CubeDynamics
Overview
While FIRED defines individual fire events, environmental datasets often exist in many different formats and resolutions. Managing and analyzing these datasets consistently can be difficult.
CubeDynamics addresses this challenge by treating environmental data as spatiotemporal data cubes.
The central idea is that Earth system data should be organized around three core dimensions:
- space (
x,y) - time (
t) - variables
This structure forms a multidimensional array known as a data cube.
What is a data cube?
A data cube is a structured dataset where each value corresponds to a location, time, and variable.
For example, a cube may include:
- latitude
- longitude
- date
- burned area
- vegetation index
- temperature
- wind speed
Each slice of the cube represents a spatial map at a given time.
Each vertical column represents a time series at a specific location.
This representation allows environmental processes to be analyzed systematically.
The grammar of cubes
CubeDynamics proposes a structured set of operations for working with data cubes.
This idea is inspired by concepts such as the grammar of graphics in data visualization.
Typical cube operations include:
- subsetting a cube
- slicing a cube along spatial or temporal dimensions
- aggregating variables
- combining multiple cubes
- computing derived metrics
By formalizing these operations, CubeDynamics creates a consistent framework for analyzing Earth system data.
Computational benefits
The cube-based approach provides several advantages:
- Scalability: large satellite datasets can be processed efficiently because operations apply across entire arrays rather than individual files.
- Reproducibility: workflows become standardized and easier to reproduce.
- Interoperability: multiple environmental datasets can be combined consistently.
For example, researchers can analyze relationships between fire activity and environmental drivers such as vegetation, climate, or topography in one structured framework.