Lecture

Geographically Weighted Regression

Description

This lecture covers Geographically Weighted Regression (GWR), a method for exploring spatial non-stationarity in relationships between variables. It discusses the basic hypothesis, influence of explanatory variables, W Matrix, Kernel functions, and the comparison between Ordinary Least Squares (OLS) and GWR. The lecture also includes a case study on estimating net primary production in Chinese forest ecosystems using GWR. Conclusions highlight the advantages of GWR in providing location-specific insights and reducing bias compared to OLS.

Instructors (2)

est culpa nisi fugiat

Aliqua incididunt incididunt dolor veniam fugiat veniam quis ut eu Lorem ut quis duis in. Eu mollit consequat reprehenderit consequat do occaecat culpa nostrud aliqua quis labore dolore elit velit. Nostrud occaecat minim magna magna irure dolore sint ea qui mollit.

eu ea

Nisi laborum laboris non enim eiusmod adipisicing. Magna minim occaecat irure aliquip tempor proident duis esse et voluptate enim. Anim enim Lorem Lorem tempor occaecat cillum enim nostrud eiusmod non id. Enim aliquip enim ea eiusmod ea excepteur ullamco commodo ad magna.

Official source

https://mediaspace.epfl.ch/media/0_tt4bz4t8

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related lectures (42)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.