基于Matlab的遥感图像融合效果的客观评价方法

Matlab是一种基于向量的高级程序语言,从本质上提供了对图像的支持。本文试图借助其图像处理工具箱函数的支持从工程和实验角度出发,探讨了利用Matlab语言实现遥感图像融合效果的客观评价指标的方法。文章首先将均值、标准差、信息熵和峰值信噪比等评价指标进行了分类、定义,然后设计融合实验来验证该方法的可行性和正确性,同时在Matlab平台上给出了各评价指标的计算过程及数值。实验结果表明,利用这些评价指标作出的客观评价结果与主观评价结果是一致的。     

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.     

Matlab在多元线性沉降数据处理中的应用

回归分析模型是处理变形监测数据应用最多的模型,但是其含有大的矩阵,解算烦琐,模型方程求解困难。然而,Matlab中包含有归回分析函数工具箱,可以方便地解算出回归系数,用于回归模型的建立,从而将该模型应用到工程中去。本文结合实际,对某高层建筑物的监测数据运用Matlab进行回归分析,得到拟合度很好的回归模型,可为工程的沉降提供预测,保证工程的安全顺利进行。     

Exploratory Analysis of Spatial Data Using Interactive Maps and Data Mining

We present new methods for analyzing geo-referenced statistical data. These methods combine visualization and direct manipulation techniques of exploratory data analysis and algorithms for data mining. The methods have been implemented by integrating two hitherto separate software tools: Descartes for interactive thematic mapping, and the data mining toolbox Kepler. In using these tools, data analysis may proceed as a steady interaction between visual inspiration and insights gained from mathematical–statistical calculations. After introducing the various components of the methods and tools, the paper guides the reader through in-depth examples of using the tools in the context of analysis of urban demographic data. In particular, it is shown how geography-based classifications of urban districts can be related to available thematic characteristics by applying the data mining algorithms classification tree derivation, attribute weighting, and subgroup discovery.     

使用Python函数式编程进行空间回归分析

针对使用传统的编程方式进行空间回归分析过于繁杂的问题,本文提出将Python函数式编程引入空间回归分析方法,分析和探讨脚本语言＋函数式编程方式在GIS数据分析中的推广应用前景。编程实践证明,采用Python函数式编程,结合既有的开源软件包,可以有效提高编程效率,减少代码量,将实践人员的注意力集中在问题本身。     

无线数字测图程序设计中的关键技术

在数字化测图软件中,对数据进行属性拓展和添加连接关系并对图面进行美观,是软件的主体机构,但如何将野外碎步点实时展绘到测图软件里形成空间属性数据,是关系到成图质量的关键.本文就如何在数字测图的过程中通过无线传输技术通讯全站仪,将全站仪传回的数据进行分析、验证,实时将所测点位展绘到测图软件中的关键技术进行讨论.     

Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

The extension of the functional capacity of geographic information systems (GIS) with tools for statistical analysis in general and exploratory spatial data analysis (ESDA) in particular has been an increasingly active area of research in recent years. In this paper, two operational implementations that combine the functionality of spatial data analysis software with a GIS are considered more closely. They consist of linkages between the S-PLUS software for data analysis and two different GIS implementations, the ArcView desktop system, which is mostly vector-oriented, and the primarily raster-based Grassland open GIS environment. We emphasize conceptual and technical issues related to the software implementation of these approaches and suggest future directions for linking spatial statistics and GIS. Received: 14 January 1999 / Accepted: 11 May 1999     

Embedding production rules in a GIS

A long-standing issue in GIS has been the need to provide support for more complex spatial models that have specialized Operations, but at the same time to take advantage of generic functionality provided by the GIS for visualizing, querying, and manipulating spatial features. Two approaches may be used to incorporate domain-specific knowledge in computer software, namely object-oriented programming and logic-based programming. Based upon a commonly used data model and object-oriented application framework available in modern GIS software, this paper explores integrating rules as a generic approach to building more meaningful spatial representations for land use and land control planning.     

无线数字测图程序设计中的关键技术

在数字化测图软件中,对数据进行属性拓展和添加连接关系并对图面编辑处理是软件的主要功能,但如何将野外碎步点实时展绘到测图软件里形成空间属性数据,是关系到成图质量的关键。本文就如何在数字测图的过程中通过无线传输技术通讯、驱动全站仪进行讨论。     

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).     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

Shapes on a plane: evaluating the impact of projection distortion on spatial binning

ABSTRACT

One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader’s ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion’s impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.     

