From Uncertainty Description to Spatial Data Quality Control

Our research development on error and uncertainty modeling in spatial data and spatial analysis can be classified as the following stages: identifying the sources of uncertainties, describing errors in spatial data, quantifying error propagation in spatial analysis and finally controlling the quality of spatial data. This paper presents our study on quality control of spatial data. Quality control for spatial data refers to developing methods to ensure the final spatial data are produced to meet the user requirements. Here, spatial data may include, for example, object-based data, field-based data and digital elevation models. Description of uncertainties and errors was a research focus of early studies in the area, including the description of uncertainties in spatial data, spatial analysis or spatial models. From research development point of view, uncertainty description is a necessary first step, and a step further is to control or even reduce the uncertainties in the spatial data, analyses or models, if we are able to reach this objective. Therefore, the quality of spatial data for the final users is ensued. In this paper, the research on the quality control for the following three data and model is presented: Quality control for object-based spatial data – to control geometric quality of vector spatial data by the least square adjustment method; Quality control for field-based spatial data – to geometrically correct high resolution satellite imageries by pointand line-based models; and Quality control for digital elevation models – to improve DEM accuracy by newly proposed interpolation models.