A methodology is proposed which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment of risks to human health from exposure to contaminated land, in a manner which preserves the spatial distribution of risks and provides a measure of uncertainty in the assessment. Maps of soil contaminant levels, which incorporate uncertainty, are produced from sparse sample data using sequential indicator simulation. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. An abundance of information is contained in results which can be interrogated at the population and individual level, and mapped to provide a powerful visual tool for risk managers, enabling efficient targeting of risk reduction measures to different locations.