Skip to main content

The State of Spatial and Spatio-Temporal Statistical Modeling

  • Chapter
  • First Online:
Predictive Species and Habitat Modeling in Landscape Ecology

Abstract

The purpose of this chapter is to provide an overview of how statistical analyses have been used for studying ecological processes on landscapes and where the field of statistics is headed in general. Various approaches to the statistical analysis of spatial and spatio-temporal problems are presented and discussed; also, references for several suggested readings, containing further information and examples, are provided at the end of each section.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Arab A, Hooten MB, Wikle CK (2007) Hierarchical spatial models. In: Encyclopedia of geographical information science. Springer, New York.

    Google Scholar 

  • Banerjee S, Carlin BP, Gelfand AE (2004) Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC, Boca Raton, FL.

    Google Scholar 

  • Berliner LM (1996) Hierarchical Bayesian time series models. In: Hanson K, Silver R (eds), Maximum entropy and Bayesian methods, pp. 15–22. Kluwer Academic Publishers, New York.

    Google Scholar 

  • Brown LD (2000) An essay on statistical decision theory. J Am Stat Assoc. 95:1277–1281.

    Article  Google Scholar 

  • Carlin BP, Louis TA (2000) Bayes and empirical Bayes methods for data analysis, Second Edition. Chapman & Hall/CRC, Boca Raton, FL.

    Google Scholar 

  • Caswell H (2001) Matrix population models: construction, analysis, and interpretation. Sinauer Associates, Inc., Sunderland, MA.

    Google Scholar 

  • Christensen R (2002) Plane answers to complex questions. Springer-Verlag, New York.

    Google Scholar 

  • Clark, JS (2007) Models for ecological data, an introduction. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Clark JS, Gelfand AE (2006) Hierarchical modelling for the environmental sciences. Oxford University Press, New York.

    Google Scholar 

  • Cressie NAC (1993) Statistics for spatial data, Revised Edition. John Wiley & Sons, New York.

    Google Scholar 

  • Cressie NC, Calder C, Clark JS, Ver Hoef JM, Wikle C (2009). Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecol Appl 19:553–570.

    Article  PubMed  Google Scholar 

  • Cressie N, Johannesson G (2006) Fixed rank kriging for large spatial datasets. Technical Report No. 780, Department of Statistics, The Ohio State University, Columbus, OH.

    Google Scholar 

  • Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88:2783–2792.

    Article  PubMed  Google Scholar 

  • Diggle PJ, Ribeiro PJ, Jr (2007) Model-based geostatistics. Springer, New York.

    Google Scholar 

  • Efron B, Tibshirani R (1991) Statistical analysis in the computer age. Science 253:390–395.

    Article  CAS  PubMed  Google Scholar 

  • Furrer R, Genton MG, Nychka D (2006) Covariance tapering for interpolation of large spatial datasets. J Comput Graph Stat 15:502–523.

    Article  Google Scholar 

  • Gelfand AE, Silander JA, Wu S, Latimer A, Lewis PO, Rebelo AG, Holder M (2006) Explaining species distribution patterns through hierarchical modeling. Bayesian Anal 1:41–92.

    Article  Google Scholar 

  • Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis: second edition, Chapman and Hall/CRC, Boca Raton, FL.

    Google Scholar 

  • Grimm V, Railsback SF (2005) Individual-based modeling and ecology. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New York.

    Google Scholar 

  • Hilborn R, Mangel M (1997) The ecological detective, confronting models with data. Princeton University Press, Princeton, NJ.

    Google Scholar 

  • Holan S, Wang S, Arab A, Sadler J, Stone K (2008) Semiparametric geographically weighted response curves with application to site-specific agriculture. J Agric Biol Environ Stat 13:424–439.

    Article  Google Scholar 

  • Hooten MB, Wikle CK (2007) Shifts in the spatio-temporal growth dynamics of shortleaf pine. Environ Ecol Stat 14:207–227.

    Article  Google Scholar 

  • Hooten MB, Wikle CK (2010) Statistical agent-based models for discrete spatio-temporal systems. J Am Stat Assoc 105:236–248.

    Article  CAS  Google Scholar 

  • Hooten, MB, Larsen DR, Wikle CK (2003) Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model. Landsc Ecol 18:487–502.

    Article  Google Scholar 

  • Hooten MB, Wikle CK, Dorazio RM, Royle JA (2007) Hierarchical spatiotemporal matrix models for characterizing invasions. Biometrics 63:558–567.

