Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases

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.

💡 Research Summary

The paper addresses a fundamental shortcoming in contemporary geographic information systems (GIS) and spatial databases: the assumption that geographic objects have crisp, well‑defined boundaries. In reality, natural features such as lakes, forests, or disease‑affected zones often exhibit vague edges due to measurement error, temporal change, or inherent ecological variability. Traditional topological reasoning, especially the Region Connection Calculus (RCC), treats spatial relations as binary predicates (e.g., disconnected, externally connected, partially overlapping). This binary view discards the gradations that exist in the real world and can lead to misleading conclusions in applications that rely on spatial relationships, such as epidemiology, urban planning, or environmental monitoring.

To overcome this limitation, the authors propose a fuzzy extension of RCC (fuzzy RCC) that integrates fuzzy set theory with the classic RCC framework. Each spatial object is represented by a fuzzy membership function rather than a crisp geometry. The membership function is constructed using a distance‑based Gaussian kernel: points near the interior of the region have high membership values (close to 1), while points near the perceived boundary receive progressively lower values. The degree of connection between two objects is then computed as an integral over the product of their membership functions, yielding a continuous value in the interval