Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression

Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5  km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs.     

Computational improvements to multi-scale geographically weighted regression

ABSTRACT

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.     

A bootstrap test for constant coefficients in geographically weighted regression models

Statistical tests for whether some coefficients really vary over space play an important role in using the geographically weighted regression (GWR) to explore spatial non-stationarity of the regression relationship. In view of some shortcomings of the existing inferential methods, we propose a residual-based bootstrap test to detect the constant coefficients in a GWR model. The proposed test is free of the assumption that the model error term is normally distributed and admits some useful extensions for identifying more complicated spatial patterns of the coefficients. Some simulation with comparison to the existing test methods is conducted to assess the test performance, including the accuracy of the bootstrap approximation to the null distribution of the test statistic, the power in identifying spatially varying coefficients and the robustness to collinearity among the explanatory variables. The simulation results demonstrate that the bootstrap test works quite well. Furthermore, a real-world data set is analyzed to illustrate the application of the proposed test.     

Using multilevel modeling and geographically weighted regression to identify spatial variations in the relationship between place-level disadvantages and obesity in Taiwan

Objectives

We examined whether and to what extent the relationship between township disadvantages and obesity varied across geographical areas.

Methods

A cross-sectional analysis of a population-based sample of Taiwanese adults (N = 25,985) from the 2005 Social Development Trend Survey on Health and Safety was performed. Multilevel models integrated with geographically weighted regressions were employed to analyze the spatially varying association between area disadvantages and obesity. The dependent variable was body mass index calculated from respondents’ self-reported weight and height. The key explanatory variable was a township disadvantage index made of poverty level, minority composition, and social disorder. Other individual socio-demographic characteristics were included to account for the compositional effect.

Results

The association between township disadvantages and elevated obesity risk in Taiwan was found to be area-specific. In contrast to results from the commonly used global regression, geographically weighted regression model showed that township disadvantages elevated obesity level only in certain areas.

Conclusions

We found heterogeneity of place-level determinants of obesity across geographical areas. Adoption of population approach to curb obesity would require area-specific strategies for most needed areas.     

Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches

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.     

Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression

This article identifies drivers of forest transition in a province of Northern Vietnam between 1993 and 2000 by applying geographically weighted regression (GWR) analysis to remotely sensed and statistical data. The regression model highlighted the spatial variation of the relationship between the percentage of land afforested and its proximate causes. Factors identified as having a major impact on afforestation are: the presence or proximity of a wood-processing industry, the distance to highways, and land allocation to households. Whereas the two former variables are in most areas of the province positively correlated with afforestation, an unexpected negative correlation was observed for the latter. The analysis of these results, supported by an in-depth knowledge of the area and of the political context, leads to the conclusion that, during the time period considered, afforestation was largely driven by state organisations on protected state-owned land, and forestry was not a significant component of household economic activities.     

Concrete evidence & geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts

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.     

Coverage inequality and quality of volunteered geographic features in Chinese cities: Analyzing the associated local characteristics using geographically weighted regression

The volunteered geographic information (VGI) gains increasing popularity with the general public and scientific community. However, the optimism about the VGI has been tempered by two critical issues: inequality in data coverage (social justice) and data quality. It therefore requires a better understanding of the mechanism driving VGI contributions and content quality. With a case of China, this paper demonstrates one potential avenue, examining the associations between VGI coverage/quality and local demographic and socioeconomic characteristics. In particular, VGI data are harvested from the OpenStreetMap for 333 cities in China. VGI coverage is measured by the total volume of different geographic features (point, line and polygon); and VGI quality is described from two aspects: completeness and accuracy. Geographically weighted regression (GWR) shows that both demographic and socioeconomic factors have statistically significant influences on VGI coverage and quality. More specifically, densely populous cities with more young, educated and non-agricultural people enjoy higher VGI coverage and quality. Cities with lower VGI coverage and quality are primarily located in the western and southwestern regions where the ethnic minorities concentrate. High VGI coverage and quality are possibly observed in economically developed cities with high marketization degree. Besides, possibility of high VGI coverage and quality occurs in cities with more labor in scientific research and greater percentage of employers in the tertiary industry. The GWR also demonstrates that the strength and nature of the obtained relationships vary across the 333 cities. The spatial non-stationary relationships may partially answer for the controversial empirical conclusions in earlier case studies at different scales. Quantitative analysis (Gini index, Lorenz curve and Moran's I index) further evidences the great inequality in VGI coverage and quality. It can be safely inferred that the differences in engagement and use of VGI, as a new digital divide, can raise troubling concerns on the social justice implications.     

