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.

Introduction Often in environmental studies we are interested in assessing the presence or absence of a long term trend. A widely applied is a parametric test for trend, which involves fitting a linear model that includes some measure of time as one of the predictor variables, and possibly allowing for serially correlated errors in the model. Instead of fitting the data to time series parametric test, Stephen Millard bundles several functions in EnvStats package that are non–parametric and agnostic in dealing with trend (Millard 2013).

Forecast time series introduction Time series analysis comprises methods for predicting the future based on the historical in order to extract meaningful statistics and other characteristics of the data. In other words, time series forecasting is the use of a model to predict future values based on previously observed values. Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post.