Dear all,
according to perfect recommendations I perform moran.test from "spdep"
package. My datasets - csv X,Y tables has 90 000 rows representing 90 000
regular points. In order to obtain morans correlogram I did a batch run and
my plan is to obtain autocorrelation values up to 3000 meters
distance. However according to my hardware limitations I just came to to
170 meters value of the dnearneigh -neighbours of region points by Euclidean
distance function (dnr_170 <- dnearneigh(pointd_r, 0, 170). After this I
receive well know memory allocation error problem. R in my machine runs on
32-bit Windows xp version, with just 2 GB of RAM. Is there some way to
overcome this issue?
Thanks in advance.
Robert Pazur
-------------------------------------------------------
Robert Pazur
PhD student
Institute of Geography
Slovak Academy Of Sciences
Mobile : +421 948 001 705
Skype : ruegdeg
2010/6/24 Robert Pazur <pazurrob...@gmail.com>
> Thank you very much for wonderful reply! Its exactly what i was looking for
> last couple of hours.
> Its incredible smart tool!
> Kind regards,
> Robert.
>
>
>
>
>
>
> 2010/6/24 Roger Bivand <roger.biv...@nhh.no>
>
> On Thu, 24 Jun 2010, Robert Pazur wrote:
>>
>> Dear all,
>>>
>>> I would like to perform Moran'I correlogram (sp.correlogram method in
>>> spdep
>>> package) based on euclidian fixed distances but I have following
>>> problem:
>>> I created an artificial table, containing long and lati of regular points
>>>
>>>> points <-read.table("http://www.scandinavia.sk/data/moran5.csv";,
>>>> sep=",",
>>>>
>>> header=T)
>>> following the manual I also identified neighbours of region
>>>
>>>> dnb <- dnearneigh(as.matrix(points$long, points$lati), 0, 20, longlat=T)
>>>>
>>>
>> No, from your helpful link to the data, you have projected coordinates,
>> not geographical. In addition, your use of as.matrix() instead of cbind()
>> has bad consequences:
>>
>> str(as.matrix(points$long, points$lati))
>> str(cbind(points$long, points$lati))
>>
>> dnearneigh() will be revised to trap this.
>>
>> Had you said:
>>
>> coordinates(points) <- c("long", "lati")
>>
>> then:
>>
>> proj4string(points) <- CRS("+proj=longlat")
>>
>> you would have seen the problem, because the sp classes check for the
>> bounds on objects.
>>
>> So after doing:
>>
>>
>> points <-read.table("http://www.scandinavia.sk/data/moran5.csv";, sep=",",
>> header=T)
>> coordinates(points) <- c("long", "lati")
>> dnb <- dnearneigh(points, 0, 20)
>>
>> you are good to go. Next step - how to replicate the ArcGIS Moran's I - is
>> easy with the correct dnb:
>>
>> moran.test(points$GRID_CODE, listw=nb2listw(dnb, style="B"))
>>
>> You might use correlog() in pgirmess for distance bins, but you'll have
>> more control over the bin boundaries by makin new sets of neighbours for
>> your chosen bin thresholds.
>>
>> Hope this helps (and thank you for reverting to the list after writing to
>> me directly 70 minutes earlier. List is always best).
>>
>> Roger
>>
>>
>> neighbours list
>>>
>>>> ME200.listw <- nb2listw(dnb, style="W", zero.policy=T)
>>>>
>>> but if I perform sp.correlogram function:
>>>
>>>> correl<-sp.correlogram(dnb, points$GRID_CODE, order = 2, method = "I",
>>>>
>>> style = "W", randomisation = TRUE, zero.policy = TRUE, spChk=NULL)
>>> my results are :
>>> Spatial correlogram for points$GRID_CODE
>>> method: Moran's I
>>> estimate expectation variance standard deviate Pr(I) two sided
>>> 1 -0.0029855 -0.0344828 0.0019674 0.7101 0.4776
>>> 2 -0.0044436 -0.0344828 0.0022585 0.6321 0.5273
>>>
>>> and if i perform this part of this task in Arcgis for the same point
>>> shapefile Moran Calculation for Fixed distance band, Euclidian distance a
>>> and 20m threshold, result of Moran coefficient is
>>> (SpatialAutocorrelation moran GRID_CODE false "Fixed Distance Band"
>>> "Euclidean Distance" None 20 # 0 0 0) results are:
>>> Global Moran's I Summary
>>> Moran's Index: 0.746511
>>> Expected Index: -0.003521
>>> Variance: 0.001827
>>> Z Score: 17.545122
>>> p-value: 0.000000
>>>
>>> I would like to perform the same task like in Arcgis but for multiple
>>> distances. However Arcgis cannot deal with large data with multiple
>>> points,
>>> thatswhy I
>>> would like to use R. Its seems to me much better software, but
>>> unfortunatelly I never use it (but I really want)
>>> If you could give me some advice i will be very happy.
>>>
>>> Robert.
>>>
>>>
>>> -------------------------------------------------------
>>> Robert Pazur
>>> PhD student
>>> Institute of Geography
>>> Slovak Academy Of Sciences
>>>
>>> Mobile : +421 948 001 705
>>> Skype : ruegdeg
>>>
>>> [[alternative HTML version deleted]]
>>>
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo@stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>>
>> --
>> Roger Bivand
>> Economic Geography Section, Department of Economics, Norwegian School of
>> Economics and Business Administration, Helleveien 30, N-5045 Bergen,
>> Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
>> e-mail: roger.biv...@nhh.no
>>
>>
>
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