[ai-geostats] spatial relationships
Mark I could not agree more with Gregoire (with one proviso, see below). Both geostatistics and any weighted average estimators are based on the same assumptions -- that relationship between values at two locations depends on the distance between them and (possibly) their relative orientation. If you cannot get a decent semi-variogram after trying every type of graph [normal, robust, relative] and every transformation and/or interpretation of your data [logarithm, indicator, rank transforms, Normal scores, mixed populations], you do not have a distance-based relationship. This conclusion also rules out: inverse distance weighting of any kind; Delaunay triangles; Thiessen polygons and so on. My proviso: there are other forms of spatial relationship than pure distance/direction types. The simplest example of this is data with a trend, where the value at a specified point will depend on its absolute position. There may be an added component for the 'residuals' which turns out to be distance/direction based. There are also many examples where, for example, flow characteristics, connectivity and so on play a large part in the structure of your variable. In short: no decent semi-variogram does NOT mean no spatial relationship. It means no simple second-order stationary geostatistical type spatial relationship. Isobel http://geoecosse.bizland.com/whatsnew.htm ___ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
[ai-geostats] spatial relationships
Gregoire/Mark Yes, a trend is a spatial structure and can be used for prediction purposes. It just isn't suitable for 'stationary' geostatistical analysis. I have seen cases where the semi-variogram was almost pure nugget effect, but there was a spatial structure. Again, just not a straight-forward 'stationary' geostatistical analysis. Need to look at all the possibilities and at other forms of spatial relationship. Do not despair, there is a pattern! Isobel http://www.geostatistics.info ___ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
[ai-geostats] spatial relationships
Dear oh Dear, I am failing to communicate (again). As far as I know, I didn't say you could not use geostatistics when a trend is present! I regularly use Universal Kriging for data with a trend and kriging with an external drift when the trend is governed by an outside factor (see free tutorial at website). The question originally posed what how does one decide that geostatistics is not appriate. The answer Gregoire and myself gave was "when you cannot get a semi-variogam graph" after trying all possible variations of transforms, interpretation and de-trending. I recently worked with an orange grove in Florida (bugs on oranges) which showed no decent semi-variogram even though rough inverse distance maps looked reasonable. It turned out they had two different kinds of tree in the orchard. Separating the 'rootstocks' yielded a vastly improved semi-variogram and decent geostatistical analysis. My additional point was that failure to obtain a semi-variogram model simply means that there is no 'distance related' structure. It does NOT mean there is NO spatial structure. Isobel http://geoecosse.bizland.com/softwares ___ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
Re: [ai-geostats] spatial relationships
Hi all, The interesting story given by Tim Glover is a good example of "spatial outliers". The dumptruck loads of polluted soils are too different from the neighbourhood, and thus they should be regarded as spatial outliers. When one cannot get a decent variogram after trying all possible data transformations and robust calculations, another way worth trying is to detect such spatial outliers (e.g., using local Moran's I). About two years ago, I dealt with a dataset of soil organic carbon in Ireland, and found that exclusion of spatial outliers significantly improves the structure of a variogram. When doing kriging, the excluded values may be put back to preserve information of raw data. This may be an explanation to the conflict between failed variogram and spatial structure. Spatial outliers may destroy spatial structures and thus result in failed variograms. Cheers, Chaosheng --Dr. Chaosheng ZhangLecturer in GISDepartment of GeographyNational University of Ireland, GalwayIRELANDTel: +353-91-524411 x 2375Fax: +353-91-525700E-mail: [EMAIL PROTECTED]Web 1: www.nuigalway.ie/geography/zhang.htmlWeb 2: www.nuigalway.ie/geography/gis/index.htm - Original Message - From: "Isobel Clark" <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Thursday, September 02, 2004 3:30 PM Subject: [ai-geostats] spatial relationships > Dear oh Dear, I am failing to communicate (again).> > As far as I know, I didn't say you could not use> geostatistics when a trend is present! I regularly use> Universal Kriging for data with a trend and kriging> with an external drift when the trend is governed by> an outside factor (see free tutorial at website).> > The question originally posed what how does one decide> that geostatistics is not appriate. The answer> Gregoire and myself gave was "when you cannot get a> semi-variogam graph" after trying all possible> variations of transforms, interpretation and> de-trending. > > I recently worked with an orange grove in Florida> (bugs on oranges) which showed no decent> semi-variogram even though rough inverse distance maps> looked reasonable. It turned out they had two> different kinds of tree in the orchard. Separating the> 'rootstocks' yielded a vastly improved semi-variogram> and decent geostatistical analysis.> > My additional point was that failure to obtain a> semi-variogram model simply means that there is no> 'distance related' structure. It does NOT mean there> is NO spatial structure. > > Isobel> http://geoecosse.bizland.com/softwares> > > > > > ___ALL-NEW Yahoo! Messenger - all new features - even more fun! http://uk.messenger.yahoo.com> > > * By using the ai-geostats mailing list you agree to follow its rules > ( see http://www.ai-geostats.org/help_ai-geostats.htm )> > * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED]> > Signoff ai-geostats> * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
