Geographically Weighted Models: Diversified Application and Development Actions
Geographically Weighted Models: Diversified Application and Development Actions
Social Sciences Hub
Date: Thursday 21 November 2024
Time: 15:00 - 16:30
Venue: Online (Zoom) / UBDC Conference Room, 7 Lilybank Gardens
Category: Academic events
Website: www.ubdc.ac.uk/events/geographically-weighted-models-diversified-application-and-development-actions
With the recent rise in studies focused on characterising spatial non-stationarity in processes that vary across space, Geographically Weighted (GW) models have emerged as the de facto statistical toolkit for addressing such challenges. This has attracted a diverse range of users and developers from across various scientific disciplines.
In this talk, I will introduce several newly developed GW models and demonstrate their applications through a series of case studies from different fields. To support the widespread adoption of GW models in research, I will also present the R package ‘GWmodel’ alongside a newly developed standalone software, ‘GWmodelS,’ which is built upon the GWmodel framework. The standalone software offers several key advantages, including a user-friendly graphical interface, enhanced operational efficiency, and improved accessibility. These features are designed to encourage usage across a broad spectrum of users, regardless of their technical expertise.
BIO: Dr. Binbin Lu holds a Ph.D. in Geocomputation from the National University of Ireland, Maynooth, where he was affiliated with the National Centre for Geocomputation. He is currently an Associate Professor at the School of Remote Sensing and Information Engineering, Wuhan University, China. Dr. Lu's research expertise spans geocomputation, spatial statistics, geographically weighted (GW) modelling, open-source GIS, R programming, and the analysis of spatio-temporal big data. As the main developer and maintainer of the GWmodel R package, he has integrated a wide range of localised techniques, such as GW regression and GW principal component analysis, providing valuable tools for spatial data analysis. He has authored peer-reviewed publications in leading journals on spatial statistics, spatial data science, and GIS. Currently, Dr. Lu serves as the principal investigator (PI) on two projects funded by the Natural Science Foundation of China (NSFC) and as co-PI on three key NSFC projects.
This hybrid event can be attended in-person or online via Zoom.