AM-107 - Spatial Data Uncertainty
Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.
KE-34 - Multi-Organizational GIS Coordination
For many years, collaboration has been a key cornerstone in the success of efforts achieved by the geospatial community. When paired with governance, collaborative efforts often lead to sustainability and have the effect of broadening the benefits that can be achieved. The following text shares how the geospatial community uses collaboration and governance as tools to achieve benefits across the community. Case studies are provided to illustrate the process and the outcomes achieved.