Spatial Regression Analysis Using Eigenvector Spatial Filtering PDF Download
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Author: Daniel Griffith
Publisher: Academic Press
ISBN: 0128156929
Category : Business & Economics
Languages : en
Pages : 286
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Book Description
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models Includes computer code and template datasets for further modeling Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Author: Daniel Griffith
Publisher: Academic Press
ISBN: 0128156929
Category : Business & Economics
Languages : en
Pages : 286
Get Book Here
Book Description
Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models Includes computer code and template datasets for further modeling Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Author: Griffith, Daniel A.
Publisher: Edward Elgar Publishing
ISBN: 1800372825
Category : Social Science
Languages : en
Pages : 125
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Book Description
This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.
Author: Daniel A. Griffith
Publisher: Springer Science & Business Media
ISBN: 3540248064
Category : Science
Languages : en
Pages : 261
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Book Description
Scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. The text introduces a nonverbal model to subdisciplines that until now has mostly employed mathematical or verbal-conceptual models. The focus is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed. In doing so, the concept known as spatial autocorrelation - which characterizes these tendencies - is further demystified.
Author: Yongwan Chun
Publisher: SAGE
ISBN: 1446291626
Category : Reference
Languages : en
Pages : 202
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Book Description
"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in: spatial sampling spatial autocorrelation local statistics spatial interpolation in two-dimensions advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty. It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning. A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.
Author: Hajime Seya
Publisher:
ISBN:
Category :
Languages : en
Pages : 27
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Book Description
Eigenvector based spatial filtering is one of the well-used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the forward stepwise model selection procedure, but it is disappointingly slow when the number of observations n takes a large number. Hence as a complement or alternative, the present paper proposes the use of the LASSO (L1-penalized regression) to select the eigenvectors. The LASSO model selection procedure is applied to the well-known Boston housing dataset and simulation dataset, and its performance is compared with that of the stepwise procedure. The obtained results suggest that the LASSO is fairly fast compared the stepwise procedure, and can select eigenvectors effectively even if dataset is relatively large (n = 10000), to which the forward stepwise procedure is uneasy to apply.
Author: Daniel A. Griffith
Publisher: Springer
ISBN: 331972553X
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II). The principal concept is morphism (e.g., isomorphisms, homomorphisms, and allomorphisms), which is defined as a structure preserving the functional linkage between mathematical properties or operations in spatial statistics and spatial econometrics, among other disciplines. The purpose of this book is to present selected conceptions in both domains that are structurally the same, even though their labelling and the notation for their elements may differ. As the approaches presented here are applied to empirical materials in geography and economics, the book will also be of interest to scholars of regional science, quantitative geography and the geospatial sciences. It is a follow-up to the book “Non-standard Spatial Statistics and Spatial Econometrics” by the same authors, which was published by Springer in 2011.
Author: Manfred M. Fischer
Publisher: Springer Science & Business Media
ISBN: 3642217206
Category : Business & Economics
Languages : en
Pages : 85
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Book Description
The availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.
Author: Yoshiki Yamagata
Publisher: Academic Press
ISBN: 0128131322
Category : Business & Economics
Languages : en
Pages : 302
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Book Description
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics
Author: Manfred M. Fischer
Publisher: Springer Science & Business Media
ISBN: 3642036473
Category : Business & Economics
Languages : en
Pages : 801
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Book Description
The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.
Author: Sarah A. Lovell
Publisher: Taylor & Francis
ISBN: 1000636607
Category : Science
Languages : en
Pages : 451
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Book Description
The Routledge Handbook of Methodologies in Human Geography is the defining reference for academics and postgraduate students seeking an advanced understanding of the debates, methodological developments and methods transforming research in human geography. Divided into three sections, Part I reviews how the methods of contemporary human geography reflect the changing intellectual history of human geography and events both within human geography and society in general. In Part II, authors critically appraise key methodological and theoretical challenges and opportunities that are shaping contemporary research in various parts of human geography. Contemporary directions within the discipline are elaborated on by established and emerging researchers who are leading ontological debates and the adoption of innovative methods in geographic research. In Part III, authors explore cross-cutting methodological challenges and prompt questions about the values and goals underpinning geographical research work, such as: Who are we engaging in our research? Who is our research ‘for’? What are our relationships with communities? Contributors emphasize examples from their research and the research of others to reflect the fluid, emotional and pragmatic realities of research. This handbook captures key methodological developments and disciplinary influences emerging from the various sub-disciplines of human geography.
