Share Email Print

Proceedings Paper

Categorical mapping and error modeling based on the discriminant space
Author(s): Jingxiong Zhang; Michael Goodchild; Phaedon Kyriakidis; Xiong Rao
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Despite developments in error analysis for discrete objects and interval/ratio fields, there exist conceptual problems with the case of nominal fields. This paper seeks to consolidate a conceptual framework based on the discriminant space for categorical mapping and error modeling. The discriminant space is defined upon the essential properties and processes underlying occurrences of spatial classes, and lends itself to geostatistical analysis and modeling. The discriminant space furnishes consistency in categorical mapping by imposing class-conditional mean structures that are associated with discriminant or "environmental" variables in various statistical models, and facilitates physically interpretable and scale-dependent error modeling. Further research will focus on models and methods based on multi-dimensional discriminant space and at multiple scales.

Paper Details

Date Published: 28 October 2006
PDF: 8 pages
Proc. SPIE 6420, Geoinformatics 2006: Geospatial Information Science, 64201H (28 October 2006); doi: 10.1117/12.713283
Show Author Affiliations
Jingxiong Zhang, Wuhan Univ. (China)
Michael Goodchild, Univ. of California, Santa Barbara (United States)
Phaedon Kyriakidis, Univ. of California, Santa Barbara (United States)
Xiong Rao, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6420:
Geoinformatics 2006: Geospatial Information Science
Jianya Gong; Jingxiong Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top