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

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.     

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

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。     

农田背景下景观结构对半自然生境生物多样性的多尺度影响

以黄河下游典型农区封丘县为研究区,调查比较了农业景观中半自然生境(包括人工林、树篱和沟渠)与农田生境中植物和地表节肢动物的物种多样性,并应用广义线性模型(GLM)从不同的空间尺度分析半自然生境和农田中的物种多样性与景观变量之间的关系.结果表明:①林地植物多样性最高,且以人工林和沟渠中的植物物种相似度最高;各半自然生境中地表节肢动物的多度和物种丰富度明显高于农田,且以人工林和树篱间地表节肢动物的相似度最高.②在250 m景观范围内的景观变量能更好地解释植物多样性和地表节肢动物多样性,而树篱和沟渠在400 m景观范围上地表节肢动物的尺度效应最显著.③在250 m尺度上植物多样性与景观变量的拟合方面,在人工林和树篱生境中,散布与并列指数(IJI)和植物丰富度呈显著负相关;人工林中IJI和周长面积比(PA-RAMD)对植物香农多样性指数呈负相关;在树篱中,边缘密度(ED)、聚集度指数(AI)与植物香农多样性指数负相关显著,欧几里得最近距离(ENN_MN)与均匀度指数正相关;在沟渠中,ED、AI与植物丰富度显著负相关.④地表节肢动物与景观变量的拟合显示,在250 m景观范围上,人工林生境主要体现在多度与香农多样性指数(SHDI)和土地利用丰富度的负相关,而农田中则是多度与SHDI呈显著负相关,与ED、PARA_ MD、AI和土地利用丰富度(LUR)呈显著正相关;在400m景观范围上,树篱中,IJI与地表节肢动物的多度和丰富度呈显著正相关;沟渠生境中,只有多度与IJI和土地利用丰富度显著负相关,与SHDI显著正相关.     

Application of geographically weighted regression model in the estimation of surface air temperature lapse rate

The surface air temperature lapse rate(SATLR)plays a key role in the hydrological,glacial and ecological modeling,the regional downscaling,and the reconstruction of high-resolution surface air temperature.However,how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for re-searchers.The geographically weighted regression(GWR)model was applied in this paper to estimate the SATLR in China's mainland,and then the assessment and validation for the GWR model were made.The spatial pattern of regression residuals which was identified by Moran's Index indicated that the GWR model was broadly reasonable for the estimation of SATLR.The small mean absolute error(MAE)in all months indicated that the GWR model had a strong predictive ability for the surface air temperature.The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation.Therefore,the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition.     

基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究

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(R_adj²)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的R_adj^2<...     

基于地理探测器和GWR模型的中国重点镇布局定量归因

重点镇是小城镇发展的龙头,形成科学合理的重点镇布局对优化中国城市化战略格局有重要意义。论文以2004年和2014年分别公布的1887个和3675个全国重点镇为样本,对其分布及效应的变动特征进行探究,进而在地级尺度对重点镇布局的影响因子及其作用进行地理探测和局部空间回归。结果表明：① 经增补调整,中国重点镇布局及建设效应的均衡性增强,主要集聚区西移北扩,冷热点的分布突破“胡焕庸线”,经济辐射效应的分化程度减弱,体现出政策因素的有力影响。除县际均衡和区域倾斜政策外,重点镇的分布还受到海拔高度、公路网密度、常住人口城镇化率等因子的显著作用。② 因子探测器、GWR模型和交互作用探测器的结合能更精准地刻画影响因子的作用方式、方向、路径和强度。中国重点镇的布局不是5个显著性因子均匀、独立、直接作用的结果,而是影响均具空间异质性的各因子两两交互作用后增效的产物。③ 县际均衡政策与其他因子的协同作用是形成现有重点镇分布格局的主导力量;区域倾斜政策的效果总体较好,但目标区域还需更准确。     

