Alex Burnham - Keynote: Lowering the barrier of entry to spatiotemporal data analysis

SpaceTime is a Python/R toolkit aimed at lowering barriers for spatiotemporal data analysis, particularly for researchers in life sciences and social sciences who aren’t GIS experts

Key capabilities include:

• Reading common raster data types (TIFFs, NC4, PNGs, etc.)
• Aligning datasets with different spatial reference systems and grid sizes
• Trimming datasets to common bounding boxes
• Scaling time dimensions (e.g., daily to monthly)
• Mathematical operations between cubes (“cube smasher”)
• Converting to/from tabular formats

The workflow has two main components:

• Pre-cube operations: cleaning and aligning raw files
• Post-cube operations: manipulating clean spatiotemporal cubes

Built on Google Earth Engine with focus on user-friendly defaults while maintaining power/flexibility

Reduces code complexity compared to alternatives (fewer lines of code needed) while maintaining computational efficiency

Current limitations/future work:

• Working on distributed computing capabilities
• Building open source community
• Improving documentation
• Adding network/connectivity analysis features
• Expanding beyond raster data

Ideal for ecological/environmental applications like:

• Climate change analysis
• Species distribution modeling
• Agricultural modeling
• Weather data analysis

Available on PyPI (Python) and GitHub (R version), with ongoing development prioritizing the Python version

Focus on academic/research use cases but potential for commercial applications

Emphasizes compatibility with other tools/formats while simplifying initial data preparation steps

Development supported by NSF EPSCoR grant through Barracuda project collaboration between UVM and UMaine