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Luc Anselin – författare
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16 produkter
16 produkter
- Inbunden, Engelska, 2024
1 474 kr
Skickas inom 10-15 vardagar
This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa.The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required.Key Features:• Includes spatial perspectives on cluster analysis• Focuses on exploring spatial data• Supplemented by extensive support with sample data sets and examples on the GeoDaCenter websiteThis book is both useful as a reference for the software and as a text for students and researchers of spatial data science. - Inbunden, Engelska, 2024
1 474 kr
Skickas inom 10-15 vardagar
This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis.It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods. 2 903 kr
Skickas inom 10-15 vardagar
This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa.This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis.It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.- E-bokPDF, Engelska, 2024
989 kr
Läs direkt efter köp
This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa.The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required.Key Features:* Includes spatial perspectives on cluster analysis* Focuses on exploring spatial data* Supplemented by extensive support with sample data sets and examples on the GeoDaCenter websiteThis book is both useful as a reference for the software and as a text for students and researchers of spatial data science. 989 kr
Läs direkt efter köp
This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa.The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required.Key Features:* Includes spatial perspectives on cluster analysis* Focuses on exploring spatial data* Supplemented by extensive support with sample data sets and examples on the GeoDaCenter websiteThis book is both useful as a reference for the software and as a text for students and researchers of spatial data science.- E-bokPDF, Engelska, 2024
989 kr
Läs direkt efter köp
This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.
The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis.
It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.
The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.
989 kr
Läs direkt efter köp
This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.
The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis.
It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.
The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.
- Inbunden, Engelska, 2004
1 683 kr
Skickas inom 10-15 vardagar
The volume on New Directions in Spatial Econometrics appeared in 1995 as one of the first in the then new Springer series on Advances in Spatial Sciences. It very quickly became evident that the book satisfied a pent up demand for a collection of advanced papers dealing with the methodology and application of spatial economet rics. This emerging subfield of applied econometrics focuses on the incorporation of location and spatial interaction in the specification, estimation and diagnostic testing of regression models. The current effort is a follow up to the New Directions volume. Even though the number of empirical and theoretical journal articles dealing with various as pects of spatial econometrics has grown tremendously in the recent past, the need remained to bring together an advanced collection on methodology, tools and appli cations. This volume contains several papers that were presented at special sessions on spatial econometrics organized as part of a number of conferences of the Re gional Science Association International. In addition, a few papers were invited for submission. All papers were refereed. The focus in the volume reflects the advances made in the field in recent years. - Inbunden, Engelska, 2010
1 960 kr
Skickas inom 10-15 vardagar
Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis’ contributions: • Spatial analysis • Pattern analysis • Local statistics • Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications. - E-bokPDF, Engelska, 2009
2 493 kr
Läs direkt efter köp
Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis’ contributions: • Spatial analysis • Pattern analysis • Local statistics • Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications. - Häftad, Engelska, 2010
1 634 kr
Skickas inom 10-15 vardagar
The volume on New Directions in Spatial Econometrics appeared in 1995 as one of the first in the then new Springer series on Advances in Spatial Sciences. It very quickly became evident that the book satisfied a pent up demand for a collection of advanced papers dealing with the methodology and application of spatial economet rics. This emerging subfield of applied econometrics focuses on the incorporation of location and spatial interaction in the specification, estimation and diagnostic testing of regression models. The current effort is a follow up to the New Directions volume. Even though the number of empirical and theoretical journal articles dealing with various as pects of spatial econometrics has grown tremendously in the recent past, the need remained to bring together an advanced collection on methodology, tools and appli cations. This volume contains several papers that were presented at special sessions on spatial econometrics organized as part of a number of conferences of the Re gional Science Association International. In addition, a few papers were invited for submission. All papers were refereed. The focus in the volume reflects the advances made in the field in recent years. - Häftad, Engelska, 2012
1 960 kr
Skickas inom 10-15 vardagar
Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis’ contributions: • Spatial analysis • Pattern analysis • Local statistics • Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications. - E-bokPDF, Engelska, 2012
710 kr
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The field of spatial econometrics, which is concerned with statistical and econo metric techniques to be used to handle spatial effects in multiregional models, was first touched upon in the 1950s. It was given its name in the early 70s by Jean Paelinck and has expanded since. Its development can be monitored in various monographs that have been published since, starting with the seminal work by Andrew Cliff and Keith Ord. Also, the wide array of journals in which contributions to spatial econometrics have been published, shows that the relevance of the field is not restricted to regional science, but extends to geography, spatial statistics, biology, psychology, political science and other social sciences. This volume contains a collection of papers that were presented at special sessions on spatial econometrics organized in the context of the European and North American conferences of the Regional Science Association International, that took place in Louvain la Neuve (August 25-28,1992) and in Houston (November 11-14, 1993), respectively. Apart from these conference papers some contributions were written especially for this volume. The central idea of this book is to communicate the state of the art of spatial econometrics and to offer a number of new directions for future research. In order to do so, the editors sought contributions of leading scholars currently active in this field. - Häftad, Engelska, 2011
549 kr
Skickas inom 10-15 vardagar
The field of spatial econometrics, which is concerned with statistical and econo metric techniques to be used to handle spatial effects in multiregional models, was first touched upon in the 1950s. It was given its name in the early 70s by Jean Paelinck and has expanded since. Its development can be monitored in various monographs that have been published since, starting with the seminal work by Andrew Cliff and Keith Ord. Also, the wide array of journals in which contributions to spatial econometrics have been published, shows that the relevance of the field is not restricted to regional science, but extends to geography, spatial statistics, biology, psychology, political science and other social sciences. This volume contains a collection of papers that were presented at special sessions on spatial econometrics organized in the context of the European and North American conferences of the Regional Science Association International, that took place in Louvain la Neuve (August 25-28,1992) and in Houston (November 11-14, 1993), respectively. Apart from these conference papers some contributions were written especially for this volume. The central idea of this book is to communicate the state of the art of spatial econometrics and to offer a number of new directions for future research. In order to do so, the editors sought contributions of leading scholars currently active in this field. - E-bokPDF, Engelska, 2013
2 105 kr
Läs direkt efter köp
The volume on New Directions in Spatial Econometrics appeared in 1995 as one of the first in the then new Springer series on Advances in Spatial Sciences. It very quickly became evident that the book satisfied a pent up demand for a collection of advanced papers dealing with the methodology and application of spatial economet rics. This emerging subfield of applied econometrics focuses on the incorporation of location and spatial interaction in the specification, estimation and diagnostic testing of regression models. The current effort is a follow up to the New Directions volume. Even though the number of empirical and theoretical journal articles dealing with various as pects of spatial econometrics has grown tremendously in the recent past, the need remained to bring together an advanced collection on methodology, tools and appli cations. This volume contains several papers that were presented at special sessions on spatial econometrics organized as part of a number of conferences of the Re gional Science Association International. In addition, a few papers were invited for submission. All papers were refereed. The focus in the volume reflects the advances made in the field in recent years.
