- Design a comprehensive framework for geostatistics (or spatial statistics) in a modern programming language.
- Address the lack of a platform for scientific comparison of different geostatistical algorithms in the literature.
- Exploit modern hardware aggressively, including GPUs and computer clusters.
- Educate people outside of the field about the existence of geostatistics.
For a guided tour, please watch our JuliaCon2021 talk:
Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems. We have written instructions to help you with the process.
GeoStats.jl was developed as part of academic research. It will always be open
source and free of charge. If you would like to help support the project, please
star the repository 
If you find GeoStats.jl useful in your work, please consider citing it:
@ARTICLE{GeoStats.jl-2018,
title={GeoStats.jl – High-performance geostatistics in Julia},
author={Hoffimann, Júlio},
journal={Journal of Open Source Software},
publisher={The Open Journal},
volume={3},
pages={692},
number={24},
ISSN={2475-9066},
DOI={10.21105/joss.00692},
url={http://dx.doi.org/10.21105/joss.00692},
year={2018},
month={Apr}
}Get the latest stable release with Julia's package manager:
] add GeoStats- STABLE — most recently tagged version of the documentation.
- LATEST — in-development version of the documentation.
A set of Pluto notebooks demonstrating the current functionality of the project is available in GeoStatsTutorials with an accompanying series of videos:
Below is a quick preview of the high-level API:
using GeoStats
using Plots
using CSV
# data.csv:
# x, y, station, precip
# 25.0, 25.0, palo alto, 1.0
# 50.0, 75.0, redwood city, 0.0
# 75.0, 50.0, mountain view, 1.0
# read spatial data (e.g. geotable)
𝒯 = georef(CSV.File("data.csv"), (:x,:y))
# define spatial domain (e.g. Cartesian grid)
𝒟 = CartesianGrid(100, 100)
# define estimation problem for precipitation
𝒫 = EstimationProblem(𝒯, 𝒟, :precip)
# choose a solver from the list of solvers
𝒮 = Kriging(
:precip => (variogram=GaussianVariogram(range=35.),)
)
# solve the problem
sol = solve(𝒫, 𝒮)
# plot the solution
contourf(sol)
This project would not be possible without the contributions of:
