sandwichr: Spatial prediction in R based on spatial stratified heterogeneity

Spatial prediction is commonly used in social and environmental research to estimate values at unobserved locations using sampling data. However, most existing spatial prediction methods and software packages are based on the assumption of spatial autocorrelation (SAC), which may not apply when spatial dependence is weak or non-existent. In this article, we develop a modeling framework for spatial prediction based on spatial stratified heterogeneity (SSH), a common feature of geographical variables, as well as an R package called sandwichr that implements this framework. For populations that can be stratified into homogeneous strata, the proposed framework enables the estimation of values for user-defined reporting units (e.g., administrative units or grid cells) based on the mean of each stratum, even if SAC is weak or absent. The estimated values can be used to create predicted surfaces and mapping. The framework also includes procedures for selecting appropriate stratifications of the populations and assessing prediction uncertainty and model accuracy. The sandwichr package includes functions to implement each step of the framework, allowing users to implement SSH-based spatial prediction effectively and efficiently. Two case studies are provided to illustrate the effectiveness of the proposed framework and the sandwichr package.     

Empirical spatial econometric modelling of small scale neighbourhood

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.     

空间信息分层复合分析模型

尺度依赖性、空间依赖性和空间非均质性是空间信息的本质特征,如何在空间分析中综合考虑以上特征目前仍缺少有效方法.本文提出的空间信息分层复合分析模型通过“回归的回归”两阶段分析建立包含空间异质性、空间依赖关系与空间尺度效应的空间单元综合回归模型,有助于解决空间分析中数据的多维复杂性涉及的尺度效应、空间依赖性和空间异质性综合...     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

A model for integrated spatial land use characteristics linking to surface nutrient concentration

Quantifying land use heterogeneity helps better understand how it influences biophysical systems. Land use area proportions have been used conventionally to predict water quality variables. Lacking an insight into the combined effect of various spatial characteristics could lead to the statistical bias and confused understanding in previous studies. In this study, using spatial techniques and mathematical models, a diagnostic model was developed and applied for quantifying and incorporating three spatial components, namely, slope, distance to sampling spots, and arrangement. The upper catchment of Miyun Reservoir was studied as the test area. Total nitrogen, total phosphorus, and chemical oxygen demand of water samples from field measurements were used to characterize the surface water quality in 52 sub-watersheds. Using parameter calibrations and determinations, combined spatial characteristics were explored and detected. Adjusted land use proportions were calculated by spatial weights of discriminating the relative contribution of each location to water quality and used to build the integrated models. Compared with traditional methods only using area proportions, our model increased the explanatory power of land use and quantified the effects of spatial information on water quality. This can guide the optimization of land use configuration to control water eutrophication.     

多尺度表达中空间拓扑关系等价性评价模型

本文主要讨论不同尺度下地理目标之间拓扑关系的等价性问题。根据人类认知习惯,得出从大比例尺地理空间中拓扑关系抽象为小比例尺空间中拓扑关系应当遵循的基本规则,并利用模糊函数将这些规则定量化;在此基础上,得到评价地理空间场景中多个空间目标之间拓扑关系在不同尺度下是否等价的综合评价模型。该模型可以用于检验和维护空间数据质量、为地图自动综合提供空间关系处理和评价方法的参考。     

纹理频谱分析的高分辨率遥感影像最佳尺度选择

基于对纹理频谱的分析提出了一种高分辨率遥感影像最佳尺度的选择方法。首先,分析四种典型地物在傅里 叶变换频域的频谱响应特性。然后,采用点扩散函数对原始影像进行尺度扩展,进而根据地物纹理的径向与角向曲线 随尺度扩展的变化选择最佳尺度。最后,通过分析四种地物在6个尺度下的纹理特征可分性,说明本文方法能客观反映 出地物的尺度效应,具备最佳尺度选择的可行性。利用支持向量机对QuickBird全色影像进行面向对象的分类,实验结 果表明在最佳尺度下可取得较高精度。     

太阳轨迹空间连续性的日照分析算法优化

针对日照分析模拟必须考虑众多真三维地形/地物的遮挡因素,传统的日照分析方法难以在现实世界的复杂地理场景中得出高效、准确分析结果的问题展开研究,基于太阳运行轨迹的空间连续性,利用数据缓存方法对遮挡图元进行预判,实现了日照分析算法的优化。应用实例表明,将该方法与空间划分方法配合使用,可以在保证运算结果正确的基础上,进一步提高日照分析的计算效率,从而实现复杂地理场景中准确、快速的日照分析计算。     

The impact of spatial elements on the forecasting of Spanish labour series

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.     

