Data · dataset · 2026
<p>Supporting information.</p>
Listed in NCL Data
<div><p>This essay explores the advantages and limitations of using mixed-methods approaches to improve local multi-hazard risk (MHR) assessment and its inherent complexities.
Description
By combining desktop analysis, field observations, questionnaires and stakeholder interviews, we demonstrate how each method complements each other to fill data gaps, improve design of methods such as surveys, and increase the understanding of multi-hazard risk dynamics and challenges that become apparent in a local context.
Mixed-methods also allow for bridging the gap between scientific modelling and community realities, while reducing reliance on often limited datasets and standardised (often quantitative) methods that are often aimed at larger scale assessments. This combination of quantitative and qualitative methods is illustrated in a recent fieldwork experience, with the objective of suggesting Nature-based Solutions (NBS) to reduce climate risk for the island of Tenerife.
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In our research agenda, we emphasize the importance of planning ahead when implementing mixed-methods, interdisciplinary collaboration, understanding the feedback loops across methods and incorporating local knowledge to ensure that the adaptation strategies are both evidence-based and socially equitable. This iterative framework aims to enhance the understanding, feasibility and community acceptance of MHR adaptation strategies.</p></div>
Links
Where it is published
- DOI doi.org/10.1371/journal.pclm.0001070.s001 ↗
DOI / persistent id · from data ncl ac uk
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from data ncl ac uk
Topics
- From keywords
- Earth & Environmental Science · Life Sciences · Mathematics & Statistics · Social Science
- Inferred from text
- Physical geography and environmental geoscience 73%
Related
- Possibly the same as<p>Supporting information.</p>
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NCL Data | oai:figshare.com:article/34073887 | 18 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:3709 | enrichment · data ncl ac uk | taxonomy-embedding@1.1.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| description | source · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | |
| title | source · data ncl ac uk | connector:data_ncl_ac_uk@1.0.0 | /metadata/dc/title |