Four Advances in Handling Uncertainties in Spatial Data and Analysis

Data quality and uncertainty modeling for spatial data and spatial analyses is regarded as one of the disciplines of geographic information science together with space and time in geography, as well as spatial analysis. In the past two decades, a lot of research efforts have been devoted to uncertainty modeling for spatial data and analyses, and this paper presents our work in this research area. In particular, four progresses in the area are given: (a) from determineto uncertainty-based representation of geographic objects in GIS; (b) from uncertainty modeling for static data to dynamic spatial analyses; (c) from modeling uncertainty for spatial data to models; and (d) from error descriptions to quality control for spatial data.