Spatially integrated social science is a broad term used to describe the integration of space and place in social science research using Geographic Information Systems (GIS). It includes qualitative GIS approaches, such as geo-ethnology and geo-narratives, which combine qualitative social data with GIS and represent an emerging approach with significant potential for facilitating new insights into the dynamic interactions between mining companies and host communities. Mine operations are unique in their complexity, both in terms of the dynamic and diverse nature of issues and the requirement to integrate knowledge, theories, and approaches from a range of disciplines. In this paper we describe the potential for spatially integrated social science using qualitative GIS to understand the social impacts of mining. We review current literature and propose a framework that incorporates quantitative and qualitative knowledge across social and biophysical domains within a multi-user approach. We provide examples to illustrate how our approach could support past, present, and future assessment of socio-environmental systems in large-scale mining. We conclude by discussing the need for a multi-disciplinary approach to support decision makers and local stakeholders in considering complex social and environmental scenarios.

As the mining industry expands, a comprehensive understanding of its socioeconomic risks and benefits is urgently needed. This paper systematically reviews 71 studies (1996–2021) that utilized spatially integrated approaches to evaluate socioeconomic mining impacts. The number of studies that utilize geographic information systems and remote sensing to study mining impacts increased from 2014 onwards. A framework was used to classify the mining impacts studied in the literature and all eight framework categories – Environment, Land, People, Community, Culture, Livelihoods, Infrastructure and Housing – were captured by the literature though Culture was least studied. Coal mining, active mining phase, Landsat data and classic remote sensing algorithms were most highlighted. Future research should focus on advancing geospatial technology like artificial intelligence (AI) to better capture intangible socioeconomic impacts, under-researched minerals and long-term mine lifecycle components. Spatially referenced social data can improve stakeholder involvement and support spatially explicit planning to ensure sustainability.