区域土壤空间抽样方法研究

对不同空间抽样方法进行对比研究是寻找具有空间相关性的不同资源和调查对象的最适宜抽样设计方案的重要手段.该文以广东省增城市土壤铬含量为空间要素,分别采用空间简单随机抽样、空间分层抽样和三明治抽样3种方法进行抽样;为便于比较3种方法的抽样精度,设定相同样本容量,采用Kriging插值对抽样结果进行精度评价.结果表明:在较为丰富的先验知识的情况下,三明治空间抽样方法所抽样点在预测土壤重金属含量分布情况时得到的克里格估值误差最小,是一种抽样精度更高、适用范围更广的抽样方案.     

GIS支持下的空间分层抽样方法研究——以北京市人均农业总产值抽样调查为例

利用GIS技术,采用空间数据分析方法(ESDA),将传统的抽样思路与空间信息相结合,提出了基于空间的"动态抽样框"编制技术,进行空间分层抽样技术的研究和应用,形成一套基于GIS的空间抽样流程。以北京市农村区域为例进行全市人均农业总产值的抽样调查,通过对人均农业总产值因子的空间自相关分析得到空间聚类结果;经过总体样本量的估算和空间动态抽样框的生成,进行空间分层和样本分配,并最终在每个分层中实施抽样。其结果为:动态生成"高-高集聚"、"低-低集聚"、"高-低集聚"、"低-高集聚"和"分布不明显"5个抽样框,并最终通过空间分层抽样流程得到北京市人均农业总产值的抽样调查结果为3.580万元,抽样误差的允许范围为±0.3861万元。     

基于控制区空间变异性的耕地质量监测样点布控方法

监测样点布设是实现对区域耕地质量监测的基础。为了提高耕地质量监测精度,建立了一套基于监测控制区空间变异性的耕地质量监测样点布控方法。利用Neyman法确定在给定置信度和抽样误差下的研究区最优样点总数;根据耕地质量及其影响因素划定监测控制区,为研究区进行空间分层;利用变异系数为各控制区分配监测样点;在各控制区内采用空间随机法布设监测样点。为了对比该抽样法的估算精度,又采用空间随机抽样法和基于自然质量等别、基于控制区权重的分层抽样法等进行了其余4种抽样布设方案,并计算了5种样点布设方案的Kriging估计误差,通过对比发现,在相同的取样总数下,不同的样点布设方案估算精度差别显著,基于控制区空间变异性的耕地质量监测样区分层布控方法的RMSE最小。得出科学的监测样点布设方案,可以在最小的投入下获取最大的信息量,提高耕地质量监测效率和精度。     

地理空间抽样理论研究综述

抽样调查是地理研究、资源评估、环境问题研究和社会经济问题研究的重要手段.对于地理分布的各种资源,由于调查数据往往具有空间相关性,传统的抽样调查理论无法满足日益增长的空间抽样需求.空间抽样理论是对具有空间相关性的各种资源和调查对象进行抽样设计的基础.本文详细论述了空间抽样理论发展现状.首先介绍了空间抽样的产生和发展,以及空间抽样所要研究的四个问题.然后介绍了基于设计的和基于模型的抽样统计推断方式,以及它们适用的范围.最后本文详细论述了Kriging理论在抽样理论的应用、前向、后向和双向样本布局方法和六种空间抽样样本优化选择标准.     

一种顾及先验知识与光谱信息的土地利用/覆被遥感分类方法

提出一种基于贝叶斯综合模型的先验知识与光谱信息的土地利用/覆被类型联合决策方法:首先依据光谱信息对遥感影像进行预分类,然后利用先验知识修正预分类结果,以减少因"同物异谱"和"异物同谱"造成的分类错误,抑制因空间分辨率较高造成的类内噪声较大等现象.试验表明,通过引入先验知识,分类精度显著提高,单纯应用光谱信息总体分类精度为81.89%;引入数字高程模型,总体分类精度提高至88.75%;引入土地利用先验知识,总体分类精度提高至94.41%.     

