Spatial range queries are used on a daily basis in real-life applications, such as Google Maps and Yelp. Many of those applications may need to rank the spatial objects, enclosed by the spatial range, before presenting the result to the end-user. Existing systems rank spatial objects according to different rules, such as average user rating, distance to the user's location, etc.. The popularity of social networks allowed many applications to leverage the social graph in delivering a social-proximity aware ranked list of spatial objects. In this paper, we formally define a query that returns the top-k spatial objects in a given spatial region and rank them according to the social proximity of these objects to the querying user (SKNNGEO). Furthermore, a framework that integrates a joint search on both the social and spatial domains is proposed to efficiently solve the SKNNGEO query. The paper evaluates the proposed approach using real dataset extracted from the Yelp application. Extensive experiments show that the proposed approach outperform existing baseline approaches in processing SKNNGEO query.