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Morphisms for Quantitative Spatial Analysis

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

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. 


Daniel A. Griffith, an Ashbel Smith Professor of Geospatial Information Science at the University of Texas at Dallas, TX, USA, has published 18 books and over 200 articles appearing in geography, statistics, mathematics, economics, and regional science journals and other outlets. Griffith served as editor of Geographical Analysis from 2009 to 2014. Among his many awards, he is a fellow of the Royal Society of Canada, the American Statistical Association, and the Guggenheim Foundation. 

Jean H. P. Paelinck is an emeritus professor of the Erasmus University Rotterdam, and most recently was a distinguished Visiting Professor at George Mason University, VA, USA. As a (co-)author and (co-)editor, he has published around fifty volumes and over 400 articles, mainly on theoretical spatial economics and spatial econometrics. Paelinck has been awarded seven honorary PhDs and numerous other international distinctions, e.g. the Walter Isard Award in Regional Science.



Autorentext
Daniel A. Griffith, an Ashbel Smith Professor of Geospatial Information Science at the University of Texas at Dallas, TX, USA, has published 18 books and over 200 articles appearing in geography, statistics, mathematics, economics, and regional science journals and other outlets. Griffith served as editor of Geographical Analysis from 2009 to 2014. Among his many awards, he is a fellow of the Royal Society of Canada, the American Statistical Association, and the Guggenheim Foundation. 

Jean H. P. Paelinck is an emeritus professor of the Erasmus University Rotterdam, and most recently was a distinguished Visiting Professor at George Mason University, VA, USA. As a (co-)author and (co-)editor, he has published around fifty volumes and over 400 articles, mainly on theoretical spatial economics and spatial econometrics. Paelinck has been awarded seven honorary PhDs and numerous other international distinctions, e.g. the Walter Isard Award in Regional Science.



Inhalt
Preamble ..8
Chapter 1 Introduction to Part 1: Spatial Statistics............................................................................. 10
1.1 Introduction ........................................................................................................................... 10
1.2 Polish employment data: 2006-2013. .................................................................................... 10
1.3 Polish data quality ................................................................................................................. 11
1.4 Concluding comments........................................................................................................... 14
Chapter 2 Spatial Autocorrelation and the p-Median Problem .......................................................... 15
2.1 Introduction ........................................................................................................................... 15
2.2 Eigenvector spatial filtering in a nutshell.............................................................................. 15
2.3 Imputing missing spatial data................................................................................................ 16
2.4 The location-allocation problem............................................................................................ 17
2.5 Location-allocation solutions in the presence of missing and imputed data ......................... 19
2.6 Relationships between spatial autocorrelation and solutions to location-allocation problems................................................................................................................................ 22
2.7 Concluding comments........................................................................................................... 26
Chapter 3 Space-Time Autocorrelation.............................................................................................. 28
3.1 Introduction ........................................................................................................................... 28
3.2 Specifying a space-time Moran Coefficient .......................................................................... 28
3.3 Properties of the space-time Moran Coefficient.................................................................... 31
3.4 Eigenvector space-time filtering............................................................................................ 33
3.5 Omitted variables in a description of space-time response variables .................................... 35
3.6 Concluding comments........................................................................................................... 37
Chapter 4 The Relative Importance of Spatial and Temporal Autocorrelation.................................. 38
4.1 Introduction ........................................................................................................................... 38
4.2 Random effects: SSRE and SURE components.................................................................... 40
4.3 Estimating a SURE term: a sensitivity analysis .................................................................... 42
4.4 Time beats space ................................................................................................................... 44
4.5 Space beats time .................................................................................................................... 45
4.6 Concluding comments........................................................................................................... 46
Chapter 5 The Spatial Weights Matrix and ESF ................................................................................ 47
5.1 Introduction ........................................................................................................................... 47
5.2 Spatial weights matrix comparisons...................................................................................... 47
5.2.1 Some binary SWM comparisons ................................................................................... 49
5.2.2 Some row-standardized SWM comparisons.................................................................. 50
5.2.3 Variance stabilizing standardization.............................................................................. 51
5.3 Comparisons of spatial weights matrix eigenvectors ............................................................ 53
5.4 Competing model specifications: spatial autoregressions and ESFs..................................... 54

Produktinformationen

Titel: Morphisms for Quantitative Spatial Analysis
Autor:
EAN: 9783319725536
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer-Verlag GmbH
Genre: Wirtschaft
Anzahl Seiten: 258
Veröffentlichung: 07.03.2018
Dateigrösse: 7.8 MB