Cultural ontology-enhanced attribute matching for community-based geo-spatial vulnerability mapping in remote agrarian settlements
Keywords:
agricultural stressor, cultural ontology, participatory vulnerability, geospatial analysisAbstract
Vulnerability mapping in remote agrarian settlements is constrained by a persistent disconnect between qualitative local knowledge and quantitative geospatial data.
This study presents a cultural ontology-enhanced attribute-matching framework for community-based geospatial vulnerability mapping in remote agrarian settlements, addressing the disconnect between qualitative local knowledge and quantitative geospatial data by systematically integrating vernacular risk indicators into geographic information system (GIS) analysis.
Cultural vulnerability ontology (CVO) is constructed from community-generated narratives using attention-based term extraction and aligned with geospatial variables through a graph neural network (GNN)-based cross-modal framework. The approach is adopted in Amta-II Community Development Block, West Bengal, India, a flood-prone agrarian region characterized by low-lying terrain and agriculture-dependent livelihoods, where a corpus of 12,000 vernacular text segments was collected through participatory methods to extract culturally significant indicators.
Comparative evaluation against baseline methods shows improved alignment performance, with gains of up to 22% in Precision@5 and 37% over conventional rule-based approaches, while the resulting culturally grounded vulnerability index demonstrates stronger agreement with community-identified risk zones, achieving an Intersection-over-Union (IoU) of 0.64 compared to 0.48 under traditional AHP-based weighting.
These results indicate that integrating culturally embedded knowledge into geospatial frameworks enhances the contextual accuracy and interpretability of vulnerability assessments, offering a scalable, adaptable approach for spatial decision-making in data-scarce, climate-sensitive rural environments.
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