Importance of matrix construction for multiple-resolution categorical map comparison

This paper describes three important inter-related factors in multiple-resolution categorical map comparison that can effect the measured association between two maps: resolution effect, matrix effect, and parameter effect. We demonstrate how cross-tabulation matrix construction affects the quantitative comparison of categorical maps by analyzing synthesized and empirical examples at fine and several coarser resolutions. We consider three soft classification rules and their corresponding matrices-multiplication, minimum, and composite. We measure association using popular coefficients of agreement at multiple resolutions. The results show that the composite matrix has useful interpretability because it assesses clearly the information of quantity and spatial allocation concerning map categories.