DM-01 - Spatial Database Management Systems
A spatial database management system (SDBMS) is an extension, some might say specialization, of a conventional database management system (DBMS). Every DBMS (hence SDBMS) uses a data model specification as a formalism for software design, and establishing rigor in data management. Three components compose a data model, 1) constructs developed using data types which form data structures that describe data, 2) operations that process data structures that manipulate data, and 3) rules that establish the veracity of the structures and/or operations for validating data. Basic data types such as integers and/or real numbers are extended into spatial data types such as points, polylines and polygons in spatial data structures. Operations constitute capabilities that manipulate the data structures, and as such when sequenced into operational workflows in specific ways generate information from data; one might say that new relationships constitute the information from data. Different data model designs result in different combinations of structures, operations, and rules, which combine into various SDBMS products. The products differ based upon the underlying data model, and these data models enable and constrain the ability to store and manipulate data. Different SDBMS implementations support configurations for different user environments, including single-user and multi-user environments.
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.