Data analytics, statistics, and more
This post provides a brief introduction to species distribution modelling and illustrates how machine learning can be used to predict the range of a species based on a set of locations where it has been observed.
This post examines several methods for conducting temporal trend analysis using censored data that do not substitute artificial values for non-detects. Parametric methods are based on censored regression using maximum likelihood estimation. Nonparametric methods are based on Kendall’s tau and the Akritas-Theil-Sen line.
Data visualization is an important element of the data science process and the broader data presentation architecture discipline. This post will focus on performing some basic spatial data analysis using Bigfoot sightings in North America and the R language for statistics and visualization.
This post illustrates the use of rayshader, an R library that uses elevation data in a base R matrix and a combination of raytracing, hillshading algorithms, and overlays to generate 2D and 3D maps. A surface relief map created using digital elevation data will be rendered using rayshader and ggplot2.
For this post, we will evaluate rainfall in Australia using daily weather observations from multiple Australian weather stations. We will build a stacked ensemble classification model using the H2O machine learning platform for use in predicting if there will be rain tomorrow.