Providing scientific visualization for spatial data analysis: Criteria and an assessment of SAGE

A consistent theme in recent work on developing exploratory spatial data analysis (ESDA) has been the importance attached to visualization techniques, particularly following the pioneering development of packages such as SPIDER and REGARD (Haslett et al. 1990).  The focus on visual techniques is often justified in two ways: (a) the power of modern graphical interfaces means that graphics is no longer a way of simply presenting results in the form of maps or graphs, but a tool for the extraction of information from data; (b) graphical, exploratory methods are felt to be more intuitive for non-specialists to use than methods of numerical spatial statistics enabling wider participation in the process of getting data insights. However, little work has been done to decide which visualization tools might be needed to support ESDA, or how they can be most effectively implemented.  This paper briefly reviews a theoretical framework that has been suggested for addressing these issues, which comprises two elements: firstly a data model, based on the distinction between rough and smooth properties of spatial data, that defines what an analyst is looking for in data (Haining et al. 1998) and secondly a theoretical model for assessing the quality of visualisation tools (Cleveland 1994). The emphasis of this paper is the use of the theoretical framework to structure an assessment of SAGE, a software system that has been written for the spatial statistical analysis (including both exploratory and confirmatory data analysis) of area based data linked to a GIS. The aim of the assessment is to identify the desirable features of the system (that might be employed in other systems) whilst also highlighting what the additional requirements are thereby contributing to the development of systems for ESDA that contain good quality scientific visualization tools for exploratory spatial data analysis. Received: 30 September 1998/Accepted: 17 September 1999     

Matlab.NET混合编程在潮汐调和分析与预报中的应用

利用.NET中C#强大的界面开发能力和经典的Matlab潮汐调和分析程序包T_TIDE,采用Matlab.NET混合编程技术,创建潮汐调和常数计算THCCalculate.dll和潮汐预报Tide Forecast.dll,在主程序中调用对应的函数,实现了操作简单且具有良好用户界面的潮汐调和分析与潮位预报系统,具有潮位数据读取、调和分析和预报、数据绘制等功能。结果表明:使用混合编程技术能充分发挥C#与Matlab各自的特点,较好地提高程序开发和运行效率,具有较广泛的应用范围。     

结合遥感数据与地统计学方法的海岸线超分辨率制图

使用黄河三角洲海岸Landsat卫星遥感数据,基于研究区域低分辨率6波段的海陆类型软分类结果及其变差函数,以高分辨率8波段的指示变差函数为精细尺度先验信息模型,采用数据探索性分析、协同指示克里格和序贯指示协同模拟技术,生成海陆类型发生概率模拟图像,通过等值线法提取海岸线空间分布特征。实验表明,基于地统计学方法的超分辨率制图技术在低分辨率遥感数据中融合高分辨率空间结构先验模型,可以较好表达精细尺度上的海岸线空间分布特征,同时保持原始数据的海陆类型组分信息及其空间结构特征。地统计学方法集成多尺度乃至多源空间信息的潜力通过海岸线超分辨率制图形式得到展示。     

An open source,server-side framework for analytical web mapping and its application to health

In this paper, we detail the design and the implementation of an open source, server-side web mapping framework for the analysis of health data. The framework forms part of a larger project, the goal of which is to provide an analytical web geographical information system (GIS) that enables health experts to analyse spatial aspects of health data. The aim of the framework is to provide a method for the dynamic and flexible spatial visualisation of health data to facilitate data exploration and analysis. Consequently, a dynamic thematic web mapping technique, an extension to the Open Geospatial Consortium (OGC) web map service standard, was developed. The technique combines a data query, processing technique and styling methodology on the fly to generate a thematic map. The resulting thematic map represents a virtual map layer that enables a user to rapidly visually summarise properties of a data-set. A test web interface was developed to assess the efficacy of the web mapping technique. As the dynamic web mapping method builds on existing OGC web mapping standards, it can be readily integrated with the existing lightweight slippy map web clients and virtual globes.     

基于GIS与空间统计分析的可持续发展度量方法研究——以缅甸Myingyan District为例

利用空间信息系统所提供的强大空间数据处理和分析能力，并将之与统计分析软件包的统计分析功能进行有效的集成，建立了基于空间统计分析的可持续发展定量分析评价模型（ＳＢＳＡ）。从在缅甸中部Myingyan县的应用实例来看，该模型能够提示影响Myingyan县可持续发展能力的主导因子，通过对这些因子和可持续发展综合指数的定量化、空间化的分析，为政府部门规划区域综合发展方案，制定发展政策提供很好的决策支持。     

Matlab在测量数据处理教学中的应用

分析了测量数据处理的特点和当前教学中存在的不足,介绍了强大的工程计算软件Matlab在辅助测绘数据处理教学中的优势;结合测量数据处理的教学实践,从抽象内容直观化、复杂计算简单化、经典实验打基础和案例分析练能力等4个方面,探讨了Matlab在测量专业数据处理教学改革方面的应用问题。     

Impacts of Positional Error on Spatial Regression Analysis: A Case Study of Address Locations in Syracuse, New York

Positional error is the error produced by the discrepancy between reference and recorded locations. In urban landscapes, locations typically are obtained from global positioning systems or geocoding software. Although these technologies have improved the locational accuracy of georeferenced data, they are not error free. This error affects results of any spatial statistical analysis performed with a georeferenced dataset. In this paper we discuss the properties of positional error in an address matching exercise and the allocation of point locations to census geography units. We focus on the error's spatial structure, and more particularly on impacts of error propagation in spatial regression analysis. For this purpose we use two geocoding sources, we briefly describe the magnitude and the nature of their discrepancies, and we evaluate the consequences that this type of locational error has on a spatial regression analysis of pediatric blood lead data for Syracuse, NY. Our findings include: (1) the confirmation of the recurrence of spatial clustering in positional error at various geographic resolutions; and, (2) the identification of a noticeable but not shockingly large impact from positional error propagation in spatial auto‐binomial regression analysis results for the dataset analyzed.