朝鲜人口统计数据空间化模拟及影响因子分析

朝鲜人口统计数据空间化是解决朝鲜统计数据与自然要素数据融合分析的重要途径。基于朝鲜市郡级人口普查数据,将GIS空间分析技术与统计学方法相结合,分析了朝鲜人口密度与空间因子的关系,采用多元回归的方法建立了朝鲜人口密度空间化模型,在GIS平台中实现了朝鲜人口密度的空间格网化模拟,并利用地理探测器对影响朝鲜人口密度空间分布因素的决定力进行了有效探测。结果表明,多元回归模型拟合精度达到0.769,生成的栅格人口密度数据与朝鲜三级行政区人口统计数据保持一致。同时,影响该地区人口密度的因子依次为道路网密度、居民点密度、居民地指数、海拔、坡度和耕地指数。     

基于MapGIS图元参数检索开发与应用

根据GIS空间数据包含的信息:①空间定位数据;②属性数据;③空间图元信息。把数据检索进行重新分为空间检索、属性检索和空间图元参数检索等3类,并利用Visual basic6.0+Map GIS6.5二次开发函数库设计和开发出图元参数条件查询模块。该模块是空间检索和属性检索的有益的补充,且界面友好,可以有效地通过空间实体的图元参数进行数据检索,并应用于西藏冈底斯斑岩铜矿预测评价中,成功地通过图元参数检索提取了岩浆岩、断裂构造等有用信息。     

地形因子与小微地震相关度的空间异质性分析

针对传统最小二乘回归未能顾及数据的空间特性,且无法度量模型自变量与因变量相关性的空间变异特性的问题,本文提出利用地理加权回归方法分析小微地震频次与地形因子相关度的空间异质性。以四川地区的地震监测资料、DEM为实验数据,选取地形复杂度、坡度变率、坡向变率和地面曲率为自变量,地震发生频次为因变量,构建地理加权回归模型,并进行回归系数的空间变异分析。实验分析发现,地震频次与地形因子具有一定的相关性:地形复杂度与地震频次相关性最强;坡度变率、沟壑密度、剖面曲率与地震频次的相关性依次减弱;不同空间位置的地形因子和地震频次的相关性具有较明显的空间异质性。实验结果表明,地理加权回归可以有效地度量分析地震频次与地形因子相关度的空间异质性,研究结果可为地震及次生灾害的分析与预报提供辅助决策参考。     

面向对象影像多尺度分割最大异质性参数估计

多尺度分割是遥感影像分析的关键步骤,影像分割过程中的尺度参数选择直接关系到面向对象影像分析的质量和精度。首先,总结了面向对象影像分析中尺度概念的内涵,分析遥感影像空间和属性两大基本特征,依据空间统计和光谱统计获得理论上最优的空间尺度分割参数、属性尺度分割参数。其次,运用了基于谱空间统计的高分辨率影像分割尺度估计方法,分析了分形网络演化多尺度分割与影像谱空间统计特征的关系,进而将基于谱空间统计的面向对象影像分析尺度参数应用于分形网络演化多尺度分割算法中,最后,对其参数的合理性进行验证。研究采用高空间分辨率IKONOS和SPOT 5影像数据,选择建筑实验区和农田实验区进行空间和光谱特征统计,以进一步估计分割中的最佳尺度参数。使用分形网络演化方法对图像进行分割,利用监督分类对本文提出的尺度估计方法进行验证,验证结果表明尺度估计方法可以一定程度上保证后续的面向对象影像分类的精度。不同于以往分割后评价的尺度选择方法会需要大量的运算量,本文方法不需要先验知识的参与,且在分割前就可以自适应地估计出相对较为合适的尺度参数,提高了面向对象信息提取的自动化程度。     

遥感数据空间尺度分级模型与基本比例尺关系

多源遥感数据综合应用是遥感发展的必然趋势,统一的遥感数据空间尺度分级模型是多源数据集成与综合应用的基础。虽然已有多种空间尺度分级模型,但很多主流模型并非出于分尺度综合应用目的,缺乏客观的比较和评价。国家基本比例尺系统作为经过论证、中国应用面最广泛、接受度最高的一种尺度分级系统,是以应用为导向的遥感数据空间尺度分级模型的最优参照系。从不同视觉精度下国家基本比例尺对图像空间分辨率的需求出发,比较各空间尺度分级模型的层级分辨率与需求分辨率的匹配情况,包括OGC Well Known Scale Set的Global CRS84Pixel和Google Maps Compatible,以及NASA World Wind、Google Map、百度地图、天地图等软件平台采用的层级格网系统,以及"五层十五级"遥感数据组织模型,通过对数据信息冗余度的分析,对各个模型进行了评价。结果表明,在高视觉精度应用需求下,"五层十五级"模型与基本比例尺精度要求具有较明显的匹配优势,其次为OGC Google Maps Compatible模型和天地图模型,其余模型平均数据冗余倍数在2倍左右;在低视觉精度应用需求下,"五层十五级"模型平均数据冗余度仍为最低,其次为Google Map模型,其余模型平均数据冗余倍数都在2倍以上。     

