Morphisms for quantitative spatial analysis / Daniel A. Griffith, Jean H.P. Paelinck.
Griffith, Daniel A., author.; Paelinck, Jean H. P., author.
2018
QA278.2
Available Online

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Title Morphisms for quantitative spatial analysis / Daniel A. Griffith, Jean H.P. Paelinck.
Author Griffith, Daniel A., author.
ISBN 9783319725536 (electronic book)
331972553X (electronic book)
3319725521
9783319725529
Published Cham : Springer, 2018.
Language English
Description 1 online resource : illustrations.
Call Number QA278.2
Dewey Decimal Classification 519.5
Summary 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. .
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource ; title from PDF title page (viewed March 19, 2018).
Added Author Paelinck, Jean H. P., author.
Series Advanced studies in theoretical and applied econometrics ; v. 51.
Available in Other Form Print version: 9783319725529
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Preamble
Introduction to Part 1: Spatial statistics
Spatial autocorrelation and the p-Median problem
Space-time autocorrelation
The relative importance of spatial and temporal autocorrelation
The spatial weights matrix and ESF
Clustering: Spatial autocorrelation and location quotients
Spatial autocorrelation parameter estimation for massively large georeferenced datasets
Space-time data and semi-saturated fixed effects
Spatial autocorrelation and spatial interaction gravity models
General conclusions about spatial statistics
Introduction to Part 2: Spatial econometrics
Tinbergen-Bos systems: Combining combinatorial analysis with metric topology
Time, space, or econotimespace?- Hybrid dynamical systems and control
The W matrix revisited
Clustering, some non-standard approaches
Linear expenditure systems and related estimation problems
Structural indicators galore
Traveling with the salesman
Complexer and complexer, said Alice
General conclusions about spatial econometrics
Epilogue
References
Subject index
Author's index.

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