Spatial Regression Models For The Social Sciences
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| Author | : Guangqing Chi |
| Publisher | : SAGE Publications |
| Total Pages | : 229 |
| Release | : 2019-03-06 |
| Genre | : Social Science |
| ISBN | : 1544302053 |
Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
| Author | : David Darmofal |
| Publisher | : Cambridge University Press |
| Total Pages | : 263 |
| Release | : 2015-11-12 |
| Genre | : Mathematics |
| ISBN | : 0521888263 |
This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.
| Author | : Guangqing Chi |
| Publisher | : SAGE Publications |
| Total Pages | : 273 |
| Release | : 2019-03-06 |
| Genre | : Social Science |
| ISBN | : 1544302088 |
Space and geography are important aspects of social science research in fields such as criminology, sociology, political science, and public health. Many social scientists are interested in the spatial clustering of various behaviors and events. There has been a rapid development of interest in regression methods for analyzing spatial data over recent years, but little available on the topic that is aimed at graduate students and advanced undergraduate classes in the social sciences (most texts are for the natural sciences, or regional science, or economics, and require a good understanding of advanced statistics and probability theory). Spatial Regression Models for the Social Sciences fills the gap, and focuses on the methods that are commonly used by social scientists. Each spatial regression method is introduced in the same way. Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it, by connecting it to social science research topics. They try to avoid mathematical formulas and symbols as much as possible. Secondly, throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us. Spatial Regression Models for the Social Sciences provides comprehensive coverage of spatial regression methods for social scientists and introduces the methods in an easy-to-follow manner.
| Author | : Robert Nash Parker |
| Publisher | : Routledge |
| Total Pages | : 254 |
| Release | : 2009-09-10 |
| Genre | : Political Science |
| ISBN | : 1135857598 |
This is the first book to provide sociologists, criminologists, political scientists, and other social scientists with the methodological logic and techniques for doing spatial analysis in their chosen fields of inquiry. The book contains a wealth of examples as to why these techniques are worth doing, over and above conventional statistical techniques using SPSS or other statistical packages. GIS is a methodological and conceptual approach that allows for the linking together of spatial data, or data that is based on a physical space, with non-spatial data, which can be thought of as any data that contains no direct reference to physical locations.
| Author | : Robert P. Haining |
| Publisher | : Cambridge University Press |
| Total Pages | : 436 |
| Release | : 1993-08-26 |
| Genre | : Mathematics |
| ISBN | : 9780521448666 |
Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research workers and final year undergraduates in the fields of geography, environmental sciences and social sciences.
| Author | : Daniel Griffith |
| Publisher | : Academic Press |
| Total Pages | : 288 |
| Release | : 2019-09-14 |
| Genre | : Business & Economics |
| ISBN | : 0128156929 |
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 | : L. Anselin |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 295 |
| Release | : 2013-03-09 |
| Genre | : Business & Economics |
| ISBN | : 9401577994 |
Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.
| Author | : Harry Kelejian |
| Publisher | : Academic Press |
| Total Pages | : 460 |
| Release | : 2017-07-20 |
| Genre | : Business & Economics |
| ISBN | : 0128133929 |
Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentation with unusual depth of coverage. Introducing and formalizing the principles of, and 'need' for, models which define spatial interactions, the book provides a comprehensive framework for almost every major facet of modern science. Subjects covered at length include spatial regression models, weighting matrices, estimation procedures and the complications associated with their use. The work particularly focuses on models of uncertainty and estimation under various complications relating to model specifications, data problems, tests of hypotheses, along with systems and panel data extensions which are covered in exhaustive detail. Extensions discussing pre-test procedures and Bayesian methodologies are provided at length. Throughout, direct applications of spatial models are described in detail, with copious illustrative empirical examples demonstrating how readers might implement spatial analysis in research projects. Designed as a textbook and reference companion, every chapter concludes with a set of questions for formal or self--study. Finally, the book includes extensive supplementing information in a large sample theory in the R programming language that supports early career econometricians interested in the implementation of statistical procedures covered. - Combines advanced theoretical foundations with cutting-edge computational developments in R - Builds from solid foundations, to more sophisticated extensions that are intended to jumpstart research careers in spatial econometrics - Written by two of the most accomplished and extensively published econometricians working in the discipline - Describes fundamental principles intuitively, but without sacrificing rigor - Provides empirical illustrations for many spatial methods across diverse field - Emphasizes a modern treatment of the field using the generalized method of moments (GMM) approach - Explores sophisticated modern research methodologies, including pre-test procedures and Bayesian data analysis
| Author | : A. Stewart Fotheringham |
| Publisher | : John Wiley & Sons |
| Total Pages | : 282 |
| Release | : 2003-02-21 |
| Genre | : Science |
| ISBN | : 0470855258 |
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).
| Author | : Chris Brunsdon |
| Publisher | : SAGE |
| Total Pages | : 386 |
| Release | : 2014-04-30 |
| Genre | : Social Science |
| ISBN | : 1473911192 |
"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using ′out of the box′ software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical ′how to′ guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive ′how to′ that takes students - of any discipline - from coding to actual applications and uses of R.
