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:

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

Generation of Benchmark Data
(Indexing)
Joining
Classification

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

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)

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.

ChocopieKewpie/dggsBenchmarks