Automatic extraction of spatial information from natural language can boost human-centered applications that rely on spatial dynamics. The field of cognitive linguistics has provided theories and cognitive models to address this task. Yet, existing solutions tend to focus on specific word classes, subject areas, or machine learning techniques that cannot provide cognitively plausible explanations for their decisions. We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language. The proposed rule-based and explainable approach contributes constructions and preposition schemas and outperforms previous solutions on the CLEF-2017 standard dataset.