Dear all,

my name is Michael Haenlein and I am Associate Professor of Marketing at
ESCP Europe in Paris, France.

For a research project I'm currently working on, I would like to simulate a
given degree of spatial auto correlation within an artificial network. The
problem is as follows: I have a network with a given number of nodes
(locations) and a given number of relationships between those nodes. Each
node i has a value xi of a certain type of metric. Assume for example that
the nodes are different cities and the xi is the average income of people
living in the respective city. I have drawn the xi independently from a
probability distribution so that there is no spatial auto correlation in the
data set. I now would like to "adjust" the individual xi in order to reach a
predefined level of spatial auto correlation such as a pre-defined level of
Moran's I. So starting from a situation without spatial auto correlation,
I'd like to use some iterative process to reach a Moran's I value of let's
say 0.60.

Can anyone give me advice on how this can be done?

Any help is very much appreciated!

Thanks in advance,

Michael



Michael Haenlein
Associate Professor of Marketing
ESCP Europe
Paris, France

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