README.md

DGGS benchmarks

Code written to generate DGGS benchmark cases, and measure their performance against non-DGGS GIS workflows.

Written to support: Law & Ardo (2024) "Using a discrete global grid system for a scalable, interoperable, and reproductible system of land-use classification" (In preparation.)

Computer specifications

Processor: 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz 2.50 GHz Installed RAM: 32.0 GB (31.7 GB usable) System type: 64-bit operating system, x64-based processor

Executing benchmarks

Two Jupyter notebooks are available to generate and run benchmarking:

1. Vector benchmarks
2. Raster benchmarks

Benchmarking Notebooks are self documented, and they follow the same workflow as outlined in the paper:

1. Generation of Benchmark Data
2. (Indexing)
3. Joining
4. Classification

Local functions are defined within Jupyter Notebooks; benchmarks can also be found here

• Vector functions

• DGGS functions (Vector)

• Raster functions

• DGGS functions (Raster)

Recorded benchmarking results

Vector

For vector experiments, each run and results of benchmarking are found in independent Jupyter notebooks, organised by number of inputs:

Vector (DGGS)

• 2
• 5
• 10
• 50
• 100
• 500
• 1000

Vector (baseline)

• 2
• 5
• 10
• 50
• 100
• 250

Data for Vector & DGGS:

Vector Data

DGGS Data

Raster

For raster experiments, data is contained within singular notebooks. The results for different numbers of inputs are in different cells.

Raster (DGGS)

• Indexing
• Joining and classifying

Raster (baseline)

• Joining
• Classifying