Abstract
User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.
Highlights
Geostatistical methods were originally developed for applications in the mining industry [1], but are nowadays used in different fields of science, including climatology, geology, agriculture and epidemiology
In MBGapp, the main areas comprise four tabs: “Explore”, “Variogram”, “Estimation” and “Prediction”. These four tabs correspond to the steps of a geostatistical analysis that we describe in more detail in the “Results” section
We have introduced MBGapp, a user-friendly web application to facilitate the teaching and learning of geostatistical methods for a target audience consisting of population health scientist with limited or no programming skills
Summary
Geostatistical methods were originally developed for applications in the mining industry [1], but are nowadays used in different fields of science, including climatology, geology, agriculture and epidemiology. There has been an increasing need for scientists from varied backgrounds to learn and apply geostatistical methods to address substantive scientific problems. Training of population health scientists in geostatistics poses pedagogical challenges that vary according to the trainee’s prior knowledge of the foundations of statistical and mathematical science. In the authors’ experience, the central challenge is to teach why MBG methods are useful tools for population health research and how to interpret the results of an MBG analysis, while keeping the mathematical formalism to a minimum.
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