Abstract
Abstract Categorical raster datasets often require upscaling to a lower spatial resolution to make them compatible with the scale of ecological analysis. When aggregating categorical data, two critical issues arise: (a) ignoring compositional information present in the high‐resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (b) restricting classes to those present in the high‐resolution dataset assumes validity of the classification scheme at the lower, aggregated resolution. I introduce a new scaling algorithm that aggregates categorical data while simultaneously controlling for information loss by generating a non‐hierarchical, representative, classification system for the aggregated scale. The Multi‐Dimensional Grid‐Point (MDGP) scaling algorithm acknowledges the statistical constraints of compositional count data. In a neutral‐landscape simulation study implementing a full‐factorial design for landscape characteristics, scale factors and algorithm parameters, I evaluated consistency and sensitivity of the scaling algorithm. Consistency and sensitivity were assessed for compositional information retention (IRcmp) and class‐label fidelity (CLF, the probability of recurring scaled class labels) for neutral random landscapes with the same properties. The MDGP‐scaling algorithm consistently preserved information at a significantly higher rate than other commonly used algorithms. Consistency of the algorithm was high for IRcmp and CLF, but coefficients of variation of both metrics across landscapes varied most with class‐abundance distribution. A diminishing return for IRcmp was observed with increasing class‐label precision. Mean class‐label recurrence probability was consistently above 75% for all simulated landscape types, scale factors and class‐label precisions. The MDGP‐scaling algorithm is the first algorithm that generates data‐driven, scale‐specific classification schemes while conducting spatial data aggregation. Consistent gain in IRcmp and the associated reproducibility of classification systems strongly suggest that the increased precision of scaled maps will improve ecological models that rely on upscaling of high‐resolution categorical raster data.
Highlights
Explicit ecological models rely on spatially exhaustive data layers at appropriate scales for the ecological process of interest (Lam & Quattrochi, 1992; Mas, Gao, & Pacheco, 2010), which often requires scaling datasets to the scale of analysis
Consistent gain in IRcmp and the associated reproducibility of classification systems strongly suggest that the increased precision of scaled maps will improve ecological models that rely on upscaling of high‐resolution categorical raster data
The simulation study demonstrated that the algorithm consistently delivers scaled class labels when generating scale‐specific classification systems
Summary
Explicit ecological models rely on spatially exhaustive data layers at appropriate scales for the ecological process of interest (Lam & Quattrochi, 1992; Mas, Gao, & Pacheco, 2010), which often requires scaling datasets to the scale of analysis. The presumption that the original class descriptors are valid at the aggregated lower resolution, regardless of scale factor, leads to uncontrolled loss of information content in each grid cell of the aggregated map, and potentially to fallacy in ecological models that use the oversimplified aggregated data. The nearest‐neighbour rule assigns the ‘tree’ class, the category closest to the centre of the scaled grid cell, and the random rule assigns the output class at random (Figure 1a) Application of these three algorithms to the same input data results in completely different class assignments to the up‐scaled grid cell, resulting in pure (100% cover) classes of either ‘tree’ or ‘grass’. An algorithm that implements scaling of classification schemes preferably provides a control mechanism for information loss, considering the relative class abundance (composition) and the spatial arrangement (configuration) of sub‐samples within samples. The algorithm presented here addresses the compositional information retention aspect
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