As a data scientist, you need to distinguish between regression predictive models and classification predictive models. Clear understanding of these models helps to choose the best one for a specific use case. In a nutshell, regression predictive models andclassification predictive models` fall under supervised machine learning. The main difference between them is that the output variable—in regression is numerical (or continuous) while that for classification is categorical (or discrete).
Heatmaps are powerful data visualization tools broadly widely used with meteorologic and oceanographic data. Heatmaps are excellent at tracking signals that move, like ocean current. These diagrams can be used for many more types of atmospheric features. The concept is to represent a matrix of values as colors where usually is organized by a gradient. This post explains how to create a heatmap of ocean current in R using the geom_tile(), geom_contour_filled from ggplot2 (Wickham 2016) and geom_contour_fill from metR package (Campitelli 2019).
What is Raster Data? Raster or “gridded” data are data that are stored in pixels. In the spatial world, each pixel represents an area on the Earth’s surface. In this post will focus raster package and its key function for importing and manipulating raster objects. I expect that toward the end of the post, you will have a glimpse of this package and you will be able to:
Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim.
WorldClim is a set of global climate layers (gridded climate data in GeoTiff format) that can be used for mapping and spatial modeling. WordlClim version 2 contains average monthly climatic gridded data for the period 1970-2000 with different spatial resolutions, from 30 seconds (~1 km2) to 10 minutes (~340 km2).
Often times when w are working with data, there always a geospatial component to the data—the locations. Most of us have used static maps to reveal information that other plots can not. And interactive maps can enliven geographic information to new insights. The most important type of interactivity, is the display of geographic data on interactive or ‘slippy’ web maps. Interactivity can take many forms, the most common and useful of which is the ability to pan around and zoom into any part of a geographic dataset overlaid on a ‘web map’ to show context.
