Methoden und Praxis der Raumanalyse: Beschreiben - Erkunden - Erklären durch GIS-

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Spatial Analysis Methods and Practice: Describe - Explore - Explain through GIS
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Book Title
Spatial Analysis Methods and Practice: Describe - Explore - Expla
Publication Date
2020-06-11
ISBN
9781108498982
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1108498981
ISBN-13
9781108498982
eBay Product ID (ePID)
17038903256

Product Key Features

Number of Pages
532 Pages
Language
English
Publication Name
Spatial Analysis Theory and Practice : Describe-Explore-Explain Through Gis
Publication Year
2020
Subject
Probability & Statistics / General, Remote Sensing & Geographic Information Systems
Type
Textbook
Subject Area
Mathematics, Technology & Engineering
Author
George Grekousis
Format
Hardcover

Dimensions

Item Height
1.2 in
Item Weight
41.3 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2019-038257
Reviews
'... An excellent course text for students of GIS, spatial statistics, quantitative geography, and ecology ... Essential reading for beginning students as well as those who wish to refresh their knowledge with respect to newer tools such as geographically weighted regression and spatial econometrics ... introduces spatial analysis to those with very little training in statistics while at the same time developing applications using standard software for spatial analysis based on the ArcGIS and Geoda software systems. An excellent primer for anyone following a full course in spatial analysis. Spatial analysis is a tough subject to teach but Grekousis guides the reader through the basic ideas about understanding how correlations define our geographic world, introducing the full range of spatial tools and models.' Michael Batty, Centre for Advanced Spatial Analysis (CASA), University College London
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
910.285
Table Of Content
1. Think spatially: basic concepts of spatial analysis and space conceptualization; 2. Exploratory spatial data analysis tools and statistics; 3. Analyzing geographic distributions and point patterns; 4. Spatial autocorrelation; 5. Multivariate data in geography: data reduction and clustering; 6. Modeling relationships: regression and geographically weighted regression; 7. Spatial econometrics.
Synopsis
This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences., This book presents an introductory overview of spatial data analysis methods and geoinformation analysis techniques. Each chapter introduces the related theory, explains how to interpret metrics outputs, and provides worked examples using ArcGIS and GeoDa. This is a valuable resource for graduate students and researchers analyzing geospatial data., This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: - Describing and mapping data through Exploratory Spatial Data Analysis; - Analyzing geographic distributions and point patterns; - Spatial autocorrelation; - Spatial clustering; - Geographically Weighted Regression and OLS regression; - Spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. - Exercises are solved twice: first through ArcGIS, and then GeoDa. - Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. - Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.
LC Classification Number
QA278.2.G737 2020

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