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1.
中国省域犯罪率影响因素的空间非平稳性分析 总被引：4，自引：2，他引：2
收入差距和流动人口是影响犯罪率的两个重要因素, 以往研究基于OLS模型, 在假设地域空间为均质的前提下分析其对犯罪率的影响, 但现实世界的空间单元往往难以满足“均质”的假设, 多数表现为“空间异质”。以OLS计量空间异质会造成计量结果出现偏差, 同时无法了解不同空间单元的不同影响。而地理加权回归模型通过将空间结构嵌入线性回归模型中, 很好的解决了空间异质的计量问题。利用地理加权回归模型研究2008 年中国大陆省域单元犯罪率的影响因素, 结果表明:① 犯罪率的影响因素表现出空间非平稳性, 流动人口与犯罪率显著相关, 但各个省份相关程度并不相同, 影响关系随空间位置变化而变化;② 地理加权回归模型的计量精度和拟合度比OLS模型有大幅提高 相似文献
2.
Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R2 for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected. 相似文献
3.
Bo Huang Bo Wu Michael Barry 《International journal of geographical information science》2013,27(3):383-401
By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data. 相似文献
4.
美国俄亥俄州土壤有机碳密度空间分布(英文) 总被引：2，自引：1，他引：1
Historical database of National Soil Survey Center containing 1424 geo-referenced soil profiles was used in this study for estimating the organic carbon(SOC) for the soils of Ohio,USA.Specific objective of the study was to estimate the spatial distribution of SOC density(C stock per unit area) to 1.0-m depth for soils of Ohio using geographically weighted regression(GWR),and compare the results with that obtained from multiple linear regression(MLR).About 80% of the analytical data were used for calibration and 20% for validation.A total of 20 variables including terrain attributes,climate data,bedrock geology,and land use data were used for mapping the SOC density.Results showed that the GWR provided better estimations with the lowest(3.81 kg m 2) root mean square error(RMSE) than MLR approach.Total estimated SOC pool for soils in Ohio ranged from 727 to 742 Tg.This study demonstrates that,the local spatial statistical technique,the GWR can perform better in capturing the spatial distribution of SOC across the study region as compared to other global spatial statistical techniques such as MLR.Thus,GWR enhances the accuracy for mapping SOC density. 相似文献
5.
Binbin Lu Chris Brunsdon Martin Charlton Paul Harris 《International journal of geographical information science》2017,31(5):982-998
Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research. 相似文献
6.
Binbin Lu Martin Charlton Paul Harris A. Stewart Fotheringham 《International journal of geographical information science》2014,28(4):660-681
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set. 相似文献
7.
基于探索性空间数据分析技术(ESDA)划分出京津冀都市地区的中心区域和外围区域,并在传统经济收敛模型基础上,运用空间俱乐部收敛模型和局部空间回归模型对京津冀都市地区经济收敛情况进行研究。结果表明,首先,京津冀都市地区已形成了以北京、天津和唐山为核心的中心区域和以张家口市、保定市为核心的环绕京津的外围区域,京津冀都市地区整体上存在微弱的经济收敛。其次,京津冀都市地区中心地区由于经济发展水平较高,空间外溢效应较大,加之中心地区接受知识、技术扩散的能力较强,存在经济收敛,并且收敛速度较快,而外围区域则不存在经济收敛。再次,中心地区和外围地区内部存在经济收敛系数结构的不稳定性。 相似文献
8.
William G. Moseley 《The Professional geographer》2013,65(4):593-595
In the past decade, the women's employment rate has increased in Gulf Cooperation Council (GCC) states as a result of improved female educational attainment and the expansion of the local market economy. A significant gender gap in labor force participation (LFP) rates has emerged, however, compared to other countries in the Middle East and North Africa. The main aim of this article is to model the spatial variations of female LFP rates across the northeastern part of Oman. A geographically weighted regression (GWR) technique, within the geographic information system platform, is used to address how the relationships between Omani female LFP rates and a set of explanatory variables vary across Omani subnational boundaries. GWR is a powerful approach that can facilitate the identification of areas with lower or higher female LFP rates and help in better understanding the predictors that are associated with women's employment in specific locations. In so doing, this work attempts to fill the gap in the geographic literature regarding the modeling of local spatial patterns of female employment in developing countries. The results show that the female LFP rate is significantly associated with different spatial measures and particularly the geographic distribution of female education. Interestingly, the percentage of female jobs in the public sector is found to have a substantial negative effect on female LFP rates, especially in urban areas. This can be attributed to the propensity of Omani women to work in governmental jobs and reduce their participation in private and other business sectors. The findings of this research analysis not only offer a more nuanced examination of female LFP rate patterns but also provide empirical evidence in support of locally tailored policies pertaining to the female labor force, which might help in increasing women's participation trends in the local economy across local communities. 相似文献
9.
