共查询到20条相似文献，搜索用时 62 毫秒
1.
针对建立地理加权回归(GWR)模型时,无法直接应用普通线性回归(OLR)常用的特征变量选择方法,且计算过程较复杂的问题,该文基于贪心算法原理,通过引入Akaike信息法则,设计了适用于GWR的特征变量选择方法:逐个引入或删除特征变量,判断该变量对模型置信水平影响程度,根据评价准则决定该变量的取舍,最终实现模型外没有关系强的变量、模型内没有关系弱的变量。实验结果表明,比较基于OLR的逐步回归、向前引入法和向后删除法3种方法选择变量建立模型,向前引入法优于向后剔除法,两者都优于基于OLR的逐步回归法,更适用于GWR分析。 相似文献
2.
一种协同时空地理加权回归PM2.5浓度估算方法 总被引：2，自引：1，他引：1
针对PM2.5浓度估算中时空特征考虑不足和样本量较少的问题,该文将协同训练和时空地理加权回归相结合,提出了协同时空地理加权回归。采用两个不同参数的时空地理加权回归模型作为回归器,利用一个回归器训练另一个回归器的未标注样本,选择最优结果作为标注样本加入标注样本,通过不断学习扩大标注样本量提升模型的回归性能。以京津冀地区2015年3-7月的PM2.5浓度数据为实验数据,利用气溶胶光学厚度产品、温度、风速和相对湿度进行建模,采用不同核函数的时空地理加权回归作为对比方法进行实验。结果显示,协同时空地理加权回归性能比基于Gauss核函数时空地理加权回归提升了10%,比基于bi-square核函数时空地理加权回归提升了6.25%,证明该文方法能够提升时空样本数量不足时的PM2.5浓度估算精度。 相似文献
3.
市域尺度货物运输碳排放时空变化及因素分析 总被引：1，自引：0，他引：1
针对货物运输导致碳排放成为温室气体主要来源之一的问题,该文综合货物运输车辆的微观温室气体排放及时空变化,从市域尺度分析货物运输碳排放的时空变化规律。利用微观排放模型计算了2000年、2005年、2010年和2015年全国286个城市货物运输碳排放的空间分布及其变化,并应用地理加权回归模型探究城市化不同层面因素对碳排放时空分布变化的影响。结果表明:货物运输碳排放具有显著的空间集聚特征,且高排放地区的集聚规律更加显著;地理加权回归模型精度明显高于普通线性回归模型,经济变量、人口变量、货运强度变量与货物运输碳排放存在显著正相关关系。该研究可为中国各市级区域制订节能减排政策提供量化的科学依据。 相似文献
4.
基于本体语义研究地理信息服务发现问题.将加权语义距离和Wu-Palmer法相结合并进行改进计算本体概念语义相似度.引入服务接口依赖关系,提出支持接口多态性的本体语义地理信息服务输入输出匹配方法.最后开发实验原型对基于本体的地理信息服务匹配方法进行实验与分析. 相似文献
5.
传统基于遥感的气温反演方法往往使用全局模型,从而忽略了气温分布及其时空影响异质性,特别是在较大区域尺度的研究中存在不足。针对长江经济带区域,引入时空地理加权神经网络模型,建立一种高精度的气温估计方法。通过在广义回归网络模型中建立局部模型来顾及时空异质性的影响,融合遥感数据、同化数据、站点数据,获取面域分布的近地表气温信息。采用基于站点的十折交叉验证方法对模型性能进行评估,结果表明,时空地理加权神经网络有效提高了气温估计的精度（均方根误差为1.899℃,平均绝对误差（mean absolute error,MAE）为1.310℃,相关系数为0.976）,与多元线性回归和传统的全局神经网络方法相比,MAE值分别降低了1.112℃和0.378℃。气温空间分布制图结果显示,该方法结果能很好地反映长江经济带气温空间上的差异和不同季节的特征信息,具有实际应用价值。 相似文献
6.
