- Good afternoon. I realize this is slightly

off-topic, but I thought it likely that someone on

this list could help and we could take further

discussion off-list.

I have a mapped set of about 800 points in a

Roughly rectangular plot and I would like to ask what

is the distribution of these points (random, regular,

aggregated, if aggregated- at what scale). I would

then like to ask the same question about various

subsets of points with respect to the underlying

distribution of points. (For example, suppose all the

points have an aggregated distribution, is a subset of

points aggregated more/less than expected given the

underlying aggregation). I thought Ripley�s K would be

the way to go, but have been having trouble getting it

to work. I�ve been trying to do it in R. I�ve made

some graphics in R, but I�m really a novice and I find

that the help (including the bit in the Venables and

Ripley S-Plus book) is not quite sufficient to get me

going with the spatial part (I�ve played with

libraries Spatial, Spatstat, Splancs, GeoR).

My questions are:

---Is Ripley�s K the way to go? Any suggestions on

that? Would you recommend something else?

---Software (preferably inexpensive) recommendations?

---Would anyone be willing to share an R program that

I could modify for my data and/or a sample or partial

data set to make sure I have my data structured

correctly for a particular library/procedure? (I found

this was the best way to make progress in SAS, but I

don�t know many R or S-Plus users).

Thanks a lot.

Juliann

aukemaj@...

__________________________________

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* Support to the list is provided at http://www.ai-geostats.org - On Tue, 19 Aug 2003, Juliann Aukema wrote:

> Good afternoon. I realize this is slightly

Using splancs in R, you should be able to use the getpoly() function

> off-topic, but I thought it likely that someone on

> this list could help and we could take further

> discussion off-list.

> I have a mapped set of about 800 points in a

> Roughly rectangular plot and I would like to ask what

> is the distribution of these points (random, regular,

> aggregated, if aggregated- at what scale). I would

> then like to ask the same question about various

> subsets of points with respect to the underlying

> distribution of points. (For example, suppose all the

> points have an aggregated distribution, is a subset of

> points aggregated more/less than expected given the

> underlying aggregation). I thought Ripleys K would be

> the way to go, but have been having trouble getting it

> to work. Ive been trying to do it in R. Ive made

> some graphics in R, but Im really a novice and I find

> that the help (including the bit in the Venables and

> Ripley S-Plus book) is not quite sufficient to get me

> going with the spatial part (Ive played with

> libraries Spatial, Spatstat, Splancs, GeoR).

> My questions are:

> ---Is Ripleys K the way to go? Any suggestions on

> that? Would you recommend something else?

> ---Software (preferably inexpensive) recommendations?

> ---Would anyone be willing to share an R program that

> I could modify for my data and/or a sample or partial

> data set to make sure I have my data structured

> correctly for a particular library/procedure? (I found

> this was the best way to make progress in SAS, but I

> dont know many R or S-Plus users).

interactively to produce subsets through bounding polygons, since the

analysis functions all need a polygon for edge correction. I think that

you will find that looking at the examples in spatial or splancs

carefully, splancs together with the relevant parts of Bailey, T.

C. and Gatrell, A. C. (1995) Interactive spatial data analysis, would show

how the data are structured - that is what the included data sets and

code examples are there for:

> library(splancs)

runs the analysis from Bailey and Gatrell p. 94-5,

> example(khat)

> example(Kenv.csr)

from p. 103-105, and

> example(Kenv.pcp)

testing against a Poisson cluster process from p. 108-9.

This doesn't answer your scale-finding question, but I'm not sure that

this is well-formed as it stands.

Roger

>

--

> Thanks a lot.

> Juliann

> aukemaj@...

>

>

> __________________________________

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>

Roger Bivand

Economic Geography Section, Department of Economics, Norwegian School of

Economics and Business Administration, Breiviksveien 40, N-5045 Bergen,

Norway. voice: +47 55 95 93 55; fax +47 55 95 93 93

e-mail: Roger.Bivand@...

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* Support to the list is provided at http://www.ai-geostats.org - I sent this a week or so ago and am resending because

I haven't seen it show up. Apologies if it did show up

for some of you.

Hi thanks for the responses,

Several people suggested using CrimeStat and one

person sent an R script for Splancs (below). I've

tried both of these with my data, trying to start as

simply as possible, and I do get nice looking output

when I put in the coordinates of all of my points

(haven't made it to subsets yet). However, when I use

CrimeStat with rectangular edge correction, my points

appear to be dispersed -below the envelope for larger

distances-(randomly distributed if I use no

correction) and when I use the Splancs script, they

appear to be aggregated -above the envelope for all

distances. I used 100 simulation replicates for both

methods. Any ideas why I would get such opposite

results? Would it be the edge correction, differences

in how L(d) is calculated, misinterpretation . . .? I

must be missing something.