The main aim of this article is to analyze the relationships between the spatial patterns of residential burglaries and the socioeconomic characteristics of neighborhoods in London, Ontario. Relative risk ratios are applied as a measure of the intensity of residential burglary. The variation in the risks of burglary is modeled as a function of contextual neighborhood variables. Following a conventional (global) regression analysis, spatial variations in the relationships are examined using geographically weighted regression (GWR). The GWR results show that there are significant local variations in the relationships between the risk of residential burglary victimization and the average value of dwellings and percentage of the population in multifamily housing. The results are discussed in the context of four hypotheses, which may explain geographical variations in residential burglary. The practical implication of the GWR analysis is that different crime prevention policies should be implemented in different neighborhoods of the city. 相似文献
10.
基于空间集聚的中国入境旅游区域经济效应分析 总被引：2，自引：0，他引：2
运用残差空间自回归模型、地理权重回归模型及基尼系数等空间分析方法,探讨了2001-2010年中国地市入境旅游的区域经济效应及其空间差异变化。在提升区域经济水平上,中国地市入境旅游对区域经济总体上具不断增强的促进效应,且存在持续而显著的空间集聚作用;局部上具显著促进效应的地市集中分布在东部沿海地带及其邻近地区,且表现为促进效应不断增强、集聚范围不断扩大的态势。在调整区域经济差异上,入境旅游具扩大区域经济差异的总体效应,但局部上集聚范围的扩大表明其总体上的扩大效应在逐渐减弱;基于空间集中性的分析也表明,不仅在总体上也在各地带的内部区域上,这种扩大效应存在且具不断减弱的趋势。研究表明,地市入境旅游区域经济效应的发挥主要体现了市场经济运行规律的作用,而其变化趋势也反映了政府宏观调整与区域旅游发展政策及规划的战略取向。 相似文献
11.
基于空间集聚的中国入境旅游区域经济效应分析简 总被引：1，自引：0，他引：1
运用残差空间自回归模型、地理权重回归模型及基尼系数等空间分析方法,探讨了2001-2010年中国地市入境旅游的区域经济效应及其空间差异变化。在提升区域经济水平上,中国地市入境旅游对区域经济总体上具不断增强的促进效应,且存在持续而显著的空间集聚作用;局部上具显著促进效应的地市集中分布在东部沿海地带及其邻近地区,且表现为促进效应不断增强、集聚范围不断扩大的态势。在调整区域经济差异上,入境旅游具扩大区域经济差异的总体效应,但局部上集聚范围的扩大表明其总体上的扩大效应在逐渐减弱;基于空间集中性的分析也表明,不仅在总体上也在各地带的内部区域上,这种扩大效应存在且具不断减弱的趋势。研究表明,地市入境旅游区域经济效应的发挥主要体现了市场经济运行规律的作用,而其变化趋势也反映了政府宏观调整与区域旅游发展政策及规划的战略取向。 相似文献
12.
都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比 总被引：2，自引：2，他引：0
以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。 相似文献
13.
基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression, GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression, PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression, M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression, MLR)为基准比较3种GWR模型的精度。研究表明：(1)安徽省土壤pH具有空间异质性,且集聚特征明显。(2) 3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(Radj2)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的Radj2<... 相似文献
14.
Zhenhong Du Zhongyi Wang Sensen Wu Feng Zhang Renyi Liu 《International journal of geographical information science》2020,34(7):1353-1377
ABSTRACT Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes. 相似文献
15.
Yelena Ogneva-Himmelberger Hamil Pearsall Rahul Rakshit 《Applied geography (Sevenoaks, England)》2009,29(4):478-487
Several studies indicate that there is a positive relationship between green vegetation land cover and wealthy socio-economic conditions in urban areas. The purpose of this research is to test for and explore spatial variation in the relationship between socio-economic and green vegetation land cover across urban, suburban, and rural areas, using geographically weighted regression (GWR). The analysis was conducted at the census block group level for Massachusetts, using Census 2000 data and impervious surface data at 1-m resolution. To explore regional variations in the relationship, four scenarios were generated by regressing each of the following socio-economic variables – median household income, percentage of poverty, percentage of minority population, and median home value – against two environmental variables – percent of impervious surface and population density. GWR results show that there is a considerable spatial variation in the character and the strength of the relationship for each model. There are two main conclusions in this study. First, the impervious surface is generally a strong predictor of the level of wealth as measured by four variables included in the analysis, at the scale of census block group; however, the strength of the relationship varies geographically. Second, GWR, not ordinary least squares technique, should be used for regional scale spatial analysis because it is able to account for local effects and shows geographical variation in the strength of the relationship. 相似文献
16.
