GIS-based exploratory spatial data analysis for geochemical mapping
|Category||GIS & Remote sensing|
|Location||International Geological Congress,oslo 2008|
|Author||Chen, Zhijun۱; Cheng, Qiuming۲; Chen, Jianguo۱|
|Holding Date||16 September 2008|
Reliable and informative geochemical maps are needed in mineral exploration and in environmental studies for policy makers. GIS graphics tend to be strong on "presentation" of data rather than "exploration", while Exploratory Spatial Data Analysis (ESDA) provides a set of robust tools for exploring spatial data. In general, geochemical data don’t follow a normal or log-normal data distribution but are poly-population, don’t consist of independent samples but the sample related to certain processes are space-dependent. The task in geochemical map is rather to display these different processes in map form and to detect local deviations from the dominating processes in any one sub-area. The selection of classes for symbols or color coding is a crucial step in mapping.
The ESDA technique --- such as the percentiles, box plot, CDF (Cumulative Distribution Function) diagram, fractal concentration area plot, contrast, local singularity exponents --- are employed for class selection to explore the data structure and geochemical processes.
The contour map and grid map are another possible way putting geochemical data onto a map. The surface produced by some smoothing interpolation algorithms will lost the local variability. A multifractal interpolator combining the local singularity analysis and the conventional interpolation methods (such as IDW, Kriging) are used to improve the results, especially for observed data with significant singularity.
A novel color map renderer is designed for coloring the thematic classes or analytical values. The minimum (or begin-class) and the maximum (or end-class) can be adjusted (decrease or increase) and the data within the range will be mapped to the whole or part of the color bar. This color mapping scheme has the advantages of presenting the regional distribution as well as highlighting the local patterns of ROI (region of interest). Some useful indexes, such as percentiles, local singularity exponents, with colors spread over the whole range of classes will show the most informative spatial distribution of the data structure and the mapped patterns become directly comparable, independent of data range and levels. Therefore, maps produced during different surveys, at different times, different sample media or even different elements, become directly comparable in the chromatic vision.
A case study using various techniques for producing and comparing different geochemical maps demonstrates the classification scheme by ESDA techniques and the coloring scheme by the new designed color map renderer are effective in data interpretation and presentation of geochemical data. The local singularity analysis is a powerful tool to enhance and detect the local mineralization information from the dominating process in any one sub-survey area.