spatial autocorrelation in marine birds   总被引：1，自引：0，他引：1  
All marine organisms exhibit some degree of spatial autocorrelation, which is the tendency for high (or low) densities to occur in proximity, rather than at random in the ocean. Autocorrelation occurs at scales ranging from the length of the organism to thousands of kilometres. Autocorrelation results from a wide variety of mechanisms, many of which act at characteristic scales. Consequently, some insight into causal mechanisms can be obtained from exploratory analysis of the scale and intensity of autocorrelation of abundance or behaviour, and the scale and intensity (coherence) of cross-correlation with environmental variables such as water temperature or prey abundance. This paper uses seabird counts along extended transects to illustrate standard measures of autocorrelation and cross-correlation. A brief discussion of exploratory and confirmatory analysis of autocorrelated data on marine birds follows.  相似文献   

12.
Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors, but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model (Autologistic model), and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves (AUC) was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.  相似文献   

13.
Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R2, Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.  相似文献   

14.
Abstract

In a uniequational hedonic model, the main source of spatial dependence is found in the explained variable, since the price of a house mainly depends on the housing prices in the neighborhood (although this can also be due to other factors, such as missing covariates and the model of choice). Dependence is one of the primary causes of spatial autocorrelation in disturbances. However, such disturbances may also be spatially correlated with the disturbances of other equations; in this case, they can be considered coregionalized. This paper presents a multi-equational hedonic regression model with coregionalized disturbances and heterotopic data. The model comprises two equations. The first explains housing prices using data from a sample, while the second explains an auxiliary variable, quality of the area, obtained from a different sample. The model is then applied practically to predict housing prices. [Key words: Cokriging, housing prices, geostatistics, multi-equational hedonic model, coregionalized.]  相似文献   

15.
Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.  相似文献   

16.
Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon.  相似文献   

17.
The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.  相似文献   

18.
近25年来塔里木河流域区域经济空间关联及演化特征分析   总被引：2，自引：1，他引：1  
以塔里木河流域为研究对象,借助Arcgis8.5及Geoda等软件平台,引入空间自相关模型,对塔里木河流域42县(市)1980-2005年近25年的区域经济空间关联类型、动态演化特征及动力机制进行研究,结果表明:(1)25年来塔里木河流域经济全局空间自相关系数增长了近1.7倍,总体呈现出空间正相关特征,且为波动上升的动态过程,在5%的显著性水平下,经济在空间上表现为显著的正相关性.(2)局部区域空间自相关特征显著,区域经济显著负相关的区域逐渐消失,正相关显著区域日趋加强,最终演化为低一低及高一高两种类型区在东北和西南集中分布格局,且差距不断增大,各类型区域面积消长在起始,震荡和稳定三个阶段呈现出不同特点.(3)区域经济增长的近邻效应不断增强,2004年低-低和高-高两种类型区域面积是1980的1.66倍,但经济空间集聚效应主要是由于低-低区域面积的迅速增加所致.(4)工业化初期阶段,矿产资源条件和区位因素及交通条件两大因素是促进区域经济空间集聚与演化的动力.文章最后探讨了流域经济空间关联特点及演化特征对区域经济集聚扩散过程、区域政策选择、空间开发模式及区域协调发展等问题的影响.  相似文献   

19.
In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area.  相似文献   

20.
ABSTRACT

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data.  相似文献   

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