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- Trends and topics in geographically weighted regression research from 1996 to 2019Publication . De La Hoz-M, Javier; Fernandez-Gómez, María José; Mendes, SusanaThis 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.
- Capturing the complexity of COVID-19 research: Trend analysis in the first two years of the pandemic using a bayesian probabilistic model and machine learning toolsPublication . De la Hoz-M, Javier; Mendes, Susana; Fernández-Gó, María Joséez; González Silva, YolandaPublications about COVID-19 have occurred practically since the first outbreak. Therefore, studying the evolution of the scientific publications on COVID-19 can provide us with information on current research trends and can help researchers and policymakers to form a structured view of the existing evidence base of COVID-19 and provide new research directions. This growth rate was so impressive that the need for updated information and research tools become essential to mitigate the spread of the virus. Therefore, traditional bibliographic research procedures, such as systematic reviews and meta-analyses, become time-consuming and limited in focus. This study aims to study the scientific literature on COVID-19 that has been published since its inception and to map the evolution of research in the time range between February 2020 and January 2022. The search was carried out in PubMed extracting topics using text mining and latent Dirichlet allocation modeling and a trend analysis was performed to analyze the temporal variations in research for each topic. We also study the distribution of these topics between countries and journals. 126,334 peerreviewed articles and 16 research topics were identified. The countries with the highest number of scientific publications were the United States of America, China, Italy, United Kingdom, and India, respectively. Regarding the distribution of the number of publications by journal, we found that of the 7040 sources Int. J. Environ. Res. Public Health, PLoS ONE, and Sci. Rep., were the ones that led the publications on COVID-19. We discovered a growing tendency for eight topics (Prevention, Telemedicine, Vaccine immunity, Machine learning, Academic parameters, Risk factors and morbidity and mortality, Information synthesis methods, and Mental health), a falling trend for five of them (Epidemiology, COVID-19 pathology complications, Diagnostic test, Etiopathogenesis, and Political and health factors), and the rest varied throughout time with no discernible patterns (Therapeutics, Pharmacological and therapeutic target, and Repercussion health services).
- GeoWeightedModel: An R-Shiny package for geographically weighted modelsPublication . De la Hoz-M, Javier; Mendes, Susana; Fernandez-Gómez, María JoséThis paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web pplication to perform techniques from a subarea of spatial Statistics known as Geographically Weighted (GW) models, such as Geographically Weighted Regression (GWR) and its extensions: Robust GWR, Generalized GWR, Heteroskedastic GWR, Mixed GWR, and ‘‘Scalable GWR), Geographically Weighted Principal Component Analysis, and Geographically Weighted Discriminant analysis. It also allows calculating a basic and robust Geographically weighted summary. The main goal of GeoWeightedModel package was to make the workflow easier to use, especially for those who are not familiar with the R environment. With GeoWeightedModel, analyses can be performed interactively (point-and-click way) in a web browser, making the applications easier for many more researchers. In addition with this tool, the results of the analyses can be mapped providing a valuable tool for exploring the spatial heterogeneity of the data.