针对传统地理加权回归方法无法解决时空非平稳性的问题,该文提出了一种路网距离约束的时空地理加权回归方法。引入时间特性,进一步把握了不同因子在时空维度影响的分异性;以路网距离度量约束,提高模型解释力。以北京市城6区1980—2015年的1 632个住宅小区特征价格数据为例,通过与直线距离约束的常规地理加权回归方法等进行比较,采用各模型的AIC与拟合优度等指标对模型置信水平高低进行评价。实验结果表明,路网距离约束的地理加权回归模型不仅能够提高模型的拟合精度,还能更好地揭示房价在时间与空间方面的变化规律。 相似文献
7.
基于反距离夹角加权算法的地理信息空间内插方法 总被引：1，自引：0，他引：1
基于加权平均理论,在权因子变量中引入距离和夹角两个量来定权,得出反距离夹角加权算法。将反距离夹角加权算法应用在地理信息空间内插中,并与常用的几何内插方法算术平均值法、邻近值法、反距离加权法进行比较分析后得出,当已知样本点只有一个时,该算法相当于最邻近值法;当只有两个已知样本点,或者多于两个已知样本点并且点位在全圆方位上分布均匀时,夹角权相等,该算法相当于反距离加权法;多于两个已知样本点,但已知样本点密度和方位分布不均时,反距离夹角加权法能减弱甚至消除已知样本点在内插点全圆方位上分布不均及疏密不匀对内插值的影响,保证内插值接近近点值。所提出的反距离夹角加权算法将广泛应用在建立DEM及图像处理等地理空间信息数据内插中。 相似文献
8.
针对时空地理加权回归模型(GTWR)进行预测时,输入变量较多导致计算复杂度高,而输入变量较少引起预测精度降低这一问题,提出了一种基于主成分分析的时空地理加权回归方法(PCA-GTWR)。该方法采用非线性主成分分析方法,先对影响PM2.5浓度的若干相关变量降维处理得到几个综合指标,并将其作为GTWR模型的输入变量进行预测。为验证该方法的有效性,采用北京市2014-04—2017-03的PM2.5数据,利用Pearson相关系数法选取与PM2.5浓度具有较高相关性的影响因素作为常规的GTWR模型的输入变量,在变量个数相同的前提下,与本文方法进行对比。结果表明应用非线性主成分分析方法对相关变量进行预处理后,有效地解决了变量之间的共线性,保留了原始影响因素主要信息,提高了运算效率,且该方法的MAE、RMSE、AIC均低于常规的GTWR模型,拟合优度GF最高达到88.11%。 相似文献
9.
针对京津冀地区多年来重工业较多、结构性污染突出等问题,该文充分利用多期扬尘地表和工业企业污染源、交通网络、地理国情地表覆盖数据、气象和地形数据,结合MODIS AOD产品和环境监测数据,采用主成分分析和最佳子集回归方法优选预测变量,构建估算PM2.5和PM10浓度的地理加权回归模型,实现京津冀地区2013、2015和2017年PM2.5/PM10年均浓度空间分布模拟制图,分析PM2.5/PM10年均浓度时空分布。实验结果表明,PM2.5和PM10浓度估算模型的决定系数R2分别为0.76和0.86,平均相对预测误差分别为10.87%和13.54%。 相似文献
10.
一种局部多项式时空地理加权回归方法 总被引：1，自引：0，他引：1
基于加权最小二乘估计的时空地理加权回归方法,在随机项方差相同且最小的假设条件下估计回归参数和拟合值,由于没有考虑时空分析中异方差影响而导致估计结果存在一定偏差。局部多项式估计是一种消除异方差影响的非参数估计方法。本文在局部多项式估计原理基础上,提出了局部多项式时空地理加权回归方法。它是采用三元一阶泰勒级数展开式重构时空回归系数和自变量矩阵,进而建立满足高斯-马尔可夫独立同分布假定要求的新模型,利用新模型回归系数估计值、拟合值以及新模型与原模型的关系,可得到原模型回归系数估计值和拟合值。本文采用模拟数据和真实数据进行试验,以GTWR与局部线性地理加权回归作为对比方法,从方法适用性、整体估计效果、回归系数估计偏差和拟合优度、整体估计偏差等方面分析了LPGTWR方法性能,有效证明了LPGTWR方法能消除异方差影响提升估计精度。 相似文献
11.
