[R-sig-Geo] density /diversity of points

[marcelino.delacruz at upm.es]

Con fecha 15/5/2011, "Matevž Pavlič" <matevz.pavlic at gi-zrmk.si>
escribió:

>Hi Marcelino, 
>
>Was out of the office for a while...
>Thanks for the help. I think this could work...but can you tell me what this line does?
>
>diversity <- apply(mol.tab,1,function(x) sum(x>0)) 


mol.tab is a table with the number of occurrences of each type (columns)
in the neighborhood of each point (rows). This line computes for each
row (i.e. for each point) the number of types whose value is ">0"
(i.e. types that are present in the neighborhood). This is a very simple
definition of diversity (i.e. "richness"). From that table you could
also compute Shannon or Simpson diversity indices, if you would prefer
that.


Marcelino

>
>i cant figure out how diversity is calculated here?
>
>Thanks again for the help, 
>
>matevz
>
>-----Original Message-----
>From: Marcelino de la Cruz [mailto:marcelino.delacruz at upm.es] 
>Sent: Thursday, May 12, 2011 2:03 PM
>To: Matevž Pavlič
>Cc: r-sig-geo at r-project.org
>Subject: Re: [R-sig-Geo] density /diversity of points
>
>On 12/05/2011 13:12, Matevž Pavlič wrote:
>> Hi all,
>>
>>
>>
>> I have a point data set (SHP) with coordinates and a attribute (i.e. type of point).
>>
>> These points are scattered around a fairly big area. What i would like to do is to find a sub-area where density of points sombined with the diversity of type is the biggest.
>>
>> Does anyone have any idea iff this is somehowe possible to do in R? 
>> Any idea would be greatly aprpeciated,
>>
>>    
>To your first question:
>
>library(fortunes)
>fortune("Yoda")
>
>;-)
>
>More seriously, you could  transform your shp data in a ppp object with spatstat. See the vignette in spatstat. Then you can use some functions there, for example (with the data set lansing):
>
>library(spatstat)
>data(lansing)
>plot(lansing)
># get an estimate of point density
>lansing.den <- density.ppp(lansing)
>plot(lansing.den)
>
># get an estimate of point diversity (here, for the shake of brevity, at the points themselves)
>lansing.tab<- marktable(lansing,R=0.05)
>diversity <- apply(lansing.tab,1,function(x) sum(x>0)) lansing.div <- setmarks(lansing,diversity) lansing.div.s <-smooth.ppp(lansing.div)
>
>plot(lansing.div.s)
>
># select areas with arbitrary high values of  density and diversity plot(eval.im(lansing.div.s >4.5 & (lansing.den/max(lansing.den))>0.9))
>
>
>HTH. Cheers,
>
>Marcelino
>
>
>
>--
>_________________________________
>
>Marcelino de la Cruz Rot
>Departamento de Biologia Vegetal
>E.U.T.I. Agricola
>Universidad Politecnica de Madrid
>  28040 Madrid
>  Tel: 34913365654
>  _________________________________
>