Heatmap to Visualize Spatio-Temporal Data

This post shows how to create a heatmap with geom_tile() to visualize the spatio-temporal evolution of the vegetative period in the Chaudière-Appalaches region.

Side by side interactive map with {leaflet} and {leaflet.extras2}

This tutorial shows how to create an interactive side-by-side map visualization of the mean temperature for 2024 and 2100 for the Chaudière-Appalaches region in Quebec using {leaflet} and {leaflet.extras2} R packages.

Using {pollen} and {vegperiod} to analyze temperature, GDD, and vegetation period

{pollen} and {vegperiod} are two R packages that can be used to analyze temperature, Growing Degree Days (GDD), and vegetation period. In this analysis, we explore historical temperature records, GDD trends, and vegetation period changes in Chaudières-Appalaches, Quebec, using these packages. By combining data visualization and exploratory data analysis (EDA) techniques, we uncover key patterns and anomalies that shed light on climate-driven changes in the region.

Aminated Visualisation for Centre-du-Québec’s Precipitation

Building upon previous analyses and predictive modeling, I details the process of creating this visualization, including data preparation, disaggregation to daily levels, and kriging for enhanced spatial resolution. The post culminates in an animated map that illustrates precipitation trends and anomalies over time, providing valuable insights for climate analysis, agriculture, and water resource management.

From Trends to Predictions: Machine Learning Forecasts for Centre-du-Québec’s Precipitation

In this phase of the analysis, we aim to model precipitation patterns in Centre-du-Québec using machine learning techniques, leveraging historical climate and environmental data. We will train an XGBoost models and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.

Blog