Geographically weighted principal components analysis

Principal components analysis (PCA) is a widely used technique in the social and physical sciences. However in spatial applications, standard PCA is frequently applied without any adaptation that accounts for important spatial effects. Such a naive application can be problematic as such effects often provide a more complete understanding of a given process. In this respect, standard PCA can be (a) replaced with a geographically weighted PCA (GWPCA), when we want to account for a certain spatial heterogeneity; (b) adapted to account for spatial autocorrelation in the spatial process; or (c) adapted with a specification that represents a mixture of both (a) and (b). In this article, we focus on implementation issues concerning the calibration, testing, interpretation and visualisation of the location-specific principal components from GWPCA. Here we initially consider the basics of (global) principal components, then consider the development of a locally weighted PCA (for the exploration of local subsets in attribute-space) and finally GWPCA. As an illustration of the use of GWPCA (with respect to the implementation issues we investigate), we apply this technique to a study of social structure in Greater Dublin, Ireland.     

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<...     

High-resolution spatial assessment of population vulnerability to climate change in Nepal

Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions.     

基于主成分分析土壤水分扩散率单一参数模型的BP神经网络模型

应用水平土柱法测定了杨凌地区典型粘壤土的水分扩散率,利用土壤水分扩散率的单对数模型和双对数模型对其进行了拟合,建立了土壤水分扩散率单一参数模型,基于主成分分析建立了单一参数模型中参数B的BP神经网络模型。结果表明：利用主成分分析可将研究区域土壤容重、有机质含量、粘粒含量、粗粉粒含量和砂粒含量综合成3个主成分;基于主成分分析建立的BP神经网络模型拟合的单一参数模型参数[B]的均方根误差RMSE为0.308 2;将拟合得到的参数B代入单一参数模型中对土壤水分扩散率进行预测,除去其中较大值的预测结果偏低外,其余土壤水分扩散率预测结果都比较接近实测值,预测结果的均方根误差RMSE为0.257 8,可利用基于主成分分析建立的BP神经网络模型预测单一参数模型中的参数B。     

Exploring population spatial concentrations in Northern Ireland by community background and other characteristics: an application of geographically weighted spatial statistics

Information on how populations are spatially concentrated by different characteristics is a key means of guiding government policies in a variety of contexts, in addition to being of substantial academic interest. In particular, to reduce inequalities between groups, it is necessary to understand the characteristics of these groups in terms of their composition and their geographical structure. This article explores the degree to which the population of Northern Ireland is spatially concentrated by a range of characteristics. There is a long history of interest in residential segregation by religion in Northern Ireland; this article assesses population concentration not only by community background (‘religion or religion brought up in’) but also by housing tenure, employment and other socioeconomic and demographic characteristics. The spatial structure of geographical variables can be captured by a range of spatial statistics including Moran's I. Such approaches utilise information on connections between observations or the distances between them. While such approaches are conceptually an improvement on standard aspatial statistics, a logical further step is to compute statistics on a local basis on the grounds that most real-world properties are not spatially homogenous and, therefore, global measures may mask much variation. In population geography, which provides the substantive focus for this article, there are still relatively few studies that assess in depth the application of geographically weighted statistics for exploring population characteristics individually and for exploring relations between variables. This article demonstrates the value of such approaches by using a variety of geographically weighted statistical measures to explore outputs from the 2001 Census of Population of Northern Ireland. A key objective is to assess the degree to which the population is spatially divided, as judged by the selected variables. In other words, do people cluster more strongly with others who share their community background or others who have a similar socioeconomic status in some respect? The analysis demonstrates how geographically weighted statistics can be used to explore the degree to which single socioeconomic and demographic variables and relations between such variables differ at different spatial scales and at different geographical locations. For example, the results show that there are regions comprising neighbouring areas with large proportions of people from the same community background, but with variable unemployment levels, while in other areas the first case holds true but unemployment levels are consistently low. The analysis supports the contention that geographical variations in population characteristics are the norm, and these cannot be captured without using local methods. An additional methodological contribution relates to the treatment of counts expressed as percentages.     

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...     

近40年坝上后山地区沙漠化因子主分量的耦合波动

利用EOF (经验正交函数 )方法对河北坝上和内蒙古后山地区近 4 0年降水、年风速、大风日数、气候因子、牲畜头数和气候产量等 6个沙漠化波动因子主分量进行了计算 ,表明沙漠化诸因子尽管受降水的影响有同步波动的一面 ,但又可分解为风、畜、水三个相对独立变化的系列。由此将年大风日数、气候因子、牲畜头数和气候产量确定为影响沙漠化的主要波动因子 ,并以生物学年度将其按牧区和农牧交错区两类进行等权重耦合。结果表明 ,近 4 0年全区沙漠化因子主分量的耦合度经 3年滑动平均后 ,在 2 5 %～ 10 0 %间变化 ,196 5年至今一直维持在 75 %上下波动 ,196 7～ 1977年为耦合度最高的阶段。 80年代中期多雨段沙漠化的持续蔓延 ,充分证明了人类不合理活动在坝上后山地区沙漠化发展中的主导作用。     

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.     