基于多光谱和地理加权回归模型的石嘴山城市土壤有机碳空间分布研究

城市土壤有机碳(SOC)分布受城市建设、工业发展等人为因素的影响表现出明显的空间差异。为揭示石嘴山市SOC受城市化、工业化等人类活动的影响,分别利用普通克里格法(OK)、多元线性回归克里格法(RK)、遥感反演方法(RS)和遥感-地理加权回归克里格法(RGWRK)预测石嘴山市SOC空间分布。结果表明:石嘴山市SOC含量在1.31～66.92 g·kg^-1之间变化,其平均值为17.61 g·kg^-1。石嘴山市不同功能区SOC含量存在显著差异(p<0.05),具体表现为工业区>医疗区>商业区>道路>住宅区>公园>农田>科教区;SOC含量变异系数为66.27%,呈中等程度变异;其最佳拟合模型为高斯模型,C₀/(C₀+C)为0.02,属于强空间自相关。SOC与遥感影像波段DN值的差值(B1-B7、B3-B7、B4-B7)和地形因子(高程、坡度、起伏度)之间存在着极显著的相关性(p<0.01);通过对4种方法的结果进行对比可知以各波段DN值差值与地形因子为输...     

Comparison of spatial and aspatial logistic regression models for landmine risk mapping

Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km² municipality in Bosnia and Herzegovina (BiH) and a large 4500 km² region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches.     

中国高技术产业创新效率影响因素的空间异质效应—基于地理加权回归模型的实证研究

首先借助超越对数生产函数形式的多产出随机前沿模型估算出2008年~2013年中国30个省域高技术产业创新效率,继而基于地理加权回归模型(GWR)研究了企业规模、市场结构、政府投入及研发支出结构对创新效率的影响。结果表明:中国30个省域高技术产业创新效率存在空间正自相关性;企业规模、市场结构及研发支出结构对创新效率产生正向影响,而政府投入阻碍了高技术产业创新效率;各影响因素对高技术产业创新效率的影响均具有空间异质性特征,且随着时间的推移稳定存在。最后提出相关政策建议。     

Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R² = 0.642) and geographically weighted regression (GWR) (Adjusted R² = 0.797) models were worse than those of MGWR (Adjusted R² = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R² = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.     

农业景观保护路径历史回顾及启示

农业景观保护是实现农业景观可持续发展的重要举措,关系到中国粮食安全保障、农耕文明传承、乡村风貌维系、生态安全格局构建乃至社会经济稳定发展.本文借助对发达国家农业景观保护路径的长时间尺度历史回顾,总结归纳出农业景观保护路径发展的一般规律,即其在社会经济发展进程中一般经历传统保护、应对保护、管制保护、治理保护和管护保护五个...     

庐山森林景观美学质量与景观格局指数的关系

森林景观美学价值和生态可持续价值是森林景观系统中两种主要的价值。采集一系列典型森林景观照片,将这些景观照片对公众进行审美评价,并判别他们的景观生态特征,最后进行景观美学质量与景观格局指数之间相互关系的研究。本文得出了以下主要结论：（1）美景度值（SBE值）与景观组分指数中的水域所占面积比存在很强的正相关（r=0．472,P〈0．01）,与自然性指数也有较强的正相关（r=0．368,P〈O．05）;而与建筑所占面积比存在很强的负相关（r=-0．422,P〈O．01）。（2）SBE值与景观格局指数中的斑块密度（r=-0．489,P〈0．01）存在显著的负相关,而与边界密度（r=0．481,P〈o．01）、多样性指数（r=0．602,P％0．01）存在显著的正相关。（3）SBE值与开放性指数、最大斑块面积比和形状指数之间并没有显著的相关性。