Thanks a lot,

Juliann

> library(splancs)

ylim=c(min(c(min(sqrt(pointk$khat/pi)-s),min(sqrt(UL.khat$lower/pi)-s)))-0.5

>

> file<-"Filename.csv"

>

> point<-read.csv(file, header=TRUE, sep=";")

>

> box<-array(c(0,1000,1000,0,0,0,1000,1000),

> dim=c(4,2))

> s<-seq(0,500,5)

> rep<-100

>

> pointk<-khat(as.points(point), box,s, newstyle=TRUE)

> UL.khat<-Kenv.csr(length(point$x), box,rep, s)

>

> plot(s, sqrt(pointk$khat/pi)-s, type="l",

>

>

,max(c(max(sqrt(pointk$khat/pi)-s),max(sqrt(UL.khat$upper/pi)-s)))+0.5),

>

>

----------Original Post---------------------------

> main=paste(sub(".csv","",file),"_RK", sep=""),

> xlab="Distance d (m)",

> ylab="L(d)-d")

> lines(s, sqrt(UL.khat$upper/pi)-s, lty=2)

> lines(s, sqrt(UL.khat$lower/pi)-s, lty=2)

> lines(s, s*0, lty=1)

>

> df<-data.frame(d=s, data=sqrt(pointk$khat/pi)-s,

> upper=sqrt(UL.khat$upper/pi)-s,

> lower=sqrt(UL.khat$lower/pi)-s)

>

> write.table(df,

> paste(sub(".csv","",file),"_RK",rep,".csv", sep=""),

>

> sep=";", row.names=TRUE)

>

> write.table(sqrt(pointk$khats/pi),

> paste(sub(".csv","",file),"_RKMap.csv",

> sep=""), sep=";", row.names=TRUE)

>

> dev.copy2eps()

>

> shell(paste("move rplot.eps

> ",sub(".csv","",file),"_RK",rep,".eps", sep=""))

> At 14:05 19.08.2003 -0700, you wrote:

=== message truncated ===

> > Good afternoon. I realize this is slightly

> >off-topic, but I thought it likely that someone on

> >this list could help and we could take further

> >discussion off-list.

> > I have a mapped set of about 800 points in a

> >Roughly rectangular plot and I would like to ask

> what

> >is the distribution of these points (random,

> regular,

> >aggregated, if aggregated- at what scale). I would

> >then like to ask the same question about various

> >subsets of points with respect to the underlying

> >distribution of points. (For example, suppose all

> the

> >points have an aggregated distribution, is a subset

> of

> >points aggregated more/less than expected given the

> >underlying aggregation). I thought Ripley's K would

> be

> >the way to go, but have been having trouble getting

> it

> >to work. I've been trying to do it in R. I've made

> >some graphics in R, but I'm really a novice and I

> find

> >that the help (including the bit in the Venables

> and

> >Ripley S-Plus book) is not quite sufficient to get

> me

> >going with the spatial part (I've played with

> >libraries Spatial, Spatstat, Splancs, GeoR).

> > My questions are:

> >---Is Ripley's K the way to go? Any suggestions on

> >that? Would you recommend something else?

> >---Software (preferably inexpensive)

> recommendations?

> >---Would anyone be willing to share an R program

> that

> >I could modify for my data and/or a sample or

> partial

> >data set to make sure I have my data structured

> >correctly for a particular library/procedure? (I

> found

> >this was the best way to make progress in SAS, but

> I

> >don't know many R or S-Plus users).

> >

> >Thanks a lot.

> >Juliann

> >aukemaj@...

> >

> >

> >__________________________________

> >Do you Yahoo!?

> >Yahoo! SiteBuilder - Free, easy-to-use web site

> design software

> >http://sitebuilder.yahoo.com

> >

> >--

> >* To post a message to the list, send it to

> ai-geostats@...

> >* As a general service to the users, please

> remember to post a summary of

> >any useful responses to your questions.

>

__________________________________

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* Support to the list is provided at http://www.ai-geostats.org - Juliann

I am not certain that Ned Levine, the developer of CrimeStat, has read this.

I will forward it to him. Normal edge corrections don't work very well

in looking at point patterns of crime because incidents of crime tend to

cluster at city boundaries and jurisdictional changes. The program may be

less tested for edge corrections than other things; however, CrimeStat 3 is

now under development. Notification of any corrections and problems would

be welcome.

Dick----- Original Message -----

From: "Juliann Aukema" <aukemaj@...>

To: <ai-geostats@...>; "fs" <jaukema@...>

Sent: Friday, August 29, 2003 3:37 PM

Subject: Follow-up Q Crimestat/Splancs AI-GEOSTATS: point pattern analysis/

R question

> I sent this a week or so ago and am resending because

> I haven't seen it show up. Apologies if it did show up

> for some of you.