Multicollinearity and correlation among local regression coefficients in geographically weighted regression 总被引：5，自引：3，他引：5
Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.This study was supported by grant number 1 R1 CA95982-01, Geographic-Based Research in Cancer Control and Epidermiology, from the National Cancer Institute. The author thank the anonymous reviewers and the editor for their helpful comments. 相似文献
12.
Semiparametric Regreswsion and Model Refining 总被引：11，自引：0，他引：11
SUN Haiyan WU Yun 《地球空间信息科学学报》2002,5(4):10-13
This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presented.Finally,a simulated adjustment problem is constructed to explain the method given in this paper,The results from the semiparametric model and G-M model are compared.The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method. 相似文献
13.
This paper presents a semiparametric adjustment method suitable for general cases. Assuming that the regularizer matrix is positive definite, the calculation method is discussed and the corresponding formulae are presented. Finally, a simulated adjustment problem is constructed to explain the method given in this paper. The results from the semiparametric model and G-M model are compared. The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method. 相似文献
14.
This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters. 相似文献
15.
Local regression methods such as geographically weighted regression (GWR) can provide specific information about individual locations (or places) in spatial analysis that is useful for mapping nonstationary covariate relationships. However, the distance-based weighting schemes used in GWR are only adaptable for spatial objects that are point or area features. In particular, spatial object-pairs pose a challenge for local analysis because they have a linear dimensionality rather than a point dimensionality. This paper proposes using an alternative local regression model – quantile regression (QR) – for investigating the stationarity of regression parameters with respect to these linear features as well as facilitating the visualization of the results. An empirical example of a gravity model analysis of trade patterns within Europe is used to illustrate the utility of the proposed method. 相似文献
16.
17.
一元线性回归是应用最为广泛的参数估计方法之一。文中提出一元线性回归的自变量在等差级数的基础上进行双向黄金分割,提高两端点观测值的多余观测分量,缩小观测值之间多余观测分量的差异,在不增加观测值数量和不改变观测值精度的前提下,提高稳健估计方法消除或减弱粗差的能力。 相似文献
18.
Urban growth pattern modeling using logistic regression 总被引：1，自引：0，他引：1
Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made.This paper tends to apply logistic regression to model urban growth in the Jiayu county of Hubei province,China.It is applied in a GIS environment to calculate variables and,then,in SPSS to discover the relationships between urban growth and the driving forces.The relative operating characteristic(ROC) shows the modeling accuracy with the curve 0.891 with standard er-ror 0.001.A probability map is generated finally to predict where urban growth will occur as a result of the computation.The result shows the model simulates urban growth well in the county scale. 相似文献
19.
Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process,we built a regularized regression model using Truncated Singular Value Decomposition(TSVD).An earth-rock dam in China is presented and discussed as an example.The analysis consists of three steps:multicollinearity detection,regularization pa-rameter selection,and crack opening modeling and forecasting.Generalized Cross-Validation(GCV) function and L-curve criterion are both adopted in the regularization parameter selection.Partial Least-Squares Regression(PLSR) and stepwise regression are also included for comparison.The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models.However,no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem.Both fitting accuracy and coefficients’ reasonability should be considered when evaluating the model reliability. 相似文献
20.
Under the assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formulating the multipath estimation as a regression problem, we construct a nonlinear continuous model for estimating multipath error based on well-known sparse kernel regression, for example, support vector regression. We will empirically show that the proposed method achieves state-of-the-art performance on code multipath mitigation with 79 % reduction on average in terms of standard deviation of multipath error. Furthermore, by simulation, we will also show that the method is robust to other coexisting signals of phenomena, such as seismic signals. 相似文献