lsp_add_quality: Calculates quality metrics of clustering or segmentation
In Nowosad/lopata: Local Pattern Analysis
View source: R/lsp_add_quality.R
lsp_add_quality R Documentation
Calculates quality metrics of clustering or segmentation
Description
Calculates three metrics to evaluate quality of spatial patterns' clustering or segmentation.
When the type is "cluster", then metrics of inhomogeneity, distinction, and quality are calculated.
When the type is "segmentation", then metrics of inhomogeneity, isolation, and quality are calculated.
For more information, see Details below.
Usage
lsp_add_quality(x, x_dist, type = "cluster", regions = FALSE)
Arguments
x
Object of class sf - usually the output of the lsp_add_clusters() function
x_dist
Object of class dist - usually the output of
the lsp_to_dist() function
type
Either "cluster" or "segmentation"
regions
Not implemented yet
Details
For type "cluster", this function calculates three quality metrics to evaluate spatial patterns' clustering:
(1) inhomogeneity - it measures a degree of mutual dissimilarity
between all objects in a cluster. This value is between 0 and 1,
where small value indicates that all objects in the cluster
represent consistent patterns so the cluster is pattern-homogeneous.
(2) distinction - it is an average distance between the focus cluster
and all of the other clusters.
This value is between 0 and 1, where large value indicates that the cluster
stands out from the other clusters.
(3) quality - overall quality of a cluster. It is calculated as
1 - (inhomogeneity / distinction). This value is also between 0 and 1,
where increased values indicate increased quality.
For type "segmentation", this function calculates three quality metrics to evaluate spatial patterns' segmentation:
(1) inhomogeneity - it measures a degree of mutual dissimilarity
between all objects in a cluster. This value is between 0 and 1,
where small value indicates that all objects in the cluster
represent consistent patterns so the cluster is pattern-homogeneous.
(2) isolation - it is an average distance between the focus cluster
and all of its neighbors. This value is between 0 and 1,
where large value indicates that the cluster
stands out from its surroundings.
(3) quality - overall quality of a cluster. It is calculated as
1 - (inhomogeneity / distinction). This value is also between 0 and 1,
where increased values indicate increased quality.
Value
Object of class sf with three additional columns representing quality metrics.
References
Jakub Nowosad & Tomasz F. Stepinski (2021) Pattern-based identification and mapping of landscape types using multi-thematic data, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2021.1893324
See Also
lsp_add_clusters
Examples
# see examples of `lsp_add_clusters()`
Nowosad/lopata documentation built on Aug. 27, 2024, 6 a.m.
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View source: R/lsp_add_quality.R
| lsp_add_quality | R Documentation |
Calculates quality metrics of clustering or segmentation
Description
Calculates three metrics to evaluate quality of spatial patterns' clustering or segmentation.
When the type is "cluster", then metrics of inhomogeneity, distinction, and quality are calculated.
When the type is "segmentation", then metrics of inhomogeneity, isolation, and quality are calculated.
For more information, see Details below.
Usage
lsp_add_quality(x, x_dist, type = "cluster", regions = FALSE)
Arguments
x |
Object of class |
x_dist |
Object of class |
type |
Either |
regions |
Not implemented yet |
Details
For type "cluster", this function calculates three quality metrics to evaluate spatial patterns' clustering:
(1) inhomogeneity - it measures a degree of mutual dissimilarity
between all objects in a cluster. This value is between 0 and 1,
where small value indicates that all objects in the cluster
represent consistent patterns so the cluster is pattern-homogeneous.
(2) distinction - it is an average distance between the focus cluster
and all of the other clusters.
This value is between 0 and 1, where large value indicates that the cluster
stands out from the other clusters.
(3) quality - overall quality of a cluster. It is calculated as
1 - (inhomogeneity / distinction). This value is also between 0 and 1,
where increased values indicate increased quality.
For type "segmentation", this function calculates three quality metrics to evaluate spatial patterns' segmentation:
(1) inhomogeneity - it measures a degree of mutual dissimilarity
between all objects in a cluster. This value is between 0 and 1,
where small value indicates that all objects in the cluster
represent consistent patterns so the cluster is pattern-homogeneous.
(2) isolation - it is an average distance between the focus cluster
and all of its neighbors. This value is between 0 and 1,
where large value indicates that the cluster
stands out from its surroundings.
(3) quality - overall quality of a cluster. It is calculated as
1 - (inhomogeneity / distinction). This value is also between 0 and 1,
where increased values indicate increased quality.
Value
Object of class sf with three additional columns representing quality metrics.
References
Jakub Nowosad & Tomasz F. Stepinski (2021) Pattern-based identification and mapping of landscape types using multi-thematic data, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2021.1893324
See Also
lsp_add_clusters
Examples
# see examples of `lsp_add_clusters()`
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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