Daniel A. Griffith - Böcker

This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers, regional scientists, applied spatial statisticians, and spatial scientists from across disciplines.

Uncertainty and context pose fundamental challenges in GIScience and geographic research. Geospatial data are imbued with errors (e.g., measurement and sampling) and various types of uncertainty that often obfuscate any understanding of the effects of contextual or environmental influences on human behaviors and experiences. These errors or uncertainties include those attributable to geospatial data measurement, model specifications, delineations of geographic context in space and time, and the use of different spatiotemporal scales and zonal schemes when analyzing the effects of environmental influences on human behaviors or experiences. In addition, emerging sources of geospatial big data – including smartphone data, data collected by GPS, and various types of wearable sensors (e.g., accelerometers and air pollutant monitors), volunteered geographic information, and/ or location- based social media data (i.e., crowd- sourced geographic information) – inevitably contain errors, and their quality cannot be fully controlled during their collection or production.Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data illustrates how cutting- edge research explores recent advances in this area, and will serve as a useful point of departure for GIScientists to conceive new approaches and solutions for addressing these challenges in future research. The seven core chapters in this book highlight many challenges and opportunities in confronting various issues of uncertainty and context in GIScience and geography, tackling different topics and approaches.The chapters in this book were originally published as a special issue of the International Journal of Geographical Information Science.

Uncertainty and context pose fundamental challenges in GIScience and geographic research. Geospatial data are imbued with errors (e.g., measurement and sampling) and various types of uncertainty that often obfuscate any understanding of the effects of contextual or environmental influences on human behaviors and experiences. These errors or uncertainties include those attributable to geospatial data measurement, model specifications, delineations of geographic context in space and time, and the use of different spatiotemporal scales and zonal schemes when analyzing the effects of environmental influences on human behaviors or experiences. In addition, emerging sources of geospatial big data – including smartphone data, data collected by GPS, and various types of wearable sensors (e.g., accelerometers and air pollutant monitors), volunteered geographic information, and/ or location- based social media data (i.e., crowd- sourced geographic information) – inevitably contain errors, and their quality cannot be fully controlled during their collection or production.Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data illustrates how cutting- edge research explores recent advances in this area, and will serve as a useful point of departure for GIScientists to conceive new approaches and solutions for addressing these challenges in future research. The seven core chapters in this book highlight many challenges and opportunities in confronting various issues of uncertainty and context in GIScience and geography, tackling different topics and approaches.The chapters in this book were originally published as a special issue of the International Journal of Geographical Information Science.

This volume is in honour of the career of the father of spatial econometrics, Professor Jean Paelinck, presently of the Tinbergen Institute, Rotterdam. Jean Paelinck is arguably the founder of modern spatial econometrics. The impact on the profession through his work in spatial econometrics, regional science, and more conventional economics can be measured in many ways: through the work of his students; his devotion to and activism in facilitating the diffusion of regional science to Poland; the formulation and development of his FLEUR model; his co-founding of the French-speaking Regional Science Association; the voluminous references to his scholarly publications; his many invitations to be a featured speaker at conferences and universities throughout the world; the offices he has held in scholarly and professional associations, Erasmus University Rotterdam and the Netherlands Economic Institute; and the numerous honorary degrees he has been awarded. A series of special sessions in honour of Jean Paelinck were organized at the most prominent regional science meetings around the world.A number of prominent scholars in the field organized and participated in special sessions labelled "In Honour of Professor Paelinck". These sessions reflect a global reach of the techniques and methods pioneered by him. As an outgrowth of six conferences, final versions of the selection of papers are collected in this volume. Prominent ideas contained in each of the selected contributions can be traced to work by Jean Paelinck.

This volume is in honour of the remarkable career of the Father of Spatial Econometrics, Professor Jean Paelinck, presently of the Tinbergen Institute, Rotterdam. Jean Paelinck, arguably, is the founder of modern spatial econometrics. The impact on the profession through his work in spatial econometrics, regional science, and more conventional economics can be measured in many ways: through the work of his students, his devotion to and activism in facilitating the diffusion of regional science to Poland, the formulation and development of his FLEUR model, his co-founding of the French-speaking Regional Science Association, the voluminous references to his scholarly publications, his many invitations to be a featured speaker at conferences and universities throughout the world, the offices he has held in scholarly and professional associations, Erasmus University Rotterdam and the Netherlands Economic Institute, and the numerous honorary degrees he has been awarded. A series of special sessions in honour of Jean Paelinck were organized at the most prominent regional science meetings around the world.A number of prominent scholars in the field organized and participated in special sessions labelled 'In Honour of Professor Paelinck.' These sessions reflect a truly global reach of the techniques and methods pioneered by him. As an outgrowth of six conferences final versions of the selection of papers are collected in this volume. Prominent ideas contained in each of the selected contributions can be traced explicitly to work by Jean Paelinck.

