Spatial variation of feature density in multiscale topographic data

Density-Based Multiscale Data Condensation

ÐA problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approa...

Spatial Data Infrastructure for Topographic Maps

Adaptive multiscale feature extraction from range data

We present a method to extract meaningful features from range images which introduces some novel ideas: First, our primitives are a richer set than the usual one, as we not only extract high frequences events, which correspond to jump boundaries and sharp creases, but also low frequency events such as smooth ridges and ravines. These last features are generally discarded as they tend to hide in...

Modeling Spatial Variation in Leukemia Survival Data

In this article we combine ideas from spatial statistics with lifetime data analysis techniques to investigate possible spatial variation in survival of adult acute myeloid leukemia patients in northwest England. Exploratory analysis suggests both clinically and statistically signiŽ cant variation in survival rates across the region. A multivariate gamma frailty model incorporating spatial depe...

The Study of Soil Fertility Spatial Variation Feature Based on GIS and Data Mining

Studying on the spatial distribution and variation characteristics of soil fertility by Application of geographical information systems (GIS) and data mining(DM), with the quantitative analysis of spatial variability of soil fertility in complexity, and provide the effective way for simulations which can be closer to the farmland soil fertility variability. At the same time, interpolation metho...