Abstract
In this chapter the tools for spatial exploratory analysis are provided. These include data postings, swathplots and experimental variograms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
With respect to the metric underlying the starting transformed scores ζ = glr(z).
- 2.
If the factors do not exhibit structured spatial cross correlation, then the representation is an expansion via empirical orthogonal functions.
References
Bandarian, E. M., Bloom, L. M., & Mueller, U. A. (2008). Direct minimum/maximum autocorrelation factors within the framework of a two structure linear model of coregionalisation. Computers & Geosciences, 34(3), 190–200.
Bivand, R. S., Pebesma, E., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R, 2nd edition (405 pp.). New York: Springer Verlag.
Chilès, J. P., & Delfiner, P. (2012). Geostatistics - Modeling spatial uncertainty (2 ed., 699 pp.). Hoboken, NJ, USA: Wiley.
Desbarats, J., & Dimitrakopoulos, R. (2000). Geostatistical simulation of regionalised pore-size distributions using min/max autocorrelation factors. Mathematical Geology, 32(8), 919–942.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation (483 pp.). Applied Geostatistics Series. New York, NY, USA: Oxford University Press.
Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics (561 pp.). New York, NY, USA: Oxford University Press.
Mueller, U., Tolosana-Delgado, R., Grunsky, E. C., & McKinley, J. M. (2020). Biplots for compositional data derived from generalized joint diagonalization methods. Applied Computing and Geosciences, 8, 100044.
Pawlowsky, V. (1986). Räumliche Strukturanalyse und Schätzung ortsabhängiger Kompositionen mit Anwendungsbeispielen aus der Geologie (170 p.). Ph.D. thesis, Fachbereich Geowissenschaften, Freie Universität Berlin, Berlin (D).
Pawlowsky-Glahn, V., & Olea, R. A. (2004). Geostatistical analysis of compositional data. In DeGraffenreid, Jo Anne (Ed.) Number 7 in Studies in Mathematical Geology. Oxford University Press.
Petitgas, P., Woillez, M., Doray, M., & Rivoirard, J. (2018). Indicator-based geostatistical models for mapping fish survey data. Mathematical Geosciences, 50(2), 187–208.
Switzer, P., & Green, A. A. (1984). Min/Max autocorrelation factors for multivariate spatial imaging. Technical Report 6, Department of Statistics, Stanford University.
Tercan, A. (1999). Importance of orthogonalization algorithm in modelling conditional distributions by orthogonal transformed indicator methods. Mathematical Geology, 31(2), 155–174.
Tolosana-Delgado, R. (2006). Geostatistics for constrained variables: positive data, compositions and probabilities. Application to environmental hazard monitoring (198 p.). Ph.D. thesis, Universitat de Girona (Spain).
Tolosana-Delgado, R., Boogaart, K. G. v. d., & Pawlowsky-Glahn, V. (2011). Geostatistics for compositions. In V. Pawlowsky-Glahn, A. Buccianti (Eds.), Compositional data analysis: Theory and applications (pp. 73–86, 378 pp.). John Wiley & Sons.
Tolosana-Delgado, R., Mueller, U., & Boogaart, K. (2019). Geostatistics for compositional data: an overview. Mathematical Geosciences, 51(4), 485–526.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tolosana-Delgado, R., Mueller, U. (2021). Exploratory Spatial Analysis. In: Geostatistics for Compositional Data with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-82568-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-82568-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82567-6
Online ISBN: 978-3-030-82568-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)