Spatial analysis of geochemical data
Shawn Laffan
Hotspot identification
• Where are the regions of excess element abundance?• Greater than expected• Anomalously high
• Where are the regions of less than expected abundance?
Hotspot identification
• Need quantitative comparison within and between data sets
• Looking for clusters
• Moving window analyses• Geographically local
Tobler’s First Law
• That everything is related to everything else, but that near things are more related than those far apart
Hotspot identification
• Spatial scale• Spatial extent• Spatial non-stationarity• Significance
Getis-Ord hotspot statistic
Getis-Ord hotspot statistic
Sum weighted values
in window
Subtract sum of weights * mean
(expected value)
Divide by standard deviation andcorrect for weights used in window
Getis-Ord hotspot statistic
• Positive for samples that are, on average, above the mean
• Negative if below the mean• Z-score
• >+1.96 significant hotspot• <-1.96 significant coldspot
Choice of weights (sample window)• Binary
• Resultant surfaces can have abrupt changes
• Continuous• Smoother surfaces
Gaussian – asymptotes to zero
IDW - asymptotes to zero
Bisquare – decays to zero
Gi* analyses
• Fe, Ni, Pb, Cu, Li, Cr, Ce/Li, Cr/Fe• log10 scaled• 1 km resolution rasters• Maximum value if >1 point in a cell
Gi* analyses
• Bisquare weights with 4 bandwidths• 2, 3, 4 & 5 km
• Identified “optimal” scale at each location • Bandwidth with most extreme Gi* score
Visual comparison with lithology and landform
• Landform (terrain): • Slope gradient • Longitudinal curvature
Rate of change of slope gradient
+ve = Convex up = spur line
-ve = Concave up = break of slope
0 = Planar
• Circular analysis windowsRadii: 1 & 5 km (local & regional)
• SRTM 3 arc second DEM
Conclusions
• Hotspots broadly consistent with lithology
• Weak association with landform• and terrain is controlled by lithology...
• Finer detail possibly due to other causes• e.g. Pb & anthropogenic activities
Jenny’s CLORPT model
• Soil = f (Climate,Organic,Relief,Parent material,Time)
Future
• Use alternate expected values• Environmental guidelines• Economic grade
• Analyse as indicators• Binary above/below threshold