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Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
 
 
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Geographically Weighted Regression: The Analysis of Spatially Varying Relationships [Hardcover]

A. Stewart Fotheringham (Author), Chris Brunsdon (Author), Martin Charlton (Author)
2.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

October 21, 2002 0471496162 978-0471496168 1
Geographical Weighted Regression (GWR) is a new local modelling technique for analysing spatial analysis. This technique allows local as opposed to global models of relationships to be measured and mapped. This is the first and only book on this technique, offering comprehensive coverage on this new 'hot' topic in spatial analysis.


* Provides step-by-step examples of how to use the GWR model using data sets and examples on issues such as house price determinants, educational attainment levels and school performance statistics
* Contains a broad discussion of and basic concepts on GWR through to ideas on statistical inference for GWR models
* uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details).

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Geographically Weighted Regression: The Analysis of Spatially Varying Relationships + Quantitative Geography: Perspectives on Spatial Data Analysis + ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics
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Editorial Reviews

Review

"...this excellent volume..." (Geomatics World, July/August 2003)

From the Back Cover

Geographically Weighted Regression: The Analysis of Spatially Varying Relationships is based on the premise that relationships between variables measured at different locations might not be constant over space. The prevailing assumption is that such relationships are constant, an assumption that would appear to be the result of convenience rather than of any serious examination of the issues. If relationships do vary significantly over space, then serious questions are raised about the reliability of traditional, global-level analyses.

Geographically Weighted Regression, as part of a broader research area in local modelling, provides a new analytical tool and a different perspective on spatial analysis. Instead of being restricted to simple global analyses in which interesting local variations in relationships are 'averaged away' and unobservable, GWR allows local relationships to be measured and mapped. In many ways the output from GWR is similar to that presented by a microscope: previously unimagined detail suddenly comes into focus. This book challenges many of the global statements of spatial relationships that have been made in the academic literature.

Geographically Weighted Regression: The Analysis of Spatially Varying Relationships contains a broad discussion of local models in general and of the details of GWR, and provides many empirical examples on issues such as house price determinants, educational attainment levels and school performance statistics. A unique accompanying feature of this book is the author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end. This software is readily available from the authors and notes on using the software and an example application are documented in the book itself.

Geographically Weighted Regression: The Analysis of Spatially Varying Relationships is an essential resource for quantitative spatial analysts and GIS researchers and students. It will be of interest to researchers in any discipline in which spatial data are used across the broad spectrum of social sciences, medicine, science and engineering. The underlying message is that locality is important and measuring local relationships is vital to understanding spatial processes.

'Stewart Fotheringham and his colleagues have produced a book that will be widely used by geographers and others interested in spatial analysis. Geographically weighted regression is an important method, and the authors have developed and explained it well.' Peter Rogerson, Department of Geography, University at Buffalo, USA

'The realisation that almost any statistic can be made 'local', and that mapping the results almost always leads to greater insight is powering a revolution in spatial analysis. In particular, the localisation of standard regression models, or GWR, has led to important and powerful insights. This book, written by the team that has done most to develop it, makes this approach accessible for the first time under a single cover. It should be required reading for anyone involved with the analysis of spatially referenced data.' David Unwin, School of Geography, Birkbeck College London


Product Details

  • Hardcover: 282 pages
  • Publisher: Wiley; 1 edition (October 21, 2002)
  • Language: English
  • ISBN-10: 0471496162
  • ISBN-13: 978-0471496168
  • Product Dimensions: 9.7 x 6.5 x 0.8 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 2.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #507,601 in Books (See Top 100 in Books)

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12 of 13 people found the following review helpful:
2.0 out of 5 stars Software does not come with the book - must pay extra, January 26, 2008
By 
This review is from: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Hardcover)
The book is well organized, with many useful maps and diagrams so that those not too keen on mathematical formulas can understand what is being explained. I am not a statistician, and I was able to read and understand the text. And GWR is a most wonderful advancement, I am very excited about using it for my research. I would give this book five stars if it was not for my feeling of being mislead by the Amazon's review and the book itself into believing that buying the book would give me access to the software, and all the time I have been wasting with waiting for the book to arrive, exchanging e-mails with the author, etc.

Amazon's editorial review / book description states "uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details)".

It happens that *see book for details* refers to getting the software, because the software DOES NOT come with the book. The details are on a footnote on page 207: "The software is made available to users on condition that it is not used for commercial purposes (...) A small charge is made to cover the costs of packing and postage".

Well, Dr. Fotheringham told me through e-mail that, despite having bought the book and being an academic user, I still have to pay 55 euros to get the software.

I had read the book already, and bought it simply to have access to the software, so I am very displeased to be forced to pay 55 euros on top of the price of the book to have a software that is said to be accompanying it.

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Inside This Book (learn more)
First Sentence:
Imagine reading a book on the climate of the United States which contained only data averaged across the whole country, such as mean annual rainfall, mean annual number of hours of sunshine, and so forth. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
local parameter estimates, flatted properties, local standard errors, geographical weighting, global parameter estimate, global regression model, house price data, geographically weighted regression, local spatial autocorrelation, spatial nonstationarity, spatial regression models, varying parameter estimates, aspatial data, spatial kernel, regression point, hedonic price model, negative spatial autocorrelation, relationships being examined, positive spatial autocorrelation, areal unit problem, autocorrelated error terms, terraced properties, census ward, weighted statistics, local statistics
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Non-stationary Non-stationary Stationary Stationary, Akaike Information Criterion, Nationwide Building Society, United States, Census of Population, Golden Section, File Folder, Model Listing File, Tower Hamlets, Waltham Forest
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