降雨量地面观测数据空间探索与插值方法探讨

空间插值方法广泛应用于气象数据产品的制作,其精度与气象要素的空间变异特征、气象观测站分布和插值方法选择有关。选择美国得州599个地面观测站30年平均降雨量记录,设计了27个观测站样本方案,选择全年、1月和8月数据,利用空间统计、空间自相关、半变异函数等方法探索降雨量的空间变异特征,并采用5种常规方法进行空间插值,比较和解释插值结果,在此基础上讨论基于知识的气象要素空间插值方法。案例研究发现:①降雨量地面观测数据通常具有明显的空间趋势、较强的空间自相关特征和较稳定的空间变异规律,但针对不同时段或采样方案,其空间自相关强度和半变异函数模型会有一定的差异。②增加气象观测站数,空间插值误差有减小的趋势;但观测站数目达到一定数值后,增加观测站数,插值精度提高并不明显。③在观测站较少时,不同插值方法间的精度差异较大,而在观测站充足的情况下,其差异有减小的趋势。④探讨气象要素与地理环境要素之间的关系,获得定量化的先验知识,开发基于知识的空间插值模型,是高精度气象要素插值的关键;线性加权回归和地理加权回归方法的初步试验验证了这一思路的有效性。     

长三角区域知识合作网络演化的空间特征

借助社会网络方法和动态演化的视角,对1991—2010年长三角16个地区间知识合作网络演化的空间特征进行考察,剖析了各时段内网络空间分布特征及空间格局、网络节点的空间驱动机制和区域知识合作的空间选择倾向。结果表明:长三角区域知识合作网络演化过程存在空间极化现象,以沪宁为轴线的苏南地区明显优于浙北地区;区域结网的空间驱动方式逐步由沪宁杭为主的大城市驱动向中小城市驱动转变;知识合作网络在空间上呈明显的核心-边缘格局并将长期存在。地理临近、行政区临近和知识规模临近是影响区际知识合作空间对象选择的主要因素。其中,知识规模临近的作用不断增强,而地理临近与行政区临近的作用则不断减弱。     

黄土高原典型流域LCM模型集总、半分布和分布式构建对比分析

暴雨中心的空间分布是影响小流域暴雨洪水过程模拟精度的关键要素。不同模型构建方式对降雨空间异质性的响应差异很大,选择恰当的水文模型构建方式对提高模型计算效率,减少降雨空间异质性对模型的影响从而提高预测精度具有重要意义。为此,本文基于刘昌明提出的适用于黄土高原超渗产流机制的LCM （Liu Changming Model）模型,分别构建了集总式、半分布式和全分布式的暴雨—径流模型,旨在比较分析不同模型构建方式对于降雨空间异质性的响应及其对模拟精度和计算效率的影响。结果表明：分布式构建模拟精度最高,纳什效率系数达到0.81,相关系数达到0.82;集总式构建计算时间最短;相比较而言,半分布式构建方法纳什效率系数达到0.78,主峰值模拟精度分别达到76.1%和65.8%。总体上,全分布式构建方法模拟精度最高,集总式计算耗时最短效率最高,半分布式在保持较高模拟精度的同时也具有较高的计算效率。     

基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较

鉴于已有研究主要集中探讨住房价格的空间依赖性,较少涉及空间异质性对住房特征价格的影响,也很少尝试构建不同计量模型来比较模型间刻画住房价格影响因素空间分异的准确性,以长沙市中心城区为研究区,采用空间扩展模型和地理加权回归模型比较分析城市住房价格影响因素的空间分异,结果表明：① 空间扩展模型和地理加权回归模型都表明,长沙市中心城区的住房属性边际价格随着区位的变化而变化,揭示住房价格影响因素具有显著的空间异质性;小区环境、交通条件、教育配套、生活设施等因素对住房价格的影响强度存在明显的空间分异。② 地理加权回归模型和空间扩展模型都能对传统特征价格模型进行改进,但地理加权回归模型在解释能力和精度方面都超过空间扩展模型;对属性系数估计空间模式的分析,地理加权回归模型形成的结果比采用坐标多义扩展的空间扩展模型更为复杂和直观。     

