Paper Info
Title
Advanced Spatial Statistics For Analyzing And Visualizing Geo-Referenced Data
Abstract
Spatial statistics supplies advanced methods for analysing environmental data, and copes with observational interdependencies similar to the way principal components analysis treats multicollinearity. The U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) utilizes kriging from geostatistics for mapping and visualizing environmental data. A conceptual framework is articulated between the interpolation problem in kriging and the missing data problem in spatial statistics, with special reference to relations between the exponential semi-variogram and the conditional autoregressive models. Supercomputing experiments are summarized that simplify numerically the probability density function normalizing factor, which is of particular relevance to estimation tasks for the EMAP project.
Year
DOI
Venue
1993
10.1080/02693799308901945
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS
Keywords
Field
DocType
probability density function,missing data,conceptual framework,principal component analysis,spatial statistics
Spatial analysis,Kriging,Autoregressive model,Data mining,Computer science,Multicollinearity,Interpolation,Environmental data,Probability density function,Geostatistics
Journal
Volume
Issue
ISSN
7
2
0269-3798
Citations 
PageRank 
References 
2
0.45
0
Authors
1
Name
Order
Citations
PageRank
Daniel A. Griffith19123.76