"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 LondonFocusing 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 samplingspatial autocorrelationlocal statisticsspatial interpolation in two-dimensionsadvanced 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.

"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 LondonFocusing 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 samplingspatial autocorrelationlocal statisticsspatial interpolation in two-dimensionsadvanced 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.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.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.Key Features:A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysisContemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniquesA detailed articulation of a spatial statistical workflow conceptualizationThe helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.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.Key Features:A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysisContemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniquesA detailed articulation of a spatial statistical workflow conceptualizationThe helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines.

This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II).

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.

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.

This book contains refereed papers from the 13th International Conference on GeoComputation held at the University of Texas, Dallas, May 20-23, 2015.

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.

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.

Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree "spatial analysis with measurement"—some may remember the debate after WWII about "theory without measurement" versus "measurement without theory"—several general themes have emerged in the pertaining literature.But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree "spatial analysis with measurement"—some may remember the debate after WWII about "theory without measurement" versus "measurement without theory"—several general themes have emerged in the pertaining literature.But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

In recent years there has been a growing interest in and concern for the development of a sound spatial statistical body of theory. This work has been undertaken by geographers, statisticians, regional scientists, econometricians, and others (e. g. , sociologists). It has led to the publication of a number of books, including Cliff and Ord's Spatial Processes (1981), Bartlett's The Statistical Analysis of Spatial Pattern (1975), Ripley's Spatial Statistics (1981), Paelinck and Klaassen's Spatial Economet~ics (1979), Ahuja and Schachter's Pattern Models (1983), and Upton and Fingleton's Spatial Data Analysis by Example (1985). The first of these books presents a useful introduction to the topic of spatial autocorrelation, focusing on autocorrelation indices and their sampling distributions. The second of these books is quite brief, but nevertheless furnishes an eloquent introduction to the rela­ tionship between spatial autoregressive and two-dimensional spectral models. Ripley's book virtually ignores autoregressive and trend surface modelling, and focuses almost solely on point pattern analysis. Paelinck and Klaassen's book closely follows an econometric textbook format, and as a result overlooks much of the important material necessary for successful spatial data analy­ sis. It almost exclusively addresses distance and gravity models, with some treatment of autoregressive modelling. Pattern Models supplements Cliff and Ord's book, which in combination provide a good introduction to spatial data analysis. Its basic limitation is a preoccupation with the geometry of planar patterns, and hence is very narrow in scope.

Proceedings of the NATO Advanced Study Institute, Bonas, France, July 6-19, 1980

In recent years there has been a growing interest in and concern for the development of a sound spatial statistical body of theory. This work has been undertaken by geographers, statisticians, regional scientists, econometricians, and others (e. g. , sociologists). It has led to the publication of a number of books, including Cliff and Ord's Spatial Processes (1981), Bartlett's The Statistical Analysis of Spatial Pattern (1975), Ripley's Spatial Statistics (1981), Paelinck and Klaassen's Spatial Economet~ics (1979), Ahuja and Schachter's Pattern Models (1983), and Upton and Fingleton's Spatial Data Analysis by Example (1985). The first of these books presents a useful introduction to the topic of spatial autocorrelation, focusing on autocorrelation indices and their sampling distributions. The second of these books is quite brief, but nevertheless furnishes an eloquent introduction to the rela­ tionship between spatial autoregressive and two-dimensional spectral models. Ripley's book virtually ignores autoregressive and trend surface modelling, and focuses almost solely on point pattern analysis. Paelinck and Klaassen's book closely follows an econometric textbook format, and as a result overlooks much of the important material necessary for successful spatial data analy­ sis. It almost exclusively addresses distance and gravity models, with some treatment of autoregressive modelling. Pattern Models supplements Cliff and Ord's book, which in combination provide a good introduction to spatial data analysis. Its basic limitation is a preoccupation with the geometry of planar patterns, and hence is very narrow in scope.