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Vector dataset representing relative vulnerability levels for local municipalities in KwaZulu‑Natal (KZN), derived from the integration of social vulnerability indicators. The dataset was produced by enriching the official local municipality boundary layer with multiple socio‑economic, service delivery, land‑use, and hazard‑related indicators and calculating composite vulnerability indices for disaster risk assessment and planning. |
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Vector dataset representing relative vulnerability levels for local municipalities in KwaZulu‑Natal (KZN), derived from the integration of social vulnerability indicators. The dataset was produced by enriching the official local municipality boundary layer with multiple socio‑economic, service delivery, land‑use, and hazard‑related indicators and calculating composite vulnerability indices for disaster risk assessment and planning. |
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Municipal Demarcation Board (MDB), Produced by Herman Booysen, SRK Consulting.
Project context: Disaster Management and climate risk assessment for KwaZulu‑Natal (internal GIS workflows). |
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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;"><p>This dataset represents a composite vulnerability assessment for local municipalities in KwaZulu‑Natal, developed to support disaster risk profiling, planning, and prioritisation. Vulnerability modelling was conducted based on relative vulnerability levels associated with social, structural, economic, and environmental dimensions.</p><p>Social vulnerability mapping was based on demographic and socio‑economic indicators reflecting dependency and deprivation. Vulnerable age groups (children younger than 14 years and adults older than 65 years) were combined with indicators of poverty and food insecurity to represent populations with reduced resilience and adaptive capacity. These indicators were integrated into a composite social vulnerability index and visualised through thematic mapping in a Geographic Information System (GIS) environment. </p><p>Indicators were expressed as proportions at municipal level, classified using quantile‑based methods, and visualised through choropleth mapping to identify spatial patterns and clusters of higher and lower vulnerability. Composite vulnerability indices were calculated by integrating reclassified indicator scores, producing relative vulnerability rankings rather than absolute risk measures.</p><p>This dataset represents a screening‑level vulnerability product and does not provide real‑time conditions or predictive outcomes. It is intended to support strategic planning, comparative assessment, and prioritisation within disaster management and climate risk contexts.</p></div></div></div></div> |
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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;"><p>This dataset represents a screening‑level vulnerability assessment intended to support disaster risk profiling, planning, and prioritisation at municipal and district scales.</p><p>It is <strong>not suitable</strong> for:</p><ul><li>Operational disaster response</li><li>Real‑time vulnerability monitoring</li><li>Site‑specific or household‑level analysis</li></ul><p>Results should be interpreted comparatively and used in conjunction with supporting socio‑economic data, expert judgement, and local validation.</p></div></div></div></div> |
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LDR.SRK_LMVulnerability |
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["Vulnerability","local municipality","KwaZulu Natal","KZN","disaster management","social vulnerability","economic vulnerability","service delivery","GIS","risk assessment"] |
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en-ZA |
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150000000 |
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