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Mapping local knowledge through spatial text mining

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The ecological knowledge of local residents in a given area is valuable information that can supplement physical environmental data. Like ecosystems, ecological knowledge is influenced by geographical effects. Hence, it can be effectively represented as a map. However, few studies have represented ecological knowledge spatially. This is largely attributable to the difficulties in quantifying oral materials of ecological knowledge. This study attempts to quantify and map ecological knowledge through spatial text mining for Namyangju, a major water supply source for the Seoul metropolitan area, in Korea. The result shows the geographical trend; in other words, in Namyangju, there were such ecological functions as surface water (18.671), flood prevention (6.793), wildlife habitat (5.113), environment remediation (3.332), Han River inflow (3.043), food chain (2.875), student activities (2.708), aquatic insect (2.375), and research (2.171). Geographically, there have been various water-related contents focusing on the Han River (e.g., surface water, flood prevention, and Han River inflow), leading to civic activities (e.g., environment remediation, student activities, and research) around city center rivers; geographical trends in which biodiversity has been emphasized at the foot of the mountain (e.g., wildlife habitat, food chain, and aquatic insect) were identified. Such a mapping of ecological knowledge can serve as reference material for a range of environmental and spatial policies.

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Datasets for this research are included in Jung et al. (2020).


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This work was funded by the Korea Environment Institute as “Stakeholders’ needs mapping for just energy transition in renewable energy complex (RE2022-05).”

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Correspondence to Jae-hyuck Lee.

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Lee, Jh. Mapping local knowledge through spatial text mining. Landscape Ecol Eng 19, 243–255 (2023).

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