How GIS and Data Analytics Are Revealing Hidden Risks in South African Mining

CLIENT: Minerals Council South Africa
FOCUS: GIS, data analytics, community engagement and mining operations

The mining industry generates vast amounts of information that helps track and manage critical impacts on workers, communities and the environment.

But that data is often fragmented – with information sitting across different datasets, reports, systems and operational environments.

The challenge is bringing these different sources together to build a clearer picture of what is happening, where risks are emerging and where action may be needed most.

The Ask 

RIIS worked with Minerals Council South Africa to bring these layers of information together.

The approach was applied across four areas: community dynamics, COVID-19 transmission, occupational health and safety, and tailings storage facility risk.

The work focused on strengthening Minerals Council’s ability to use data to:

• understand the relationship between mining operations and surrounding communities;
• identify geographic patterns and emerging areas of risk;
• support more targeted stakeholder engagement and community interventions;
• monitor the adoption of occupational health and safety practices;
• track and communicate COVID-19 transmission patterns across mining operations; and
• use Earth observation and machine learning to better understand risks associated with tailings storage facilities.

The objective was to make existing and newly generated information more useful for decisions.

The Methodology 

RIIS combined spatial analysis, demographic data, operational information, dashboards and Earth observation to create tools that could translate complex information into practical insights.

1. Mapping the relationship between mines and communities: Through MC2030+, RIIS conducted detailed demographic and spatial analysis of communities surrounding mining operations. The work brought together information about local populations and mining activity to provide a clearer picture of community dynamics and identify areas where targeted community interventions could have the greatest relevance.This included mapping community protests and identifying geographic hotspots, helping stakeholders understand where community concerns were concentrated and where further engagement or intervention could be prioritised.
2. Using spatial intelligence to monitor COVID-19: During the COVID-19 pandemic, RIIS applied spatial analytics to track cases among mining employees and analyse transmission patterns. Rather than treating cases as isolated numbers, the analysis examined where transmission was occurring and how patterns differed geographically, including ward-to-ward and inter-municipal transmission. This helped identify high-transmission areas and potential risk factors, supporting more targeted containment strategies.
3. Turning OHS adoption into something that could be seen and tracked: For MOSH — Mining Industry Occupational Safety and Health, RIIS developed a geographically integrated platform to monitor the adoption of safety practices across mining operations. Dashboards and maps enabled stakeholders to track progress geographically, identify areas requiring further attention and understand where leading practices were being adopted. The value was in moving from a question of “Are practices being adopted?” to a more actionable question: “Where are they being adopted, where are gaps emerging, and where should attention be directed?”
4. Applying Earth observation and machine learning to tailings risk
5. For Tailings Storage Facilities (TSFs), RIIS explored the use of Earth observation and machine learning as tools for comprehensive risk management and early warning.
6. Spatial data provided a way of understanding the location and potential scale of TSF-related risks, creating evidence that could support earlier intervention and more informed engagement with mining stakeholders.

What the data made possible

Across these applications, the value of the work extended beyond the individual tools.

For MC2030+, community mapping and protest tracking helped reveal patterns in community dynamics and identify areas where engagement and community projects could be better targeted. For COVID-19 monitoring, spatial analysis helped identify transmission hotspots and high-risk areas, supporting more targeted responses rather than treating the mining sector as a single geographic unit.

For MOSH, geographically integrated dashboards provided a way to track safety-practice adoption and identify where further support or intervention was required. For TSFs, Earth observation provided evidence about the location and potential scale of risk, strengthening the information available to the Minerals Council and its members when engaging stakeholders and advocating for action.

The bigger insight

The project demonstrates an important lesson for a data-rich sector like mining: the value of data is not in how much of it an organisation has. It is in what that data enables people to see, understand and do.

Across community engagement, health, safety and environmental risk, the underlying challenge was similar. Information existed but decision-makers needed a way to connect it, visualise it and interpret it in context.

GIS and analytics became the bridge between data and action.

This is particularly important in mining, where many of the sector’s most complex challenges are inherently spatial. A community issue happens somewhere. A safety practice is adopted across particular operations. Disease transmission follows geographic patterns. A tailings facility occupies a specific landscape and presents risks to surrounding areas.

Bringing those dimensions together creates a different kind of intelligence: not simply knowing what is happening, but understanding where it is happening, who is affected and where intervention may matter most.

The Impact

The work strengthened the ability of the Minerals Council and its members to use spatial and analytical intelligence across several areas of mining decision-making.

It contributed to:

• Better community intelligence through demographic and protest mapping;
• More targeted COVID-19 responses through geographic identification of transmission hotspots;
• Improved visibility of OHS practice adoption through spatially integrated dashboards;
• Stronger evidence around tailings risk through Earth observation and machine learning; and
• More targeted stakeholder engagement and resource allocation by making complex patterns easier to identify and act upon.

More broadly, the project demonstrated how data science can move from being a technical capability sitting behind the organisation to becoming part of how mining decisions are made.

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