Semi-parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal

What:
Poster
When:
2 hours
Tags:
S-GWRInvasive plant speciesSocio-ecological factorsCommunity forestsEcosystem services
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0
Geographically weighted regression (GWR) is a spatial statistical methodology to explore the impact of non-stationarity on the interaction between spatially measured dependent and independent variables. In this paper we use a semi-parametric geographically weighted regression (S- GWR) and demonstrate the effectiveness of the method on a case study on socio-ecological factors on forest vulnerability. The case study is based on community forests in and around the buffer zone of Chitwan National Park, Nepal, a biodiversity hotspot that is being rapidly degraded by exotic invasive plant species. This research integrated heterogeneous data sources such as observational ecological surveys, household interviews, and remotely sensed imagery. These data were utilized to extract and represent invasive plant species coverage, human activity intensity, topographical parameters and vegetation greenness indices. Research findings both demonstrate the S-GWR method and offer possible interventions that could slow the catastrophic spread of invasive plant species in Chitwan, Nepal.
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Arizona State University
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Arizona State University
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