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This dataset represents the municipal‑level distribution of landslide hazard across the study area. The dataset is based on a terrain‑driven susceptibility model incorporating slope (derived from a Digital Elevation Model), rainfall intensity patterns, and soil/geological characteristics. These factors were combined into a composite landslide susceptibility raster surface, which was summarised for each municipality using zonal statistics. The resulting feature class enables planners, disaster management practitioners, and risk analysts to identify municipalities with elevated landslide susceptibility and integrate this information into multi‑hazard assessments, spatial development frameworks, and geotechnical risk reduction strategies.
The purpose of this dataset is to provide a spatially standardised representation of landslide susceptibility at the municipal scale. It supports hazard profiling, geohazard management, slope stability planning, and the prioritisation of interventions in areas where landslide risk is elevated due to steep slopes, high rainfall, or sensitive soil conditions. |
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This dataset represents the municipal‑level distribution of landslide hazard across the study area. The dataset is based on a terrain‑driven susceptibility model incorporating slope (derived from a Digital Elevation Model), rainfall intensity patterns, and soil/geological characteristics. These factors were combined into a composite landslide susceptibility raster surface, which was summarised for each municipality using zonal statistics. The resulting feature class enables planners, disaster management practitioners, and risk analysts to identify municipalities with elevated landslide susceptibility and integrate this information into multi‑hazard assessments, spatial development frameworks, and geotechnical risk reduction strategies.
The purpose of this dataset is to provide a spatially standardised representation of landslide susceptibility at the municipal scale. It supports hazard profiling, geohazard management, slope stability planning, and the prioritisation of interventions in areas where landslide risk is elevated due to steep slopes, high rainfall, or sensitive soil conditions. |
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Prepared by: Herman Booysen, Principal GIS Scientist / Associate Partner
Organisation: SRK Consulting |
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5000 |
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[] |
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<div style="text-align:Left;"><div><div><div style="font-family:'Segoe UI';font-size:14px;font-style:normal;font-weight:400;line-height:20px;">This dataset contains municipal‑level landslide hazard values derived from a composite susceptibility model built using DEM‑derived slope, long‑term rainfall patterns, and soil/geological characteristics. A weighted overlay was used to generate the regional landslide susceptibility raster, reflecting the combined influence of steep gradients, high precipitation, and weak or weathered soil conditions on slope instability. Zonal Statistics were applied to calculate susceptibility metrics for each municipality, with the median value selected as the most robust indicator of overall landslide hazard. The resulting values were joined to the municipal boundary layer and classified into categorical hazard levels for integration into multi‑hazard assessments, geotechnical risk reduction strategies, and development planning processes.</div><p> </p></div></div></div> |
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<div style="font-family:'Segoe UI';font-size:14px;font-style:normal;font-weight:400;line-height:20px;"><ul><li>Intended for hazard and geotechnical risk assessment, spatial planning, and strategic analysis.</li><li>Not suitable for site‑specific geotechnical design without detailed field investigation.</li></ul></div> |
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LandSlideKZN |
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["Landslide","Slope Instability","Mass Movement","Geohazards","Susceptibility Mapping","DEM","Weighted Overlay","Zonal Statistics","Multi\u2011Hazard Analysis","Municipality","South Africa","GIS"] |
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en-ZA |
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150000000 |
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