>

> Hi thanks for the responses,

>

> Several people suggested using CrimeStat and one

> person sent an R script for Splancs (below). I've

> tried both of these with my data, trying to start as

> simply as possible, and I do get nice looking output

> when I put in the coordinates of all of my points

> (haven't made it to subsets yet). However, when I use

> CrimeStat with rectangular edge correction, my points

> appear to be dispersed -below the envelope for larger

> distances-(randomly distributed if I use no

> correction) and when I use the Splancs script, they

> appear to be aggregated -above the envelope for all

> distances. I used 100 simulation replicates for both

> methods. Any ideas why I would get such opposite

> results? Would it be the edge correction, differences

> in how L(d) is calculated, misinterpretation . . .? I

> must be missing something.

>

> Thanks a lot,

>

>

> Juliann

>

> > library(splancs)

> >

> > file<-"Filename.csv"

> >

> > point<-read.csv(file, header=TRUE, sep=";")

> >

> > box<-array(c(0,1000,1000,0,0,0,1000,1000),

> > dim=c(4,2))

> > s<-seq(0,500,5)

> > rep<-100

> >

> > pointk<-khat(as.points(point), box,s, newstyle=TRUE)

> > UL.khat<-Kenv.csr(length(point$x), box,rep, s)

> >

> > plot(s, sqrt(pointk$khat/pi)-s, type="l",

> >

>

ylim=c(min(c(min(sqrt(pointk$khat/pi)-s),min(sqrt(UL.khat$lower/pi)-s)))-0.5

> >

> >

> ,max(c(max(sqrt(pointk$khat/pi)-s),max(sqrt(UL.khat$upper/pi)-s)))+0.5),

> >

> > main=paste(sub(".csv","",file),"_RK", sep=""),

> > xlab="Distance d (m)",

> > ylab="L(d)-d")

> > lines(s, sqrt(UL.khat$upper/pi)-s, lty=2)

> > lines(s, sqrt(UL.khat$lower/pi)-s, lty=2)

> > lines(s, s*0, lty=1)

> >

> > df<-data.frame(d=s, data=sqrt(pointk$khat/pi)-s,

> > upper=sqrt(UL.khat$upper/pi)-s,

> > lower=sqrt(UL.khat$lower/pi)-s)

> >

> > write.table(df,

> > paste(sub(".csv","",file),"_RK",rep,".csv", sep=""),

> >

> > sep=";", row.names=TRUE)

> >

> > write.table(sqrt(pointk$khats/pi),

> > paste(sub(".csv","",file),"_RKMap.csv",

> > sep=""), sep=";", row.names=TRUE)

> >

> > dev.copy2eps()

> >

> > shell(paste("move rplot.eps

> > ",sub(".csv","",file),"_RK",rep,".eps", sep=""))

>

>

> ----------Original Post---------------------------

> > At 14:05 19.08.2003 -0700, you wrote:

> > > Good afternoon. I realize this is slightly

> > >off-topic, but I thought it likely that someone on

> > >this list could help and we could take further

> > >discussion off-list.

> > > I have a mapped set of about 800 points in a

> > >Roughly rectangular plot and I would like to ask

> > what

> > >is the distribution of these points (random,

> > regular,

> > >aggregated, if aggregated- at what scale). I would

> > >then like to ask the same question about various

> > >subsets of points with respect to the underlying

> > >distribution of points. (For example, suppose all

> > the

> > >points have an aggregated distribution, is a subset

> > of

> > >points aggregated more/less than expected given the

> > >underlying aggregation). I thought Ripley's K would

> > be

> > >the way to go, but have been having trouble getting

> > it

> > >to work. I've been trying to do it in R. I've made

> > >some graphics in R, but I'm really a novice and I

> > find

> > >that the help (including the bit in the Venables

> > and

> > >Ripley S-Plus book) is not quite sufficient to get

> > me

> > >going with the spatial part (I've played with

> > >libraries Spatial, Spatstat, Splancs, GeoR).

> > > My questions are:

> > >---Is Ripley's K the way to go? Any suggestions on

> > >that? Would you recommend something else?

> > >---Software (preferably inexpensive)

> > recommendations?

> > >---Would anyone be willing to share an R program

> > that

> > >I could modify for my data and/or a sample or

> > partial

> > >data set to make sure I have my data structured

> > >correctly for a particular library/procedure? (I

> > found

> > >this was the best way to make progress in SAS, but

> > I

> > >don't know many R or S-Plus users).

> > >

> > >Thanks a lot.

> > >Juliann

> > >aukemaj@...

> > >

> > >

> > >__________________________________

> > >Do you Yahoo!?

> > >Yahoo! SiteBuilder - Free, easy-to-use web site

> > design software

> > >http://sitebuilder.yahoo.com

> > >

> > >--

> > >* To post a message to the list, send it to

> > ai-geostats@...

> > >* As a general service to the users, please

> > remember to post a summary of

> > >any useful responses to your questions.

> >

> === message truncated ===

>

>

>

>

>

>

> __________________________________

> Do you Yahoo!?

> Yahoo! SiteBuilder - Free, easy-to-use web site design software

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>

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any useful responses to your questions.

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