Variowin

Software for Spatial Data Analysis in 2D
Softcover Nachdruck of the original 1. Auflage 1996
ISBN: 978-1-4612-7525-1
Verlag: Springer

Buch, Englisch, 91 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 172 g

Reihe: Statistics and Computing

1.1 Overview V ARIOWIN 2.2 is a collection of four Windows™ programs - Prevar2D, Vari02D with PCF, Model, and Grid Display - that are used for spatial data analysis and variogram modeling of irregularly spaced data in two dimensions. Prevar2D builds a pair comparison file (PCF), that is, a binary file containing pairs of data sorted in terms of increasing distance. Pair comparison files can be built from subsets in order to reduce memory requirements. Vari02D with PCF is used for spatial data analysis of 2D data. It uses an ASCII data file and a binary pair comparison file produced by Prevar2D. Features implemented in Vari02D with PCF include: • the possibility to characterize the spatial continuity of one variable or the joined spatial continuity of two variables, • variogram surfaces for identifying directions of anisotropies, • directional variograms calculated along any direction, • several measures of spatial continuity. Not only the variogram but also the standardized variogram, the covariance, the correlogram, and the madogram are used to measure spatial continuity. • h-scatterplots to assess the meaning of these measures, • the identification and localization of pairs of data adversely affecting the measure of spatial continuity. Once identified, these pairs can be masked from the calculation interactively. • variogram clouds for identifying pairs of data values having the most influence on the measure of spatial continuity. Those pairs can also be located on the sample map.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Pannatier, Yvan

Fachgebiete

• Geowissenschaften Geographie | Raumplanung Geostatistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

1 Introduction.- 1.1 Overview.- 1.2 History.- 1.3 Warning: This Is a Methodological User’s Guide.- 1.4 Notation Used Within This User’s Guide.- 1.5 Minimum Requirements.- 1.6 Content of the Release.- 1.7 Installation Procedure.- 1.8 Uninstallation Procedure.- Acknowledgments.- References.- 2 Quick Start.- 2.1 Prevar2D — Construction of a Pair Comparison File.- 2.2 Vario2D with PCF — Exploratory Variography.- 2.3 Model — Interactive Variogram Modeling.- 2.4 Grid Display — Displaying Grid Files as Pixel Maps.- 3 Construction of a Pair Comparison File (PCF) with Prevar2D.- 3.1 Overview.- 3.2 What Is a PCF?.- 3.3 Working with a PCF.- 3.4 Building a PCF from a Subset.- 3.5 What Should Be Done with Duplicate Data Points?.- Further Reading.- 4 Vario2D with PCF — A Program for Interactive Exploratory Variography.- 4.1 Overview.- 4.2 Working with Subsets.- 4.3 H-Scatterplots and Cross H-Scatterplots.- 4.4 Variogram Surface and Cross Variogram Surface.- 4.5 Directional Variogram and Directional Cross Variogram.- 4.6 Variogram Cloud and Cross Variogram Cloud.- 4.7 Measures of Spatial Continuity Available Within Vario2D with PCF.- Further Reading.- 5 Model — Interactive Variogram Modeling.- 5.1 Overview.- 5.2 Variogram Models.- 5.3 Interactive Construction of a 2D Nested Model.- 5.4 The Linear Model of Coregionalization.- Further Reading.- 6 Files Used Within VARIOWIN 2.2.- 6.1 Data Files (.DAT).- 6.2 Pair Comparison Files (.PCF).- 6.3 Grid Files (.GRD).- 6.4 Variogram Surface Files (.VS).- 6.5 Variogram Files (.VAR).- 6.6 Variogram Cloud Files (.CLD).- 6.7 Model Files (.MOD).- Further Reading.- Appendix A Geostatistical Concepts.- A.1 Random Variables, Regionalized Variables, and Random Functions.- A.2 Moments Considered in Linear Geostatistics.- A.2.1 First-OrderMoment — Mathematical Expectation.- A.2.2 Second-Order Moments.- A.3 Ergodicity.- A.4 Hypothesis of Stationarity.- A.4.1 Strict Stationarity.- A.4.2 Second-Order Stationarity.- A.4.3 Intrinsic Hypothesis.- A.5 Why Do We Need to Model Variograms?.- A.6 The Multivariate Gaussian Random Function.- Further Reading.