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

Zhao, Q. , Wentz, E. A., Fotheringham, S., Yabiku, S. T., Hall, S. J., Glick, J. A., Dai, J., Clark, M. and Heavenrich, H. (2016) Semi-Parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal. In: GIScience 2016, Montreal, QC, Canada, 27-30 Sep 2016, pp. 372-375. (doi:10.21433/B3118kv3n3bq)

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Abstract

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 (SGWR) 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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Qunshan
Authors: Zhao, Q., Wentz, E. A., Fotheringham, S., Yabiku, S. T., Hall, S. J., Glick, J. A., Dai, J., Clark, M., and Heavenrich, H.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Copyright Holders:Copyright © 2016 The Authors
Publisher Policy:Reproduced with the permission of the authors

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