data&code.rar

The integrated application of geographic information systems (GIS) and ordered weighted averaging (OWA) has attracted significant attention because it reflects the risk-taking attitude of decision-makers and controls the trade-off between indicators. The reordering of spatial attributes by the GIS-OWA algorithm renders the relationship between evaluation results and input factors non-intuitive. We thus propose a factor-contribution measure to explore the interpretability of the GIS-OWA algorithm at the pixel-to-local scale. Taking the Golden Triangle of Southern Fujian Province of China as the study area and the ecological constraints as the research background, the new approach is used to identify the major factors that influence the evaluation of GIS-OWA. The results show that significant differences exist in the proportion and degree of aggregation of ecological factors in the ranked layer. A set of key factors exists in the distribution of the attribute contribution at the pixel scale, and the set remains relatively stable for different evaluation strategies. The sets of key factors for cells in different OWA-equivalence areas differ significantly, and an optimal window size exists for the spatial scope of the set under a given threshold. Contribution metrics prove to be effective tools for exploring the key inputs of the GIS-OWA algorithm.