The primary objective of mining spatial prevalent co-location patterns (SPCP) is to extract subsets of spatial features from large datasets, which are often found in close geographical neighbor. Traditional SPCP mining techniques have overlooked the intrinsic attributes of spatial instances. This paper investigates the impact of elevation attributes on SPCPs and proposes the ISCPM-ENM (Improved Spatial Co-location Pattern Mining with Enhanced Neighbor Relationship Measures) method, designed to improve the assessment of neighboring relationships among spatial instances. More importantly, this paper proposes a pioneering post-mining methodology that employs ISCPM-ENM to mitigate efficiency impacts when incorporating additional attributes. It also seamlessly integrates elevation attributes into the mining process for experimental purposes. Extensive experiments demonstrate that ISCPM-ENM offers higher accuracy than traditional methods, and the new post-mining method not only preserves time efficiency on par with conventional techniques but also significantly cuts memory usage, reducing it to approximately half that of traditional methods across datasets of any size.