Trends and topics in geographically weighted regression research from 1996 to 2019

This research was conducted in order to improve the understanding of the structure, contents, and trend of topics within the existing literature in the field of geographically weighted regression. Additionally, it intended to determine and produce a mapping of scientific networks in the domain of ge...

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Bibliographic Details
Published in:Area (London 1969) 2022-03, Vol.54 (1), p.105-117
Main Authors: De La Hoz‐M, Javier, Fernandez‐Gómez, María José, Mendes, Susana
Format: Article
Language:English
Subjects:
bibliometric analysis
Bibliometrics
Citation analysis
collaboration pattern
Dirichlet problem
geographically weighted regression
latent Dirichlet allocation
Machine learning
Mapping
Regression
topic modelling
Topics
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Summary:This research was conducted in order to improve the understanding of the structure, contents, and trend of topics within the existing literature in the field of geographically weighted regression. Additionally, it intended to determine and produce a mapping of scientific networks in the domain of geographically weighted regression. The proposed methodology implements a combination of bibliometric techniques and modelling of topics in order to extract the latent topics from the collected literature by utilising latent Dirichlet allocation and a machine learning tool. The results identified the most prolific authors, the most cited authors, the most representative articles and journals, and the countries which are responsible for the publications. This research was conducted in order to improve the understanding of the structure, contents, and trend of topics within the existing literature in the field of geographically weighted regression.
ISSN:0004-0894
1475-4762
DOI:10.1111/area.12757