Local configuration measures for categorical spatial data: binary regular lattices

This paper examines the utility of a number of pattern measures for local exploratory analysis of binary spatial data. Based on a review of existing pattern measures in cartography, geography, image analysis, and landscape ecology, two fundamental classes of such measures, termed compositional and configurational, are identified. The paper focuses on configurational measures and it is suggested that as many as five such measures (join counts, patch numbers, patch sizes, patch proximity, and distribution of the classes relative to the focal cell of the window) are required to differentiate between all possible local categorical maps. This suggestion is explored by examining aspects of the statistical behaviour (probability distributions and correlations between extreme values of pairs of measures) of a set of 12 configurational measures. Their use is also demonstrated by means of an empirical example.     

局部空间自相关统计洪水灾害影像分析

针对洪水灾害分析在速度、准确性和及时性等方面的需求,该文在研究空间自相关分析的基础上,提出了一种基于局部自相关统计的洪水灾害影像分析方法。首先,对影像进行掩膜处理,去除云层干扰;其次,采用局部空间统计的方法对影像进行统计分析;然后,通过密度分割的方法提取水体,将影像分为水体和陆地两类;最后,将3幅影像分类的结果进行空间叠加分析,分析洪水灾害影响情况。以2013年嫩江流域3个时期的影像为实验数据,设计了仿真实验。实验结果表明,该方法可以较准确地对大面积洪水影响区域进行分析。     

Identifying local spatial association in flow data

In this paper we develop a spatial association statistic for flow data by generalizing the statistic of Getis-Ord, G _i (and G _i ^*). This local measure of spatial association, G _ij, is associated with each origin-destination pair. We define spatial weight matrices with different metrics in flow space. These spatial weight matrices focus on different aspects of local spatial association. We also define measures which control for generation or attraction nonstationarity. The measures are implemented to examine the spatial association of residuals from two different models. Using the permutation approach, significance bounds are computed for each statistic. In contrast to the G _i statistic, the normal approximation is often appropriate, but the statistics are still correlated. Small sample properties are also briefly discussed. Received: 18 February 1998/Accepted: 29 September 1998     

Web-based analytical tools for the exploration of spatial data

This paper deals with the extension of internet-based geographic information systems with functionality for exploratory spatial data analysis (esda). The specific focus is on methods to identify and visualize outliers in maps for rates or proportions. Three sets of methods are included: extreme value maps, smoothed rate maps and the Moran scatterplot. The implementation is carried out by means of a collection of Java classes to extend the Geotools open source mapping software toolkit. The web based spatial analysis tools are illustrated with applications to the study of homicide rates and cancer rates in U.S. counties.This research was supported in part by a number of grants from the US National Science Foundation: NSF Grant SBR-9410612, BCS-9978058, to the Center for Spatially Integrated Social Science (csiss), and a grant from the National Consortium on Violence Research (ncovr is supported under grant SBR-9513040 from the National Science Foundation). In addition, support was provided by grant RO1 CA 95949-01 from the National Cancer Institute. Special thanks to Dr. Eugene J. Lengerich of the Pennsylvania State Cancer Institute for providing the data on colon cancer diagnoses.     

Global and local spatial autocorrelation in bounded regular tessellations

This paper systematically investigates spatially autocorrelated patterns and the behaviour of their associated test statistic Moran's I in three bounded regular tessellations. These regular tessellations consist of triangles, squares, and hexagons, each of increasing size (n=64; 256; 1024). These tesselations can be downloaded at http://geo-www.sbs.ohio-state.edu/faculty/tiefelsdorf/regspastruc/ in several GIS formats. The selection of squares is particularly motivated by their use in raster based GIS and remote sensing. In contrast, because of topological correspondences, the hexagons serve as excellent proxy tessellations for empirical maps in vector based GIS. For all three tessellations, the distributional characteristics and the feasibility of the normal approximation are examined for global Moran's I, Moran's I ^(k) associated with higher order spatial lags, and local Moran's I _i. A set of eigenvectors can be generated for each tessellation and their spatial patterns can be mapped. These eigenvectors can be used as proxy variables to overcome spatial autocorrelation in regression models. The particularities and similarities in the spatial patterns of these eigenvectors are discussed. The results indicate that [i] the normal approximation for Moran's I is not always feasible; [ii] the three tessellations induce different distributional characteristics of Moran's I, and [iii] different spatial patterns of eigenvectors are associated with the three tessellations. Received: 2 July 1999 / Accepted: 9 November 1999     

基于局部空间统计分析的SPOT 5影像分类

受同物异谱和异物同谱现象的影响,对遥感影像进行分类时若仅利用光谱信息则分类精度的提高将会受到限制,而局部空间统计特征可以通过对地物空间聚集度的描述与分析在一定程度上减轻这种影响。本文研究了局部空间统计在不同指数(Moran’s I, Getis-Ord G_i, Geary’s C)、邻域规则和间隔距离下,对高空间分辨率的SPOT 5影像分类精度的影响规律。首先,对波段1进行局部空间统计分析,运算结果作为纹理波段添加到原始的光谱波段中;然后,综合利用光谱波段和纹理波段进行监督分类;最后,选取测试样本进行分类的精度评价,并比较分析不同条件下的分类精度,得到地物分类精度同参数之间的关系与规律。通过分析可以得出Getis-Ord G_i指数对于总体分类精度的提高最理想,总体分类精度从 87.74%提高到95.12%。     

Computing environments for spatial data analysis

This paper describes the functionality and architecture of SpaceStat, the SpaceStat Extension for ArcView and the DynESDA Extension for ArcView. It compares the features of these packages to five other software implementations for spatial data analysis. Some ideas are formulated on generic requirements and future directions pertaining to computing environments for spatial data analysis.     

