A technique for constructing monotonic regression splines to enable non-linear transformation of GIS rasters

18th World IMACS Congress and MODSIM 2009 - International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings

Volumn , Issue , 2009, Pages 2507-2513

  Manion, G ^a  

^a OFFICE OF ENVIRONMENT AND HERITAGE   (Australia)

Author keywords

Generalised Dissimilarity Modelling (GDM); I Splines

Indexed keywords

ECOLOGY; FORESTRY; LINEAR TRANSFORMATIONS; REGRESSION ANALYSIS; SURVEYING;

DIGITAL ELEVATION MODEL; ECOLOGICAL DISTANCES; ENVIRONMENTAL SURFACES; LINEAR REGRESSION MODELS; MONOTONIC REGRESSIONS; NON-LINEAR TRANSFORMATIONS; PIECEWISE POLYNOMIALS; SPATIAL PREDICTION;

MATHEMATICAL TRANSFORMATIONS;

EID: 85086225039     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Bray J. R., Curtis J.T., (1957) An ordination of the upland forest of the southern Wisconsin. Ecological Monographies, 27: 325-349.
2. Environmental Systems Research Institute Inc, (1996) ArcView Spatial Analyst: Advanced Spatial Analysis Using Raster and Vector Data A: 131-139.
3. Ferrier S., Manion G., Elith., Richardson K., (2007) Using generalised dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13: 252-264.
4. Gauch, H. G., (1973) The relationship between sample similarity and ecological distance. Ecology, 54: 618-622.
5. Hearn D., Baker M. P., (1986) Computer Graphics: Prentice Hall, 10: 200-204.
6. Ramsay J. O., (1998) Monotone Regression Splines in Action. Statistical Science 3.4: 425-461.
7. Winsberg S., De Soete G., (1997) Multidimensional scaling with constrained dimensions: CONSCAL. British Journal of Mathematical and Statistical Psychology 50: 55-72.
8. Yandell B. S., Smoothing splines - a tutorial. The statistician 42: 317-319.