    Article  PubMed  Google Scholar 

  • Hooten MB, Wikle CK, Sheriff S, Rushin J (2009) Optimal spatio-temporal hybrid sampling designs for monitoring ecological structure. J Veg Sci 20:639–649.

    Article  Google Scholar 

  • Kays RW, Gompper ME, Ray JC (2008) Landscape ecology of eastern coyotes based on large-scale estimates of abundance. Ecol Appl 18:1014–1027.

    Article  PubMed  Google Scholar 

  • Le ND, Zidek JV (2006) Statistical analysis of environmental space-time processes. Springer, New York.

    Google Scholar 

  • Lele SR, Dennis B, Lutscher F (2007) Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecol Lett 10:551–563.

    Article  PubMed  Google Scholar 

  • Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models. WCB/McGraw-Hill, Boston.

    Google Scholar 

  • Nychka D (2000) Spatial-process estimates as smoothers. In: Schimek, MG (ed) Smoothing and regression: approaches, computation, and application. John Wiley & Sons, New York.

    Google Scholar 

  • Olea RA (1984) Sampling design optimization for spatial functions. Math Geol 16:369–392.

    Article  Google Scholar 

  • Royle JA, Dorazio RM (2006) Hierarchical models of animal abundance and occurrence. J Agric Biol Environ Stat 11:249–263.

    Article  Google Scholar 

  • Royle JA, Dorazio RM (2008) Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations, and communities. Academic Press, London.

    Google Scholar 

  • Royle JA, Kery M (2007) A Bayesian state-space formulation of dynamic occupancy models. Ecology 88:1813–1823.

    Article  PubMed  Google Scholar 

  • Royle JA, Wikle CK (2005) Efficient statistical mapping of avian count data. Environ Ecol Stat 12:225–243.

    Article  Google Scholar 

  • Salsburg D (2001) The lady tasting tea: how statistics revolutionized science in the twentieth century. Henry Holt and Company, New York.

    Google Scholar 

  • Schabenberger O, Gotway CA (2005) Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton, FL.

    Google Scholar 

  • Shi T, Cressie NAC (2007) Global statistical analysis of MISR aerosol data: a massive data product from NASA’s Terra satellite. Environmetrics 18:665–680.

    Article  Google Scholar 

  • Shumway R, Stoffer DS (2006) Time series analysis and its applications. Springer, New York.

    Google Scholar 

  • Stevens DL, Olsen AR (2004) Spatially balanced sampling of natural resources. J Am Stat Assoc 99:262–278.

    Article  Google Scholar 

  • Stigler SM (1990) The history of statistics: the measurement of uncertainty before 1900. Harvard University Press, Cambridge.

    Google Scholar 

  • Ver Hoef JM, Peterson E, Theobald D (2006) Spatial statistical models that use flow and stream distance. Environ Ecol Stat 13:449–464.

    Article  Google Scholar 

  • Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. John Wiley & Sons, Inc. Hoboken, NJ.

    Google Scholar 

  • Wikle CK, Royle JA (1999) Space-time models and dynamic design of environmental monitoring networks. J Agric Biol Environ Stat 4:489–507.

    Article  Google Scholar 

  • Wikle CK (2001) A kernel-based spectral approach for spatio-temporal dynamic models. Proceedings of the 1st Spanish workshop on spatio-temporal modelling of environmental processes (METMA), Benicassim, Castellon (Spain), 28–31 October 2001, pp. 167–180.

    Google Scholar 

  • Wikle CK (2003) Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84:1382–1394.

    Article  Google Scholar 

  • Zhu J, Huang H-C, Wu C-T (2005) Modeling spatial-temporal binary data using Markov random fields. J Agric Biol Environ Stat 10:212–225.

    Article  Google Scholar 

  • Zhu Z, Stein M (2006) Spatial sampling design for prediction with estimated parameters. J Agric Biol Environ Stat 11:24–44.

    Article  Google Scholar 

  • Zimmerman DL (2006) Optimal network design for spatial prediction, covariance estimation, and empirical prediction. Environmetrics 17:635–652.

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to thank A. Arab, C. Wikle, and two anonymous reviewers of this chapter for their helpful comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mevin B. Hooten .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+BUsiness Media, LLC

About this chapter

Cite this chapter

Hooten, M.B. (2011). The State of Spatial and Spatio-Temporal Statistical Modeling. In: Drew, C., Wiersma, Y., Huettmann, F. (eds) Predictive Species and Habitat Modeling in Landscape Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7390-0_3

Download citation

Publish with us

Policies and ethics