Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to uncover valuable educational insights from individuals' social and spatial data traces. These traces are captured automatically through sensing technologies in physical learning spaces, and the research is commonly based on the theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, a systematic literature review is timely in order to provide educational researchers and practitioners with a detailed summary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of 25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyses were conducted to identify the citation networks, essential components, opportunities, and challenges enabled by SSLA. The findings illustrated that SSLA provides the opportunity to: (1) contribute unobtrusive and unsupervised research methodologies; (2) support educators' classroom orchestration through visualizations; (3) support learner reflection with continuous and reliable evidence; (4) develop novel theories about social and collaborative learning; and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. These findings could support learning analytics and educational technology scholars and practitioners to better understand and utilize SSLA to support future educational research and practice.