基于空间认知的三维地理信息系统研究

从空间信息中更快、更多地获取用户所需信息以辅助完成工作是GIS系统的根本目标。从空间认知的不同模式出发,分析了三维空间可视化的两种趋势;考虑到不同人群认知能力的差别,对用户进行了具体的分类,结合用户的使用场景与目的并在可视化实现中对比例尺与LOD进行分析,提出了一种适人化三维GIS模型。     

市县国土空间规划主体功能区划定方法研究

为了健全现行国土空间规划体系中市县尺度主体功能区的划定方法。该文以主体功能区基本理论为指导,综合地理国情信息中的地表现状数据与DEM数据,结合专题调查与经济社会统计数据等空间信息,利用GIS空间分析、多元统计分析、基于规则的分类模型等技术,研究细化上位主体功能区进行市县主体功能区划定的方法、指标与分类体系、与上位主体功能区的衔接机制。提出了一种基于局部功能单元的市县主体功能分区方法和流程。并以榆林市为实证研究对象,进行实证研究,划定了榆林市42920 km²的市县级主体功能分区。探索将主体功能分区的空间尺度延伸到具体的地理单元,发挥主体功能区在国土空间规划体系中基础性作用,为主体功能区战略格局在市县层面落地提供科学依据。     

SuperMap GIS地理加权回归模型研究与实现

地理加权回归分析是对普通线性回归模型的扩展,将空间数据的地理位置嵌入线性回归参数之中,以此来研究空间关系的空间异质性或空间非平稳性,属于局部空间分析模型.通过地理加权回归分析可以确定两种或两种以上变量间相互依赖的定量关系,局部区域的参数估计可以得到地理空间存在的不同空间关系,核函数的选取规则和带宽参数的验证方法也是本文研究的内容.     

Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach

This paper adopts a Bayesian spatial modeling approach to investigate the distribution of young offender residences in York Region, Southern Ontario, Canada, at the census dissemination area level. Few geographic researches have analyzed offender (as opposed to offense) data at a large map scale (i.e., using a relatively small areal unit of analysis) to minimize aggregation effects. Providing context is the social disorganization theory, which hypothesizes that areas with economic deprivation, high population turnover, and high ethnic heterogeneity exhibit social disorganization and are expected to facilitate higher instances of young offenders. Non-spatial and spatial Poisson models indicate that spatial methods are superior to non-spatial models with respect to model fit and that index of ethnic heterogeneity, residential mobility (1 year moving rate), and percentage of residents receiving government transfer payments are, respectively, the most significant explanatory variables related to young offender location. These findings provide overwhelming support for social disorganization theory as it applies to offender location in York Region, Ontario. Targeting areas where prevalence of young offenders could or could not be explained by social disorganization through decomposing the estimated risk map are helpful for dealing with juvenile offenders in the region. Results prompt discussion into geographically targeted police services and young offender placement pertaining to risk of recidivism. We discuss possible reasons for differences and similarities between the previous findings (that analyzed offense data and/or were conducted at a smaller map scale) and our findings, limitations of our study, and practical outcomes of this research from a law enforcement perspective.     

协同遥感信息与统计信息的人口空间格局分析

针对人口空间格局分布分析不足的问题,借助遥感信息与基本统计信息,引入人口地理集中度、探索性空间数据分析和地理加权回归模型,定量地分析了特征因子在市域范围上的空间异质性。人口集中指数与Moran指数能有效地反映空间事物之间的密切作用关系,在产业空间分布中能较好地评价集聚效应,反映空间分布状态。以长江经济带为研究区域,对人口空间分布进行了研究,得出了“圈层集中-东西对立-南北差异-四周分散”的分布特征,并从经济、社会、环境、土地利用方面分析原因,为人口合理增长和资源分配提供了参考。     

结合空间分析的面向对象无人机影像土地利用分类

利用高分辨遥感影像进行土地利用分类,为农村土地利用动态监测及土地综合整治快速地提供基础地理空间数据。以高分辨无人机影像为数据源,研究利用面向对象多尺度分割技术结合GIS空间分析对影像进行土地利用分类。根据对象内同质性高、对象间异质性高的准则,引入加权局部方差与空间自相关指数构建全局最优分割非监督评价指数,然后利用最邻近分类器对影像进行分类。实验结果表明,该方法减少人工目视确定最优分割尺度的主观性,能够避免某些地物不能被有效归类的现象,在单一尺度下获得较高的分类精度。