Re: AI-GEOSTATS: clustering of locations vs locations data

[Carolyn & Richard Block]

Simon
You might also look at crimestat II and it's online manual.  Police
detection of hot spots is very similar to looking at any kind of clustering.
For clustering algorithms without a known distribution, the program allows
for Monte carol simulations to check for significance. The program and
manual are available free at:
http://www.icpsr.umich.edu/NACJD/crimestat.html

Dick Block


----- Original Message ----- 
From: "Pierre Goovaerts" <[EMAIL PROTECTED]>
To: "Simon Berlage" <[EMAIL PROTECTED]>
Cc: "AI Geostats" <[EMAIL PROTECTED]>
Sent: Thursday, April 15, 2004 10:00 AM
Subject: Re: AI-GEOSTATS: clustering of locations vs locations data


> Dear Simon,
>
> I would recommend that you look at the cluster advisor
> that is available on the following webpage:
> http://www.terraseer.com/products/csr/clusterseer_advisor.html
> This will guide you among the tests available to detect clusters
> in space, time and space-time at a global, local or focused level.
>
> If you look at the detection of clusters in space, you will find
> the following definition:
> Ripley's K-function is used to analyze the spatial pattern of point data.
> It can detect global spatial clustering in individual-level data. In
> essence, you can use it to compare the observed pattern of cases with that
> generated by a homogenous Poisson process.
>
> An alternative is the Besag and Newell's method:
> This method scans the data for collections of cases that appear to be
> unusual clusters. To do so, it centers a circular window on each region in
> turn. This window is then expanded to include neighboring regions until
> the total number of cases in the window reaches a user-specified
> threshold, k. Then, the population size inside the window is compared to
> that expected under an average or expected disease frequency.
>
> Examples and references are provided, which should help you selecting the
> correct method.
>
> Pierre
>
>
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<><>
>
> Dr. Pierre Goovaerts
> President of PGeostat, LLC
> Chief Scientist with Biomedware Inc.
> 710 Ridgemont Lane
> Ann Arbor, Michigan, 48103-1535, U.S.A.
>
> E-mail:  [EMAIL PROTECTED]
> Phone:   (734) 668-9900
> Fax:     (734) 668-7788
> http://alumni.engin.umich.edu/~goovaert/
>
>
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<><>
>
> On Thu, 15 Apr 2004, Simon Berlage wrote:
>
> > Hello Geostatters,
> >
> > I have various monitoring sites in an area. Before determining whether
the
> > DATA is clustered or not, I would like to know if the sample locations
(x,y)
> > are clustered (otherwise whats the point if detecting clusting in your
data?!).
> >
> > I understand that I should be able to tell by just looking at the
spatial
> > distribution
> > of the monitoring points. But in my case its not really clear whether
the
> > locations could be considered as clustered or not. Its for this reason I
> > would like
> > a formal unbiased expression of clustering/non-clustering.
> >
> > I have looked at the Ripley's K function but (correct me if I am wrong)
this
> > concentrates on clustering issues of the data and not the location of
the
> > data points locations in space. I have read about nearest neighbour
analysis
> > whereby you input the site coordinates and set their attribute values to
> > 1. Any output Z-value should indicate the degree of randomness of the
sample
> > locations and hence the degree of clustering. I fail to understand where
> > the criteria for random vs. non random distribution come from? but
that's
> > not the the point here and I shall (try to) refrain from hassling you
about
> > this.
> >
> > What's more important is the recommended statistic to use for this, what
> > statistic has the greatest consensus?
> >
> > Thank you,
> > Simon
> >
> >
> > _____________________________________________________________________
> >
> > Bent u toe aan sneller en goedkoper internet?
> > Ga dan naar http://adsl.tiscali.nl
> >
> >
> >
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