基于时空窗口改进的时空加权回归分析——以湖北省黄石市住房价格为例 总被引：3，自引：3，他引：0
针对地理加权回归(GWR)模型不能有效处理样本数据空间自相关性这一问题,构造局部时空窗口统计量,尝试改进时空加权回归(GTWR)模型。定义多时空窗口的概念,给出其选取、计算和验证方法;计算时空窗口包含的各样本点的被解释变量平均值,与样本拟合点的被解释变量值的比值,作为新的解释变量,构建改进的时空加权回归(IGTWR)模型。以土地稀缺、多中心、资源型城市——湖北省黄石市为例,收集2007~2012年商品住宅成交价格1.93万个数据和398个楼栋样本点,选取小区等级、绿化率、楼栋总层数、容积率、距区域中心距离和销售年份6个解释变量,分别利用常规线性回归(OLS)、GWR、GTWR和IGTWR方法进行回归分析。模型结果表明：计算Moran’s I指数和分析时间序列的自相关性,能确定时空窗口的大小和数量的选取;IGTWR模型和各变量的回归统计均通过0.05的显著性水平检验,有关解释变量的系数估计值在空间分布上能合理解释;GWR拟合结果优于OLS,GTWR优于GWR,而IGTWR拟合精度最好。与GTWR模型分析相比, IGTWR模型R2从0.877提升到0.919,而AICc、残差方(RSS)和均方差(MSE)分别从6 226、49 996 201和354.427下降到6 206、32 327 472和284.969。案例研究表明：IGTWR能够表达一定时空范围的时空自相关特征,减小了估计误差,提高了回归拟合精度。 相似文献
17.
Huang Jixian Mao Xiancheng Chen Jin Deng Hao Dick Jeffrey M. Liu Zhankun 《Natural Resources Research》2020,29(1):439-458
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
相似文献18.
中国亚热带丘陵山区植被沿海拔梯度分布格局(英文) 总被引：3，自引：0，他引：3
Knowledge of vegetation distribution patterns is very important.Their relationships with topography and climate were explored through a geographically weighted regression(GWR) framework in a subtropical mountainous and hilly region,Minjiang River Basin of Fujian in China.The HJ-1 satellite image acquired on December 9,2010 was utilized and NDVI index was calculated representing the range of vegetation greenness.Proper analysis units were achieved through segregation based on small sub-basins and altitudinal bands.Results indicated that the GWR model was more powerful than ordinary linear least square(OLS) regression in interpreting vegetation-environmental relationship,indicated by higher adjusted R 2 and lower Akaike information criterion values.On one side,the OLS analysis revealed dominant positive influence from parameters of elevation and slope on vegetation distribution.On the other side,GWR analysis indicated that spatially,the parameters of topography had a very complex relationship with the vegetation distribution,as results of the various combinations of environmental factors,vegetation composition and also anthropogenic impact.The influences of elevation and slope generally decreased,from strongly positive to nearly zero,with increasing altitude and slope.Specially,most rapid changes of coefficients between NDVI and elevation or slope were observed in relatively flat and low-lying areas.This paper confirmed that the non-stationary analysis through the framework of GWR could lead to a better understanding of vegetation distribution in subtropical mountainous and hilly region.It was hoped that the proposed scale selection method combined with GWR framework would provide some guidelines on dealing with both spatial(horizontal) and altitudinal(vertical) non-stationarity in the dataset,and it could easily be applied in characterizing vegetation distribution patterns in other mountainous and hilly river basins and related research. 相似文献
19.
Huiguo Zhang 《International journal of geographical information science》2013,27(9):1467-1489
The geographically weighted regression (GWR) has been widely applied to many practical fields for exploring spatial non-stationarity of a regression relationship. However, this method is inherently not robust to outliers due to the least squares criterion in the process of estimation. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression relationship. Using the least absolute deviation criterion, we propose two robust scenarios of the GWR approaches to handle outliers. One is based on the basic GWR and the other is based on the local linear GWR (LGWR). The proposed methods can automatically reduce the impact of outliers on the estimates of the regression coefficients and can be easily implemented with modern computer software for dealing with the linear programming problems. We then conduct simulations to assess the performance of the proposed methods and the results demonstrate that the methods are quite robust to outliers and can retrieve the underlying coefficient surfaces satisfactorily even though the data are seriously contaminated or contain severe outliers. 相似文献
20.
多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用 总被引：3，自引：2，他引：1
对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。 相似文献