ArcGIS 9: Using ArcGIS Geostatistical Analyst
Esri Press, 2004 - Computers - 300 pages
ESRI(R) ArcGIS(TM) Geostatistical Analyst is one of the available extensions to the ArcGIS(TM) Desktop products-- ArcInfo(TM), ArcEditor(TM), and ArcView(R). ArcGIS Geostatistical Analyst enables users to predict the magnitude or quantity of some phenomena across, above, or below a landscape. Predictions of such phenomena are made using measured sample points taken at various locations in the study site. ArcGIS Geostatistical Analyst creates the most accurate continuous surface possible from those measured samples, allowing an individual or team to make more effective decisions.
You will learn how to:
Represent your data.
Explore your data and determine what trends may exist.
Choose and fit a model such as kriging, cokriging, IDW, and others.
Perform diagnostic tests on your data.
Compare the results of different models.
Begin by following the quick-start tutorial to get an overview of how to perform the basics with ArcGIS Geostatistical Analyst. If you prefer, jump right in and experiment on your own. When you have questions, you will find concise, step-by-step answers inside, fully illustrated, to help you complete a task.
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GeostatsUser Review - gogogadgetgators - Overstock.com
This book is a very easy to understand manual for this extension. I used it in conjunction with a class I took and it helped immensely on my final project! I would like to see more help with ... Read full review
O nte nts 1 Welcome to ArcGlS Geostatistical Analyst
The principles of geostatistical analysis
Exploratory Spatial Data Analysis
Deterministic methods for spatial interpolation
Creating a surface with geostatistical techniques
________ ___ k
Using analytical tools when generating surfaces
Assessing decision protocol using validation
Using transfonnations log BoxCox and arcsine
Implementing declustering to adjust for preferential sampling 21 1
Displaying and managing geostatistical layers
Additional geostatistical analysis tools
Modeling semivariograms and covariance functions
Determining the neighborhood search size
Performing crossvalidation and validation