Geostatistical clustering methods for the GeoStats.jl framework.
Simple Linear Iterative Clustering (SLIC) produces clusters
that are spatially connected based on a geospatial distance
The samples in these clusters are similar in terms of their features
according to a distance
dᵥ. The tradeoff is controlled with a
m in an additive model
dₜ = √(dᵥ² + m²(dₛ/s)²).
The original method developed for images is described in
Achanta et al. 2011.
It has been generalized in this package for any geospatial data set
(e.g. point sets).
Geostatistical Hierarchical Clustering (GHC) produces clusters based on (cross-)variograms between covariates and on a kernel function between geospatial coordinates. The method is described in Fouedjio, F. 2016.
Geostatistical Spectral Clustering (GSC) produces clusters based on the spectral decomposition of the graph Laplacian constructed with weights that are a function of features and locations. The method is described in Romary et al. 2015.
Get the latest stable release with Julia's package manager:
] add GeoClustering
This package is part of the GeoStats.jl framework.
For a simple example of usage, please check the main documentation.
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