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.     

Effects of soil surface components on soil hydrological behaviour in a dry Mediterranean environment (Southern Spain)

Various researchers have studied the spatial pattern of soil surface components such as vegetation, rock fragments, bare soil, litter and surface crusts, as a key factor of hydrological behaviour in Mediterranean settings with heterogeneous patches of vegetation cover and strong human impact. The studies indicate that there is a mosaic of patches that generate run-off or infiltrate overland flow, distributed in various ways along hillslopes. Few of these studies, however, have looked at areas underlain by metamorphic rocks such as phyllites or schists. This study analysed the temporal and spatial variability of the effects of soil surface components on hydrological processes in a small dry Mediterranean catchment underlain by metamorphic rocks. A systematic sampling of multiple sites throughout a hydrological year was carried out.We related the hydrological behaviour of soil surface components to 1) their position along the hillslope, 2) the distance of existing vegetation tussocks from the line of run-off, 3) rainfall intensity and 4) the main physical/chemical soil properties affecting infiltration processes. Statistical analysis was used to check the validity of the relationships. The results show that soil surface components have highly variable effects, in both space and time, on soil hydrological behaviour. These effects particularly depend on the location along the line of maximum slope and the intensity of preceding rainfall, whose interaction defines soil hydrological status. These results are similar to those for other Mediterranean settings with different lithology, in that the succession of contributing patches are hydrologically interconnected along a hillslope. The variables used and the grouping of explanatory variables through principal component analyses were found to be suitable for discussing the spatial distribution of soil surface components in the hydrologically dynamic environment of the study area.     

Local population and housing changes associated with destination redevelopments in Detroit (1990-2010): a geographically weighted shift-share analysis

The aim of this research is to utilize a novel approach called geographically weighted shift-share (GWSSA) analysis to estimate the degree and manner to which recent destination redevelopments have played a role in changing the characteristics of the nearby population and housing in Detroit between 1990 and 2010. The main benefit of geographically weighted shift-share analysis is that this technique isolates the local changes due to such projects while simultaneously controlling for the amount of the change expected for unrelated factors (e.g., the exodus of people leaving Detroit due to its overall negative reputation). Results suggest that such destination redevelopments in Detroit seem to be connected to a number of surprising positive local impacts during this period related to total population, 18-to-29-year-olds, non-Hispanic whites, employed civilians, unemployed civilians, households earning between $50,000 and $100,000 in annual income, total housing units, occupied housing units and vacant housing units.     

江浙典型茶园土壤铜含量的空间分异对比分析

土壤铜含量是影响茶叶质量的关键性因素之一,对其空间分布规律及其影响因素加以揭示,可为茶叶生产与品质提升提供理论指导与调控建议.以江浙典型茶园为例,运用ArcGIS空间分析功能,对比分析江浙典型地区茶园表层土壤铜的空间分异特征.结果表明:江浙茶园土壤铜的平均水平均符合茶园土壤环境质量的相关标准,且都具有强烈的空间相关性.江苏茶园土壤铜含量呈现西密东疏的团块状分布,空间分异不明显,少数区域出现高值,受区域因素的影响较大;而浙江茶园铜含量呈现高密度多同心圆状分布,空间分异显著,总体集中偏低,除与地势有较大关联性外,还受到一定程度的人为活动的干扰以及小地形气候的影响.     

新疆石河子农区土壤含盐量定量反演及其空间格局分析

盐渍化严重威胁着土地持续耕作和粮食生产安全,土壤含盐量的快速反演和及时监测,对新疆石河子农区农业可持续发展有着重大意义。以Landsat-8 OLI遥感影像与野外采样测定的72个样点土壤含盐量为数据基础,引入土壤亮度指数、土壤湿度指数、土壤盐盖度指数和土壤辐射水平指数等构建的特征遥感指数定量反演新疆石河子农垦区土壤含盐量,并对其空间布局进行了分析。研究表明：① 利用遥感影像像元特征遥感指数值反演地面对应土壤含盐量的精度较好,反演方法可行,反演精度为66%;② 反演得到土壤含盐量空间分布图在空间上等级明显,层次分明,呈现出“一带一区”的格局,“一带”是玛纳斯流域形成的盐渍化程度较高的条带,“一区”是内在土质形成的严重盐渍化区域;③ 在新疆石河子农区中,35.40%的棉田适合耕作,64.60%的棉田耕地需要进行改良,农区土壤盐渍化治理仍十分紧迫。