6 edition of Geomatic Methods for the Analysis of Data in the Earth Sciences (Lecture Notes in Earth Sciences) found in the catalog.
July 31, 2000
Written in English
|Contributions||Athanasios Dermanis (Editor), Armin Grün (Editor), Fernando Sanso (Editor)|
|The Physical Object|
|Number of Pages||256|
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Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics is the perfect companion for students who are learning to use SPSS® software to interpret and manage data within their social statistics and/or research methods courses. Both first-time and more experienced SPSS® users will appreciate author William E. Wagner, III’s step-by-step explanations of SPSS. Course Summary: Covers modern methods for analysis of repeated measures, correlated outcomes and longitudinal data. Topics: repeated measures ANOVA, random effects and growth curve models, generalized estimating equations (GEE) and generalized linear mixed models (GLMMs). Extensive use of statistical software, e.g. SAS, R.
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Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools. This book is an attempt to identify and rationally organize the statistical-mathematical methods which are common in many fields where geomatics is applied, like geodesy, geophysics and, in particular, the field of inverse problems and image analysis as it enters into.
Get this from a library. Geomatic methods for the analysis of data in the earth sciences. [Athanasios Dermanis; Armin Grün; F Sansò;] -- Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools.
This book is. ISBN: OCLC Number: Description: ix, pages: illustrations ; 24 cm. Contents: An overview of data analysis methods in geomatics / A. Dermanis, F. Sanso and A. Grun --Data analysis methods is geodesy / A. Dermanis and R. Rummel --Linear and nonlinear inverse problems / R.
Snieder and J. Trampert --Image Preprocessing for Feature Extraction in Digital. This book is an attempt to present the mathematical-statistical methods used in data analysis within various disciplines—geodesy, geophysics, photogrammetry and remote sensing—from a unifying perspective that inverse problem formalism permits.
Geomatic Methods for the Analysis of Data in the Earth Sciences: Lecture Notes in Earth Cited by: 3. try, etc.), spatial data analysis methods are also concerned with the questions of "what" and "where", i.e., the identification of the nature of an object of known posi. Cite this chapter as: Dermanis A., Sansò F., Grün A.
() An overview of data analysis methods in geomatics. In: Dermanis A., Grün A., Sansò F. (eds) Geomatic Method for the Analysis of Data in the Earth Sciences. Geomatic Methods for the Analysis of Data in the Earth Sciences. New York: Springer. ISBN Kavanagh, Barry F. Geomatics.
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First, in an examination of “techniques and data analysis”, we explored how frequently each geomatic approach and spatial data type were used in selected papers. Second, we conducted an “accuracy analysis”, which was.
This book presents an innovative approach to large geosciences data sets processing and interpretation and introduces geosciences applications that deal with monitoring of volcano activity, mineral deposits locations, earthquake-prone areas, magnetic storms and other natural extreme events. The objective of this book is also clear to see from the methods we have chosen and the subjects we address.
This book deals first and foremost with spatially explicit data that can be mapped. MATLAB Recipes for Earth Sciences introduces different methods of using MATLAB to perform data analysis in the geosciences.
These methods include basic statistics for univariate, bivariate and multivariate datasets; time-series analysis; signal processing; the analysis of spatial and directional data; and image analysis.
Geographic information system (GIS)-based predictive modelling is widely used in archaeology to identify suitable zones for ancient settlement locations and determine underlying factors of their distribution. In this study, we developed predictive models on Roman viticulture in the Laetanian Region (Hispania Citerior-Tarraconensis), using the location of 82 ancient wine-pressing facilities or.
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Fundamentals of data analysis for earth sciences with emphasis on practical applications using high-level computer programming languages: matrix algebra, least squares, Gaussian statistics, gridding, cartographic projections, spectral analysis. • To a large extent, earth sciences aim to inferring past processes and predicting future events based on relationships among attributes, preferably quantifying uncertainty.
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A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software.
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