[R-sig-Geo] How to separate a set of geographical regions to multiple groups that are similar to each other but also maintains geographic contiguity?

[Danlin Yu]

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

-- 
___________________________________________
Danlin Yu, Ph.D.
Professor of GIS and Urban Geography
Department of Earth & Environmental Studies
Montclair State University
Montclair, NJ, 07043
Tel: 973-655-4313
Fax: 973-655-4072
Office: CELS 314
Email:yud using montclair.edu
webpage: csam.montclair.edu/~yu

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