Advertisement

Coordination of Contrariety and Ambiguity in Comparative Compositional Contexts: Balance of Normalized Definitive Status in Multi-indicator Systems

Chapter

Abstract

We address oppositional aspects of comparative compositional contexts for some particular purpose. Compositional components of land cover in localities provide our context, with the exemplifying purpose being cooperative conservation. A subset of cover components is considered definitely propitious (pro) for the purpose, with another subset being definitely contraindicative (con), and the rest as ambiguous “other.” Plotting percent pro on the ordinate and percent con on the abscissa gives a “definitive domain display” for visualization. A “Balance Of Normalized Definitive Status” (BONDS) is used for scalar sequencing. Using concepts of “down-set” and “up-set” from theory of partially ordered sets (posets), this is extended to obtain an intrinsically compositional context of pro and con that applies objectively to any suite of (monotonic) indicators. Indicators are eliminated in a systematic manner to resolve ties in the extended version by lexicographic suborder. Computations are specified in terms of R software.

Keywords

Land Cover Data Frame National Land Cover Database Spatial Setting Strip Minis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Allerhand M (2011) A tiny handbook of R. Springer, New York, NYCrossRefMATHGoogle Scholar
  2. Brüggemann R, Patil GP (2010) Multicriteria prioritization and partial order in environmental sciences. Environ Ecol Stat 17(4):383–410MathSciNetCrossRefGoogle Scholar
  3. Brüggemann R, Patil GP (2011) Ranking and prioritization for multi-indicator systems. Springer, New York, NYCrossRefGoogle Scholar
  4. Brüggemann R, Voigt K (2008) Basic principles of Hasse diagram technique in chemistry. Comb Chem High Throughput Screen 11:756–769CrossRefGoogle Scholar
  5. Brüggemann R, Sorensen P, Lerche D, Carlsen L (2004) Estimation of averaged ranks by a local partial order model. J Chem Inf Comput Sci 44:618–625CrossRefGoogle Scholar
  6. Brüggemann R, Simon U, Mey S (2005) Estimation of averaged ranks by extended local partial order models. Match Commun Math Comput Chem 54:489–518MathSciNetGoogle Scholar
  7. Chander G, Huang C, Yang L, Homer C, Larson C (2009) Developing consistent Landsat data sets for large area applications – the MRLC protocol. IEEE Geosci Remote Sens Lett 6(4):777–781CrossRefGoogle Scholar
  8. De Loof K, De Baets B, De Meyer H, Brüggemann R (2008) Hitchhiker’s guide to poset ranking. Comb Chem High Throughput Screen 11:734–744CrossRefGoogle Scholar
  9. Homer C, Huang C, Yang L, Wylie B, Coan M (2004) Development of a 2001 National Landcover Database for the United States. Photogramm Eng Remote Sensing 70(7):829–840CrossRefGoogle Scholar
  10. Myers W, Patil GP (2010) Preliminary prioritization based on partial order theory and R software for compositional complexes in landscape ecology, with applications to restoration, remediation, and enhancement. Environ Ecol Stat 17:411–436MathSciNetCrossRefGoogle Scholar
  11. Myers, W, Patil GP (2011) Geoinformatics for human environment interface. In: Proceedings of the joint statistical meetings (JSM) 2011, July 31, 2011, Miami Beach, FL, session 206322, presentation 300319, http://www.amstat.org on-line archives
  12. Myers W, Patil GP (2012a) Statistical geoinformatics for human environment interface. Chapman & Hall/CRC, Boca Raton, FLCrossRefGoogle Scholar
  13. Myers W, Patil GP (2012b) Multivariate methods of representing relations in R for prioritization purposes: selective scaling, comparative clustering, collective criteria and sequenced set. Springer, New York, NYCrossRefGoogle Scholar
  14. Patil GP, Taillie C (2004) Multiple indicators, partially ordered sets, and linear extensions: multi-criterion ranking and prioritization. Environ Ecol Stat 11:199–228MathSciNetCrossRefGoogle Scholar
  15. R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/
  16. Short T (2009) R reference guide. Revolution Computing, New Haven, CTGoogle Scholar
  17. Venables WN, Smith DM, the R Development Core Team (2005) An introduction to R. Network Theory LTD, BristolGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Penn State Institutes of Energy and EnvironmentThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Center for Statistical Ecology and Environmental Statistics, Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations