Abstract
This paper describes three important inter-related factors in multipleresolution categorical map comparison that can affect 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
