Abstract:
In mapping and analyzing geographical phenomena, data are usually portrayed to be accurate without error. However, spatial data are often estimates derived from surveys, and are associated some levels of uncertainty (i.e. standard error) which make the estimates are unreliable. Ignoring uncertainty information in estimates may produce misleading results and generate spurious spatial patterns or relationships. Approaches dealing with spatial data quality have been developed decades ago, but they are mostly limited to visualize the variation of reliability, failing to incorporate data quality information in mapping and analysis. Without taking steps to address the uncertainty and its propagation in mapping and data analysis, the derived products and results may be misleading.
