Dear Danlin,
I think you need SKATER (Spatial 'K'luster Analysis by Tree Edge
Removal) algorithm that is implemented in package 'spdep' (function
skater()) and does spatially constrained clustering. The results of
SKATER are contiguous regions formed by more or less similar neighboring
polygons. It was published by Assuncao et al. (2006). Here you can find
a tutorial:
https://geodacenter.github.io/tutorials/spatial_cluster/skater.html
HTH,
Ákos
_____
Ákos Bede-Fazekas
Centre for Ecological Research, Hungary
2022.07.08. 6:05 keltezéssel, Danlin Yu írta:
Dear List members:
I have recently attempted to do a regionalization analysis with a group
of geographic regions, each contains multiple attributes (A1, A2, A3,
...). The goal is not like a regular regionalization problem (such as
K-means) in which you define groups with minimal within group
dissimilarity but maximal between group dissimilarity.
My regionalization is the opposite, I want the groups to be as similar
as possible (although within group does not have to be as dissimilar as
possible, but that is of less concern) in terms of means, variance, and
other statistics. I ran into the minDiff package and its successor
anticlust package in R, and it is able to do the job wonderfully except
for one problem: since this is a regionalization problem, I would really
want the final groups to be geographically connected (spatially
constrained). Results from minDiff/anticlust, however, show the
different groups are mixed with one another all over the map. Here is a
sample code:
A dataframe contains the geographic units and attributes is read from a
shapefile and stored in geo.df.
|geo.df<-as.data.frame(read_sf(dsn = getwd(), lay = "geolayer",
stringsAsFactors = FALSE)) geo.df$class <- anticlustering(geo.df[,
c("A1", "A2", "A3", "A4", ..., "An"), K = 5, objective = "variance",
standardize = TRUE) |
I've tried to include coordinates in the list of attributes (A1, A2,
..., An), pairwise distances, but none worked. I always ended up with
well separated groups, but all mixed with one another in the geographic
space.
Any pointers on how to proceed from here? Any hints will be greatly
appreciated.
Thank you all in advance.
Best,
Danlin Yu
_______________________________________________
R-sig-Geo mailing list
R-sig-Geo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
