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Spatial Regression Analysis Using Eigenvector Spatial Filtering


Spatial Regression Analysis Using Eigenvector Spatial Filtering
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Spatial Regression Analysis Using Eigenvector Spatial Filtering


Spatial Regression Analysis Using Eigenvector Spatial Filtering
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Author : Daniel Griffith
language : en
Publisher: Academic Press
Release Date : 2019-09-14

Spatial Regression Analysis Using Eigenvector Spatial Filtering written by Daniel Griffith and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-14 with Business & Economics categories.


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



Spatial Autocorrelation And Spatial Filtering


Spatial Autocorrelation And Spatial Filtering
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Author : Daniel A. Griffith
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-19

Spatial Autocorrelation And Spatial Filtering written by Daniel A. Griffith and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-19 with Science categories.


Exploiting the old maxim that "a picture is worth a thousand words," scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. It introduces a nonverbal model into subdisciplines that hitherto employed mostly or only mathematical or verbal-conceptual models. The focus of this monograph 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, affording spatial data analyses that are better understood, presented, and used. In doing so, the concept known as spatial autocorrelation - which characterizes these tendencies and is one of the key features of georeferenced data, or data tagged to the earth's surface - is further de-mystified. This self-correlation arises from relative locations in geographic space.



Spatial Data Analysis With R


Spatial Data Analysis With R
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Author : Danlin Yu
language : en
Publisher: SAGE Publications
Release Date : 2025-02-27

Spatial Data Analysis With R written by Danlin Yu and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Social Science categories.


This is an introduction for social science students to the growing field of spatial data analysis using the R platform. The text assumes no prior knowledge of either, beyond the contents of an introductory statistics course. It uses the open-source software R, and relevant spatial data analysis packages, to provide practical guidance of how to conduct spatial data analysis with readers′ own data sets. The book first briefly introduces students to R, covers some basic concepts in statistical data analysis, and then focuses on discussing the central ideas of spatial data analysis. All the discussions are supported with R scripts so that students can work on their own and produce results that the book helps interpret. Each chapter ends with review questions to test understanding. The book is suited for upper-level undergraduate social science students and graduate students, and other social scientists who are interested in analyzing their spatial data with R. A companion website for the book at https://edge.sagepub.com/yu includes R code and data for students to replicate the examples in the book. The password-protected instructor side of the site includes exercises and answers which can be set for homework.



Spatial Econometrics


Spatial Econometrics
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Author : Giuseppe Arbia
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-14

Spatial Econometrics written by Giuseppe Arbia and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-14 with Business & Economics categories.


Spatial Econometrics is a rapidly evolving field born from the joint efforts of economists, statisticians, econometricians and regional scientists. The book provides the reader with a broad view of the topic by including both methodological and application papers. Indeed the application papers relate to a number of diverse scientific fields ranging from hedonic models of house pricing to demography, from health care to regional economics, from the analysis of R&D spillovers to the study of retail market spatial characteristics. Particular emphasis is given to regional economic applications of spatial econometrics methods with a number of contributions specifically focused on the spatial concentration of economic activities and agglomeration, regional paths of economic growth, regional convergence of income and productivity and the evolution of regional employment. Most of the papers appearing in this book were solicited from the International Workshop on Spatial Econometrics and Statistics held in Rome (Italy) in 2006.



Spatial Analysis Using Big Data


Spatial Analysis Using Big Data
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Author : Yoshiki Yamagata
language : en
Publisher: Academic Press
Release Date : 2019-11-03

Spatial Analysis Using Big Data written by Yoshiki Yamagata and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-03 with Business & Economics categories.


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



The Routledge Handbook Of Methodologies In Human Geography


The Routledge Handbook Of Methodologies In Human Geography
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Author : Sarah A. Lovell
language : en
Publisher: Taylor & Francis
Release Date : 2022-10-18

The Routledge Handbook Of Methodologies In Human Geography written by Sarah A. Lovell and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-18 with Science categories.


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.



Applied Spatial Data Analysis With R


Applied Spatial Data Analysis With R
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Author : Roger S. Bivand
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-21

Applied Spatial Data Analysis With R written by Roger S. Bivand and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-21 with Medical categories.


Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.



Advances In Geocomputation


Advances In Geocomputation
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Author : Daniel A. Griffith
language : en
Publisher: Springer
Release Date : 2017-01-03

Advances In Geocomputation written by Daniel A. Griffith and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-03 with Science categories.


This book contains refereed papers from the 13th International Conference on GeoComputation held at the University of Texas, Dallas, May 20-23, 2015. Since 1996, the members of the GeoComputation (the art and science of solving complex spatial problems with computers) community have joined together to develop a series of conferences in the United Kingdom, New Zealand, Australia, Ireland and the United States of America. The conference encourages diverse topics related to novel methodologies and technologies to enrich the future development of GeoComputation research.



Advanced Introduction To Spatial Statistics


Advanced Introduction To Spatial Statistics
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Author : Griffith, Daniel A.
language : en
Publisher: Edward Elgar Publishing
Release Date : 2022-08-12

Advanced Introduction To Spatial Statistics written by Griffith, Daniel A. and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-12 with Social Science categories.


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.