Abstract
Geostatistics provides the practitioner with a methodology to quantify spatial uncertainty. Statistics come into play because probability distributions are the meaningful way to represent the range of possible values of a parameter of interest. In addition, a statistical model is well suited to the apparent randomness of spatial variations. It must be noted that there is considerable variety of statistical methods that have been applied in the analysis of spatial variation in data, summarized by Dale (Spatial pattern analysis in plant ecology. Cambridge University Press, 1999) [1]. These include dispersal analysis, spectral analysis, wavelet analysis, kriging, and spatial Monte Carlo simulations, and many geostatistics methods. Kriging was developed for estimating thresholds of continuous variables. It has been used for interpolation and simulation of categorical variables and for spatial uncertainty analysis.
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References
Dale MRT (1999) Spatial pattern analysis in plant ecology. Cambridge University Press
Baddeley A (2008) Analysing spatial point patterns in R. CSIRO, Canberra, Australia
Diggle PJ, Tawn JA, Moyeed RA (1998) Model-based geostatistics (with discussions). Appl Stat 47(3):299–350
Besag J (1974) Spatial interaction and the statistical analysis of lattice systems. J Roy Stat Soc Ser B 3:192–236
Besag J (1986) On the statistical analysis of dirty pictures. J Roy Stat Soc Ser B 48:259–302
Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattn Anal Mach Intell 5(1):25–39
Aykroyd RG, Zimeras S (1999) Inhomogeneous prior models for image reconstruction. J Am Stat Assoc (JASA) 94(447):934–946
Zimeras S (1997) Statistical models in medical image analysis. Ph.D. thesis, Leeds University, Department of Statistics
Aykroyd RG, Haigh JGB, Zimeras S (1996) Unexpected spatial patterns in exponential family auto-models. Graph Models Image Process 58(5):452–463
Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion Limited
Cressie NA (1993) Statistics for spatial data (revised edition). Wiley
Diggle PJ (2003) Spatial analysis of spatial point patterns, 2nd edn. Arnold Publishers
Diggle PJ (1983) Spatial statistics. Wiley
Ripley BD (1981) Spatial statistics. Wiley, New York
Zimeras S, Georgiakodis F (2005) Bayesian models for medical image biology using Monte Carlo Markov Chain techniques. Math Comput Model 42:759–768
Upton G, Fingleton B (1985) Spatial data analysis by example. Wiley
Baddeley AJ, Silverman BW (1984) A cautionary example of the use of second order methods for analyzing point patterns. Biometrics 40:1089–1093
Gatrell AC, Bailey TC, Diggle PJ, Rowlingson BS (1996) Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr 21(1):256–274
Wang F (2014) Quantitative methods and socio-economic applications in GIS (2ª edn). CRC Press
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The author would like to thank Prof. Phaidon Kyriakidi for his useful support for the programming implementation of this work.
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Zimeras, S. (2022). Spatial Statistics Models for COVID-19 Data Under Geostatistical Methods. In: Howlett, R.J., Jain, L.C., Littlewood, J.R., Balas, M.M. (eds) Smart and Sustainable Technology for Resilient Cities and Communities. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-9101-0_10
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