A spatial data set is consistent if it satisfies a set of integrity constraints. Although consistency is a desirable property of databases, enforcing the satisfaction of integrity constraints might not be always feasible. In such cases, the presence of inconsistent data may have a negative effect on the results of data analysis and processing and, in consequence, there is an important need for data-cleaning tools to detect and remove, if possible, inconsistencies in large data sets. This work proposes strategies to support data cleaning of spatial databases with respect to a set of integrity constraints that impose topological relations between spatial objects. The basic idea is to rank the geometries in a spatial data set that should be modified to improve the quality of the data (in terms of consistency). An experimental evaluation validates the proposal and shows that the order in which geometries are modified affects both the overall quality of the database and the final number of geometries to be pro...

Rank-based strategies for cleaning inconsistent spatial databases

Spatial data quality (data accuracy, precision, consistency and so on) is a key issue in Geographic Information System. Geographical boundary inconsistency will directly affect the correctness and efficiency of analysis in GIS application. This paper describes a framework for checking and correcting geographical boundary inconsistency. Two kinds of inconsistency are identified: geometric inconsistency and topological inconsistency. Geometric inconsistency refers to the unequal of point number or coordinates of overlapping boundary. Topological inconsistency means that adjacent boundary is not able to strictly guarantee topological consistent according 9-intersection model, and generate series of gap and fragment area. Different checking and correcting methods are adopted for these two different kinds of inconsistency. In the first case, the generalized algorithm of node snapping is used, and inconsistency is solved by the mathematical methods of repeating points removing, method of averaging, method of projection, etc. In the second case, the inconsistency is solved by buffer operation, Delaunay triangulation, overlay analysis, etc. A complete framework and algorithm procedure are given in this paper to detect and correct the boundary inconsistency problem in GIS. Meanwhile, an application of inconsistency correction to land-use data based on this framework is conducted.

A framework for correcting geographical boundary inconsistency

Data quality in spatial databases has been widely recognized as one of the most critical issues in GIS Its key dimension is assessing data suitability, including data accuracy, precision, uncertainty, compatibility, consistency, completeness, availability and timeliness recorded in the data lineage Over decades, data quality has attracted much attention in the GIS community, and various aspects of data quality have been investigated However, research on topological consistency remains very weak though it is crucial for GIS spatial analysis This paper proposes a new technique on handling topological consistencies as well as tracing error propagation First, inconsistencies of digital geographic lines are categorized into two groups with or without equal numbers of vertexes Then a standard formalizing approach has been introduced to make numbers of vertexes matched in the two poly-lines so that the generalized algorithm of node snapping can be used to remove the inconsistencies between the two poly-lines Third, the equations of estimating the coordinates of the projected points and their errors in the process of standard formalization have been derived The formulae of calculating the length, together with its accuracy, of new generated poly-lines have also been obtained Finally, simple examples are provided to demonstrate how to implement the proposed technique into practice

Feng Xue-zhi

Papers

Dealing with Inconsistency between Digital Geographic Lines from Multi-data Sources in GIS