元胞邻域对空间直观模拟结果的影响

作为一种空间直观模拟模型,地理元胞自动机(Geo-CA)能够模拟及预测城市扩展与土地利用情景.地理CA模拟中,元胞邻域及其空间构型会对转换规则的挖掘与空间直观模拟结果的可靠性产生显著影响,从模拟进度和精度、景观格局及运行效率等角度可以定量分析这种影响.以logistic回归CA模型为例,基于Von:Neumann型和M...     

The ‘cottage effect’ in citizen science? Spatial bias in aquatic monitoring programs

Citizen science aquatic monitoring programs often rely on opportunistic, incidental contributions, which can lead to spatial bias, the uneven geographical distribution of sample sites. It is less known how this spatial bias compares to professional monitoring activities, or how geospatial biases (e.g. terrain slope, population density, road density) influence aquatic citizen science and professional lake monitoring programs. This paper compares sample sites in Ontario’s volunteer Lake Partner Program, against those identified by a stratified random sampling method currently used by the Province of Ontario, Ministry of Natural Resources and Forestry. Exploration of spatial bias within each sampling method was conducted using Kernel Density Estimation, a nonparametric approach to interpolating the spatial trend of a given variable. Results indicate that two distinct patterns of sampling clusters exist between the two datasets, suggesting a ‘cottage effect’ in which volunteers are more likely to sample accessible locations associated with recreation and summer home ownership. Although professional monitoring programs are not exempt from spatial bias, our research suggests that citizen science lake monitoring programs in Ontario are more influenced by natural and demographic biases related to the location, accessibility, size and general attractiveness of lakes.     

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.     

Sandwich mapping of rodent density in Jilin Province,China

Rodents are the main host animals that spread plague, and Spermophilus dauricus (S. dauricus) is the most common rodent in North China. In this study, a rodent density survey was carried out in China’s Jilin Province from April to August 2005. Moran’s I and semivariogram curves were used to investigate the spatial distribution characteristics of the sampling data. We found that the spatial auto-correlation index was low and failed to generate a meaningful semivariogram curve. In this case, commonly used interpolators, such as kriging, were not suitable for mapping density over the study area. However, the Sandwich model, which is based on spatial stratified heterogeneity, could be applied to our data. Our results showed that the type of soil and land use significantly influenced the distribution of rodent density, and the interactive effect of these variables was much stronger than that of each variable alone. The Sandwich-estimated rodent density map showed that rodent density increased from the southeast to the northwest in Jilin Province. Finally, a framework of a rodent density survey using the Sandwich model was introduced.     

城市人口分布的空间自相关分析——以沈阳市为例

为探测经典城市人口密度模型在微观层面上的适用性,本文通过计算基于格网的沈阳市人口景观密度,利用地统计学的理论与方法来分析其空间自相关性和变异性。研究发现:在10种不同采样粒度上人口密度半变异函数都符合球状模型,表征着城市人口分布具有较强的空间自相关性和变异性;但由于粒度不同,人口密度的空间自相关尺度不同,块金值不同,基台值不同,块金值与基台值的比值差异较大,证明人口分布的空间自相关具有较强的尺度依赖性;对于700m粒度而言,通过计算不同方向的半变异函数曲线可以发现,沈阳市人口密度呈现典型的带状异向性,反映出人口分布在不同方向上具有不同的结构特征。因此,研究城市人口分布时,可以城市人口分布的自相关性和变异性分析为基础,并须充分注意适宜尺度的选择和结构特征的识别。     

