Spatial data are important in today applications, so that every day we witness the extended use of them. Spatial data include the location and properties of spatial objects such as points, lines and regions. Spatial relations constitute an important form of human understanding of spatial formation.In this context, the relationships between spatial objects, especially topological relations between regions, have attracted considerable attention.However,real-world spatial regions such as lake or forest have no exact boundaries and are fuzzy. So it would be better to define the relationships between them in as fuzzy relations. Fuzzy topological relationshave application in many contexts, including path tracking algorithms based on fuzzy relations, medical diagnosis of the patient's file, extracting topological relations from the Web, image interpretation, robot navigation and manipulation, brain MRI segmentation, soil science and many other contexts. So far, several researchesare conducted on modeling fuzzy spatial topological relations,and progresses have been made. Some methods for modeling fuzzy spatial regions and fuzzy relationships between them have been proposed.However, given the huge amount of data stored in spatial databases,and the fact that existing spatial database systems are based on non-fuzzy relations, werequire data processing methods that are based on fuzzy spatial relations.Therefore, the fuzzy enrichment of relations in spatial databases can improve data processing techniques and decision making based on them, as well as improving the user interface in comparison to most today systems. In this thesis,a novel method is proposedfor implementing fuzzy relations in spatial databases that is applicable tomany applications.As an important application, therelationships used to analyse the spatial relationship between diseaseswill be evaluated. Additionally, a method based on fuzzy RCC relationsfor fuzzification of an important group of spatial queriesnamely the skyline operatoris proposedthat can be used in decision support, data visualization and spatial databases applications.The proposed algorithms have been implemented and evaluated on real-world spatial datasets. The results of the evaluation of these algorithms show that more flexibility in comparison with existing methods, and speed and quality of the results are appropriate. Keywords: Fuzzy Spatial Reasoning, Spatial Skyline Query, Spatial Data Analysis