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The purpose of this dataset is to provide a spatially standardised representation of human‑disease risk at the municipal scale. It supports public‑health risk profiling, vulnerability assessments, multi‑hazard analysis, and the prioritisation of mitigation interventions in areas affected by environmental pollution, population‑driven exposure, and reduced capacity to manage disease outbreaks. The dataset ensures a consistent, evidence‑based foundation for informing disaster management planning, health surveillance programmes, resilience strategies, and municipal‑level decision‑making. |
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The purpose of this dataset is to provide a spatially standardised representation of human‑disease risk at the municipal scale. It supports public‑health risk profiling, vulnerability assessments, multi‑hazard analysis, and the prioritisation of mitigation interventions in areas affected by environmental pollution, population‑driven exposure, and reduced capacity to manage disease outbreaks. The dataset ensures a consistent, evidence‑based foundation for informing disaster management planning, health surveillance programmes, resilience strategies, and municipal‑level decision‑making. |
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Prepared by: Herman Booysen, Principal GIS Scientist / Associate Partner
Organisation: SRK Consulting
Additional sources:
Environmental pollution rasters (air, land)
Water quality indicators
Population density datasets
Local Municipality boundaries
Internal SRK modelling documentation and multi‑criteria analysis methods
Hazard analysis and interpretation findings contained in:
KZN DRA |
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<div style="font-family:'Segoe UI';font-size:14px;font-style:normal;font-weight:400;line-height:20px;">This dataset represents the municipal‑level distribution of human disease risk across the study area. It is derived from a multi‑criteria spatial model integrating key environmental and population‑related determinants of disease burden, including reclassified land‑use exposure factors, water quality indicators, population density, land‑based pollution, and air pollution. These factors were combined using a weighted‑sum raster overlay to generate a continuous Human Disease Potential surface. The resulting raster was summarised using zonal statistics for each Local Municipality to produce a municipal‑level <strong>Human_Disease</strong> score. Additional capacity‑to‑cope indicators were joined to the municipal dataset and used to compute a composite <strong>Vulnerability_Risk</strong> index. The final dataset enables disaster management practitioners, health analysts, planners, and risk professionals to identify municipalities with elevated human‑disease vulnerability and incorporate this information into multi‑hazard assessments, health‑risk planning, early‑warning systems, and resource‑allocation strategies.</div> |
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<div style="font-family:'Segoe UI';font-size:14px;font-style:normal;font-weight:400;line-height:20px;">This dataset is intended for public‑health and disaster‑risk assessment, strategic planning, hazard profiling, and municipal vulnerability analysis.<br>It is <strong>not suitable for clinical decision‑making or fine‑scale epidemiological modelling</strong> without additional field studies or verified health datasets.</div> |
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LDR.SRK_Munic_Human_Disease_Risk |
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["Human Disease","Vulnerability","Public Health","Pollution","Water Quality","Population Density","Multi\u2011Criteria Analysis","Weighted Overlay","Zonal Statistics","Hazard Mapping","Municipality","South Africa","GIS"] |
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en-ZA |
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