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