Trends and topics in geographically weighted regression research from 1996 to 2019
[EN]This research was conducted in order to improve the understanding of the struc-ture, 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...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión borrador |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/154608 |
| Acceso en línea: | http://hdl.handle.net/10366/154608 |
| Access Level: | acceso abierto |
| Palabra clave: | Bibliometric analysis Collaboration pattern Geographically weighted regression Latent Dirichlet allocation Machine learning Topic modelling 1209 Estadística |
| Sumario: | [EN]This research was conducted in order to improve the understanding of the struc-ture, 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 top-ics from the collected literature by utilising latent Dirichlet allocation and a ma-chine 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 |
|---|