Ternary spatial relations for error detection in map databases

Abstract

The quality of data in spatial databases greatly affects the performance of location-based applications that rely on maps such as emergency dispatch, land and property ownership registration, and delivery services. The negative effects of such dirty map data may range from minor inconveniences to life-threatening events. Data cleaning usually consists of two steps - error detection and error rectification. Data cleaning is a demanding and lengthy process that requires manual interventions of data experts, in particular where for complex situations involving the consistency of relationships between multiple objects. This thesis presents computational methods developed to automate the detectio..