GeoWeightedModel : An R-Shiny package for Geographically Weighted Models

[EN]This paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web application 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

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 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 publication

Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO

[EN]The study of biotic and abiotic factors and their interrelationships is essential in the preservation of sustainable marine ecosystems and for understanding the impact that climate change can have on different species. For instance, phytoplankton are extremely vulnerable to environmental changes and thus studying the factors involved is important for the species’ conservation. This work examines the relationship between phytoplankton and environmental parameters of the eastern equatorial Pacific, known as one of the most biologically rich regions in the world. For this purpose, a new multivariate method called MixSTATICO has been developed, allowing mixed-type data structured in two different groups (environment and species) to be related and measured on a space–time scale. The results obtained show how seasons have an impact on species–environment relations, with the most significant association occurring in November and the weakest during the month of May (change of season). The species Lauderia borealis, Chaetoceros didymus and Gyrodinium sp. were not observed in the coastal profiles during the dry season at most stations, while during the rainy season, the species Dactyliosolen antarcticus, Proboscia alata and Skeletonema costatum were not detected. Using MixSTATICO, species vulnerable to specific geographical locations and environmental variations were identified, making it possible to establish biological indicators for this region

LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools

[EN]In this paper we propose an open source application called LDAShiny, which provides a graphical user interface to perform a review of scientific literature using the latent Dirichlet allocation algorithm and machine learning tools in an interactive and easy-to-use way. The procedures implemented are based on familiar approaches to modeling topics such as preprocessing, modeling, and postprocessing. The tool can be used by researchers or analysts who are not familiar with the R environment. We demonstrated the application by reviewing the literature published in the last three decades on the species Oreochromis niloticus. In total we reviewed 6196 abstracts of articles recorded in Scopus. LDAShiny allowed us to create the matrix of terms and documents. In the preprocessing phase it went from 530,143 unique terms to 3268. Thus, with the implemented options the number of unique terms was reduced, as well as the computational needs. The results showed that 14 topics were sufficient to describe the corpus of the example used in the demonstration. We also found that the general research topics on this species were related to growth performance, body weight, heavy metals, genetics and water quality, among others.FCT (Fundação para a Ciência e a Tecnologia)Centro 2020 program, Portugal2020, European UnionEuropean Regional Development Fun