checkerboard: Checkerboard
In profrichharris/MLID: Multilevel Index of Dissimilarity
Description
Usage
Details
See Also
Description
A demonstration of how the Multilevel Index of Dissimilarity measures spatial
clustering as well as unevenness
Usage
1
Details
A criticism of the standard Index of Dissimilarity (ID) is that it only
measures one of the two principal dimensions of segregation - unevenness but
not spatial clustering. Because of this, very different spatial patterns of
segregation can generate the same ID score but the ID is unable to
distinguish between them.
In contrast, the multilevel index can detect the differences because
different patterns (scales) of segregation change the percentage of the
variance due to each level. The demonstation illustrates this using the
classic example of a checkerboard. The examples show how the percentage
of the total variance (labelled Pvariance) moves up the hierarchy with
the increase in spatial clustering at greater geographical cases. However,
the ID is always the same.
The 'stray' cell in examples 2-4 is to allow the model to be fitted. With it
the model correctly identifies that some of the variation remains at the
base level)
See Also
profrichharris/MLID documentation built on May 26, 2019, 8:34 a.m.
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Description Usage Details See Also
Description
A demonstration of how the Multilevel Index of Dissimilarity measures spatial clustering as well as unevenness
Usage
1 |
Details
A criticism of the standard Index of Dissimilarity (ID) is that it only measures one of the two principal dimensions of segregation - unevenness but not spatial clustering. Because of this, very different spatial patterns of segregation can generate the same ID score but the ID is unable to distinguish between them.
In contrast, the multilevel index can detect the differences because different patterns (scales) of segregation change the percentage of the variance due to each level. The demonstation illustrates this using the classic example of a checkerboard. The examples show how the percentage of the total variance (labelled Pvariance) moves up the hierarchy with the increase in spatial clustering at greater geographical cases. However, the ID is always the same.
The 'stray' cell in examples 2-4 is to allow the model to be fitted. With it the model correctly identifies that some of the variation remains at the base level)
See Also
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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