Spatial Regression Analysis Using Eigenvector Spatial Filtering

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

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  • Author : Daniel Griffith
  • Publisher : Academic Press
  • Pages : 286 pages
  • ISBN : 0128156929
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKSpatial Regression Analysis Using Eigenvector Spatial Filtering

Spatial Regression Analysis Using Eigenvector Spatial Filtering

Spatial Regression Analysis Using Eigenvector Spatial Filtering
  • Author : Daniel Griffith,Yongwan Chun,Bin Li
  • Publisher : Academic Press
  • Release : 14 September 2019
GET THIS BOOKSpatial Regression Analysis Using Eigenvector Spatial Filtering

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)

Spatial Autocorrelation and Spatial Filtering

Spatial Autocorrelation and Spatial Filtering
  • Author : Daniel A. Griffith
  • Publisher : Springer Science & Business Media
  • Release : 19 March 2013
GET THIS BOOKSpatial Autocorrelation and Spatial Filtering

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

Encyclopedia of GIS

Encyclopedia of GIS
  • Author : Shashi Shekhar,Hui Xiong
  • Publisher : Springer Science & Business Media
  • Release : 12 December 2007
GET THIS BOOKEncyclopedia of GIS

The Encyclopedia of GIS provides a comprehensive and authoritative guide, contributed by experts and peer-reviewed for accuracy, and alphabetically arranged for convenient access. The entries explain key software and processes used by geographers and computational scientists. Major overviews are provided for nearly 200 topics: Geoinformatics, Spatial Cognition, and Location-Based Services and more. Shorter entries define specific terms and concepts. The reference will be published as a print volume with abundant black and white art, and simultaneously as an XML online reference

Spatial Statistics and Geostatistics

Spatial Statistics and Geostatistics
  • Author : Yongwan Chun,Daniel A Griffith
  • Publisher : SAGE
  • Release : 11 January 2013
GET THIS BOOKSpatial Statistics and Geostatistics

"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

Spatial Analysis Using Big Data

Spatial Analysis Using Big Data
  • Author : Yoshiki Yamagata,Hajime Seya
  • Publisher : Academic Press
  • Release : 03 November 2019
GET THIS BOOKSpatial Analysis Using Big Data

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

Advances in Geocomputation

Advances in Geocomputation
  • Author : Daniel A. Griffith,Yongwan Chun,Denis J. Dean
  • Publisher : Springer
  • Release : 03 January 2017
GET THIS BOOKAdvances in Geocomputation

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

Handbook of Applied Spatial Analysis

Handbook of Applied Spatial Analysis
  • Author : Manfred M. Fischer,Arthur Getis
  • Publisher : Springer Science & Business Media
  • Release : 24 December 2009
GET THIS BOOKHandbook of Applied Spatial Analysis

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

Applied Spatial Data Analysis with R

Applied Spatial Data Analysis with R
  • Author : Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
  • Publisher : Springer Science & Business Media
  • Release : 21 June 2013
GET THIS BOOKApplied Spatial Data Analysis with R

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

Statistical Learning with Sparsity

Statistical Learning with Sparsity
  • Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
  • Publisher : CRC Press
  • Release : 07 May 2015
GET THIS BOOKStatistical Learning with Sparsity

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm

Introduction to Spatial Econometrics

Introduction to Spatial Econometrics
  • Author : James LeSage,Robert Kelley Pace
  • Publisher : CRC Press
  • Release : 20 January 2009
GET THIS BOOKIntroduction to Spatial Econometrics

Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances. Leaders in this field,

Handbook of Regional Science

Handbook of Regional Science
  • Author : Manfred M. Fischer,Peter Nijkamp
  • Publisher : Springer
  • Release : 17 September 2013
GET THIS BOOKHandbook of Regional Science

The Handbook of Regional Science is a multi-volume reference work providing a state-of-the-art knowledge on regional science composed by renowned scientists in the field. The Handbook is intended to serve the academic needs of graduate students, and junior and senior scientists in regional science and related fields, with an interest in studying local and regional socio-economic issues. The multi-volume handbook seeks to cover the field of regional science comprehensively, including areas such as regional housing and labor markets, regional economic