Visualization of Spatial Data Introduction

Visualization of Spatial Neuroanatomical Data

Spatial Visualization of Conceptual Data

Numerous data mining methods have been designed to help extract relevant and significant information from large datasets. Computing concept lattices allows clustering data according to their common features and making all relationships between them explicit. However, the size of such lattices increases exponentially with the volume of data and its number of dimensions. This paper proposes to us...

Data Representation for Scientific Visualization: An Introduction

Given the very rapid increase in today’s computing power, scientists and researchers are able to collect ever larger repositories of data. A common way to explore and analyze this data is through visualization because of visualization’s ability to provide rapid insight into large amounts of data. Scientific visualization, an important field of computer graphics, enables scientists to depict dat...

Constraint-Based Visualization of Spatial Interpolation Data

We propose using a constraint relational representation for spatial data derived using an inverse distance weighting interpolation method. The advantage of our approach is that many queries that could not be done in traditional GIS systems can now be easily expressed and evaluated in constraint database systems. The data visualization can also be based on constraint techniques.

Introduction to circular data

In many diverse scientific fields, the measurements are directions. For instance, a biologist may be measuring the direction of flight of a bird or the orientation of an animal. A series of such observations is called ”directional data”. Since a direction has no magnitude, these can be conveniently represented as points on the circumference of a unit circle centered at the origin or as unit ...