Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
This work addresses the need for more accurate spatial analysis in domains like healthcare by incorporating fuzziness into topological relations, though it is incremental as it builds on existing fuzzy RCC theory.
The authors tackled the problem of analyzing topological relations between fuzzy geographical regions in spatial databases by implementing a fuzzy region connection calculus method in PostGIS, and evaluation in disease relationship analysis showed it provides a more realistic and flexible view compared to existing methods.
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.