胡焕庸线两侧人口的空间分异性及其变化

胡焕庸线作为人地关系的重要地理大发现之一,揭示了中国人口分布东密西疏的重要特征,是人文与经济地理学对社会的重要贡献之一。本文采用地理探测器等定量方法,以人口普查数据为基础,对胡焕庸线两侧人口分布的分异性特征进行了深入讨论,并尝试解释中国人口分布特征变化的原因及其机制。结果表明：① 胡焕庸线作为中国最重要的人口分界线仍基本保持稳定,但从空间统计学分异特征来看该分界线仍可进一步优化调整;② 胡焕庸线两侧内部人口集聚模式发生着明显变化,东南半壁人口分布由改革开放之前和初期的相对均衡状态,转变为以长三角、珠三角等少数区域为中心的集聚模式;而西北半壁人口分布的集聚程度则趋于下降;③ 从人口空间分布的影响因素看,尽管总体上,经济发展类因素对于人口空间分布影响不断增强,但三大阶梯等自然地理本底条件依然对中国人口分布有着重要影响。     

Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning

One of the major sources of uncertainty associated with geographical data in GIS arises when they are the outcome of a sampling process. It is well known that when sampling from a spatially autocorrelated homogeneous surface, stratification reduces the error variance of the estimator of the population mean. In this study, we evaluate the efficiency of different spatial sampling strategies when the surface is not homogeneous. When the surface is first-order heterogeneous (the mean of the surface varies across the map), we examine the effects of stratifying it into first-order homogeneous zones prior to the usual stratification for a systematic or stratified random sample. We investigate the effect of this form of spatial heterogeneity on the performance of different methods for estimating the population mean and its error variance. We do so by distinguishing between the real surface to be surveyed (?), the sampling frame (?) including the choice of zoning, and the statistical estimators (Ψ). The study shows that zoning improves estimator efficiency when sampling a heterogeneous surface. Systematic comparison provides rules of thumb for choice of sample design, sample statistics and uncertainty estimation, based on considering different spatial heterogeneities on real surfaces.     

河西走廊村民关联的空间格局及影响因素分析

为深入研究村民关联的空间格局及影响因素,利用河西走廊15个县（市、区）的调查数据,采用模糊综合评判法测算村民关联的水平,运用探索性空间数据分析（ESDA）法测评村民关联的空间格局与区域差异,并运用地理加权回归（GWR）模型分析村民关联的影响因素。研究表明：（1）河西走廊地区村民关联水平普遍较低,空间格局差异明显,存在显著的正的空间自相关性。（2）河西走廊村民关联具有“点-带-团”状结构特征。村民关联水平较高的县级单元主要处于石羊河流域,形成了分值较高的聚集“点”;村民关联较低的疏勒河流域的敦煌市、玉门市、瓜州县形成了低值分布“带”;张掖市、酒泉市所辖县形成了村民关联低值“团”。（3）村内因子和村间因子与村民关联都呈显著的正相关关系,空间分异特征明显。村内影响因子的回归系数在空间上从西北向东南方向呈现逐渐递增的趋势,而村间因子形成了河西走廊东部和西部的高值聚集中心,呈现出空间地域上“两头高中间低”的“哑铃”结构。剖析村民关联空间格局及影响因素的内在机理,为促进农地制度有效合理变革进而提高农地利用效率,提出保护农地权益的合理化建议提供理论依据和方法参考。     

Spatial interpolation of marine environment data using P-MSN

ABSTRACT

When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries.     

Geospatial Thinking and Spatial Ability: An Empirical Examination of Knowledge and Reasoning in Geographical Science

This study examined the relationships between geospatial thinking and spatial ability, focusing on knowledge and reasoning about fundamental concepts in geospatial science. Scores on a geospatial thinking test had low correlations with each other, compared to high correlations among scores on spatial ability tests, and the degrees of relationship between geospatial thinking and spatial ability depended on the type of geospatial thinking. Expert geospatial scientists did not outperform students on spatial ability tests, suggesting that expertise in geospatial science might not simply be explained by superiority in spatial ability. The results point to the necessity of targeted instruction for geospatial learning.