空间数据研究的发展及对策

随着地理信息科学和地理信息系统的发展,空间数据先后经历数据生产、数据质量、数据不确定性和可用性4个研究阶段。在分析各阶段研究内容和方法的基础上,深入分析空间数据现有研究间的联系与区别,并给出未来的研究对策。     

一种基于全球剖分网格的多源遥感数据空间关联方法

针对遥感数据具有多源化且数据量大等特征,同时缺乏统一的空间组织框架,对于数据间空间关联关系建立存在困难等问题,文章提出了基于全球剖分网格的多源遥感数据空间关联模型解决方法:首先采用GeoSOT剖分框架对多源遥感数据进行统一组织与管理,然后根据提出的"网格-数据块-数据"的三层组织概念模型,建立基于全球剖分网格的多源遥感数据空间关联模型;最后进行关联影像的表达实验。实验结果表明,该方法在一定程度上实现了多源遥感数据的结构化组织,有效建立了多源遥感数据的空间关联,并与传统的坐标系统建立对比分析,提高了数据利用的应用效能,整体上促进了遥感数据的使用效率。     

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

Abstract

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).     

空间数据融合的基本框架

本文就空间数据融合的基本理论和问题进行了阐述。空间数据分为矢量数据和栅格数据,由于矢量数据的融合比较简单,本文就矢量数据的图形数据和属性数据的融合加以简单论述;主要研究了栅格数据,目前对于栅格空间数据而言主要是影像数据,本文对影像数据的融合的层次,步骤和方式等问题进行了系统研究;而且对空间数据融合存在的问题和解决的途径进行了论述。     

Big spatial data for urban and environmental sustainability

ABSTRACT

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.     

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     

地球化学的空间自相关异常信息提取方法

针对地球化学数据存在的空间分布相关性特征,该文提出了一种基于空间自相关统计的地球化学异常提取方法。以内蒙古浩布高矿床外围的土壤地球化学数据为例,通过对Sn、Cu元素地球化学数据在不同空间间隔上的全局自相关计算,测算其空间聚集的程度,选取聚集程度最高时的间隔距离作为局部空间自相关的参数,通过局部Moran’s I值研究元素的空间分布特征,分析空间聚类和异常值,从而提取地球化学异常。结果表明,局部空间自相关分析可以揭示Sn、Cu地球化学数据的空间分布特征,能够更好地提取地球化学弱缓异常,说明空间自相关是一种有效的地球化学异常识别方法。     

An Improved Hilbert Curve for Parallel Spatial Data Partitioning

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data i...     

Area-based tests for association between spatial patterns

 Edge effects pervade natural systems, and the processes that determine spatial heterogeneity (e.g. physical, geochemical, biological, ecological factors) occur on diverse spatial scales. Hence, tests for association between spatial patterns should be unbiased by edge effects and be based on null spatial models that incorporate the spatial heterogeneity characteristic of real-world systems. This paper develops probabilistic pattern association tests that are appropriate when edge effects are present, polygon size is heterogeneous, and the number of polygons varies from one classification to another. The tests are based on the amount of overlap between polygons in each of two partitions. Unweighted and area-weighted versions of the statistics are developed and verified using scenarios representing both polygon overlap and avoidance at different spatial scales and for different distributions of polygon sizes. These statistics were applied to Soda Butte Creek, Wyoming, to determine whether stream microhabitats, such as riffles, pools and glides, can be identified remotely using high spatial resolution hyperspectral imagery. These new “spatially explicit” techniques provide information and insights that cannot be obtained from the spectral information alone. Received 1 June 2001 / Accepted 25 October 2001     

空间数据模糊聚类的有效性(英文)

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.     

空间数据重复记录的清理方法研究

在空间数据的更新过程中,需要利用多种数据源对现有数据进行更新,这样可能会导致在获得的空间数据中出现重复记录。利用计算机自动对这些重复记录进行检查处理,将会极大地降低数据生产者的工作量。本文根据在数字制图系统研制过程中所遇到的此类问题,提出了对重复空间数据进行检查与处理的方法,并在所研制的数字制图系统中得到了应用。     

基于空间数据引擎的综合地下管线数据组织

在武汉市的综合地下管线建库工作中,数据组织是最为核心和关键的工作。我们结合实际情况,基于空间数据引擎制定了特有的、兼容性强的数据结构,并针对数据的特性进行了分层管理,建立了数据安全保障机制,充分发挥空间数据中间件ArcSDE的高性能,为数据库的安全、高效运行提供了坚实的保障。     

基于MicroStation的空间数据提取方法研究

目前MicroStation在测绘中的应用越来越为广泛,由于MicroStation不是专门为测绘行业设计的平台,对于测绘中的某些具体作业,还需要结合实际的需要加以改造,才能满足要求。本文主要针对空间数据提取在Mi-croStation系统中的实现进行分析,并介绍了具体的实现方法。