Modeling Spatial Autocorrelation In Spatial Interaction Data
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Modeling Spatial Autocorrelation In Spatial Interaction Data
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Author : Manfred M. Fischer
language : en
Publisher:
Release Date : 2008
Modeling Spatial Autocorrelation In Spatial Interaction Data written by Manfred M. Fischer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Spatial interaction models of the gravity type are widely used to model origindestination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize an origin region of a flow, variables that characterize a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrelation among flow residuals. An example using patent citation data that capture knowledge flows across 112 European regions serves to illustrate the application and the comparison of the two approaches.
Modelling Spatial Autocorrelation In Spatial Interaction Data
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Author : Manfred M. Fischer
language : en
Publisher:
Release Date : 2008
Modelling Spatial Autocorrelation In Spatial Interaction Data written by Manfred M. Fischer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Spatial interaction models of the gravity type are widely used to model origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that characterise a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrelation among flow residuals. An example using patent citation data that capture knowledge flows across 112 European regions serves to illustrate the application and the comparison of the two approaches.
Spatial Autocorrelation And Spatial Interaction
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Author : Roberto Patuelli
language : en
Publisher:
Release Date : 2016
Spatial Autocorrelation And Spatial Interaction written by Roberto Patuelli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Definition:Spatial interaction refers to all cases in which a relationship (a flow of persons, goods, communications, citations, and so on) is observed between pairs of spatial units. Differently from the standard areal or point units often analyzed in spatial science, spatial interaction data are denoted by their bilateral nature. They have an origin and a destination (in the particular case, the two being the same). Such origins and destinations may have observable characteristics. Moreover, each pair of spatial units may be described according to some measures of separation (such as distance) or of commonality (e.g., the presence of a common language). Such descriptors are typically employed in spatial interaction models, the most commonly used analytical framework for modeling flow data. Problems occur when the aforementioned descriptors exhibit correlation between the values of different spatial units or dyads which is due to the spatial configuration of the units themselves (e.g., ...This is an excerpt from the content.
Spatial Data Analysis
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Author : Manfred M. Fischer
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-08-05
Spatial Data Analysis written by Manfred M. Fischer 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 2011-08-05 with Business & Economics categories.
The availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.
The Spatial Autocorrelation Problem In Spatial Interaction Modelling
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Author : Daniel A. Griffith
language : en
Publisher:
Release Date : 2017
The Spatial Autocorrelation Problem In Spatial Interaction Modelling written by Daniel A. Griffith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.
Perspectives On Spatial Data Analysis
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Author : Luc Anselin
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-12-24
Perspectives On Spatial Data Analysis written by Luc Anselin 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 2009-12-24 with Business & Economics categories.
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.
Advanced Spatial Statistics
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Author : Daniel A. Griffith
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Advanced Spatial Statistics 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 2012-12-06 with Business & Economics categories.
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.
Gis India
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Author :
language : en
Publisher:
Release Date : 2004
Gis India written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Geographic information systems categories.
International Encyclopedia Of The Social Behavioral Sciences
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Author : Neil J. Smelser
language : en
Publisher:
Release Date : 2001
International Encyclopedia Of The Social Behavioral Sciences written by Neil J. Smelser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Psychology categories.
The largest work ever published in the social and behavioural sciences. It contains 4000 signed articles, 15 million words of text, 90,000 bibliographic references and 150 biographical entries.
Geographic Information Systems Spatial Modelling And Policy Evaluation
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Author : Manfred M. Fischer
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Geographic Information Systems Spatial Modelling And Policy Evaluation written by Manfred M. Fischer 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 2012-12-06 with Business & Economics categories.
Geographical Information Systems (GIS) provide an enhanced environment for spatial data processing. The ability of geographic information systems to handle and analyse spatially referenced data may be seen as a major characteristic which distinguishes GIS from information systems developed to serve the needs of business data processing as well as from CAD systems or other systems whose primary objective is map production. This book, which contains contributions from a wide-ranging group of international scholars, demonstrates the progress which has been achieved so far at the interface of GIS technology and spatial analysis and planning. The various contributions bring together theoretical and conceptual, technical and applied issues. Topics covered include the design and use of GIS and spatial models, AI tools for spatial modelling in GIS, spatial statistical analysis and GIS, GIS and dynamic modelling, GIS in urban planning and policy making, information systems for policy evaluation, and spatial decision support systems.