Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models

Increasingly, geoscientists and spatial data scientists have shown interest in modeling and analyzing spatial phenomena characterized by the feature of spatial fuzziness. Applying fuzzy logic and fuzzy inference methods to fuzzy spatial objects leads to fuzzy spatial inference models. These models pursue the goal of discovering new meaningful findings from fuzzy spatial data, hence contributing to data knowledge discovery and sharing this goal with spatial data science. In this paper, we introduce a novel type of inference method called region inference; it combines spatial query processing with fuzzy inference methods. The objective is to capture all points that intersect a search object (e.g., a query window) and whose output values fulfill some specific user requirements (e.g., the points with the maximum or minimum inferred values). For this, we propose, evaluate, and compare query window inference methods in fuzzy spatial inference models. In addition, we show their characterization and applicability in real spatial applications.