[R-sig-Geo] Question for point pattern experts

[Dan Putler]

OK, I'm really slow to responding to this thread, but I've been thinking 
about it. Are there covariates available (besides time) that can be 
used? If yes, doesn't it make sense to look at this using a spatial 
autoregressive regression model? Since the lakes typically don't touch, 
then the spatial weight matrix would need to be based on distance. I'm 
not sure what the correct projection is if it goes really far north, and 
as far south as the southern Niagara Peninsula. A distance preserving 
projection would make the most sense, but UTM (the likely culprit) I 
know will breakdown if some of the lakes are too close to the north pole.

Dan

On 02/10/2011 08:26 PM, Rolf Turner wrote:
> On 11/02/2011, at 10:14 AM, david depew wrote:
>
>> Thanks Rolf,
>> I see your point, and I admit that I've waffled a bit on this
>> issue.  I'm hesitant to drive down the kriging path (although
>> indicators might be suitable), because there are many known (and
>> unknown) covariates that affect the contaminant burden, and few of
>> these are quantified at the scale or resolution we would require to
>> make such an approach useful (at least based on my experience).
>> Regarding the data; the sites were not "chosen" as one would choose
>> locations to sample, rather they are a mix of quasi - synoptic and
>> randomized sampling designs from ~ 50 or so different investigators/
>> agencies. We've simply mashed them into one large database for our
>> purposes. The habitat of the organisms is fixed in space (lakes),
>> but given that there is no real selection protocol for sites, I
>> wondered if this fit into the "grey" area.
> 	Well, it's pretty well all grey to me! :-)
>
> 	I'm afraid I have no insights to offer.  But let me say that whether
> 	or not the sites were ``chosen'' in any organised fashion, their
> locations
> 	are in effect given to you.  They did not arise from a random process.
> 	Or rather, if they did, you are not interested in studying the process
> 	which gave rise to the locations but rather properties of observations
> 	made at those locations.
>
> 	A mathematical description of the structure is, I think, that you have
> 	a random field, defined on a discrete set of points, an irregular
> lattice
> 	 --- the observation points in your data base.  There is also a time
> variable
> 	involved, and associated covariates are observed.  It might be
> appropriate
> 	to include time as a covariate.  Or not.  You might consider the
> lattice
> 	of points to be three dimensional, with a time dimension.
>
> 	Saying all that doesn't really help to develop an appropriate
> 	analysis strategy.  However it make help to focus the mind and to
> 	formulate clearly and precisely just what problems you are trying to
> 	solve.
>
> 		cheers,
>
> 			Rolf Turner
>>
>> On Thu, Feb 10, 2011 at 3:42 PM, Rolf Turner
>> <r.turner at auckland.ac.nz>  wrote:
>>
>> On 11/02/2011, at 6:00 AM, david depew wrote:
>>
>> Dear list,
>> A brief and (hopefully) simplistic question regarding point pattern
>> analysis.
>>
>> We have compiled a large, continental database of chemical burdens
>> in a
>> model organism. Currently, the data span 40 years and covers the
>> entire
>> country of Canada (including the high arctic). We have categorized the
>> numeric data into categorical data (i.e. categorical marks) based
>> on risk
>> thresholds. We'd like to assess whether or not there are
>> interesting spatial
>> patterns ( i.e. clusters of levels of risk (high vs low)), much like a
>> case/control approach. My question is as follows;
>>
>> Is there a "best" geographic projection for this approach?
>> Currently all
>> data are in Latitude/Longitude. My inclination is to use something
>> along the
>> lines of an equal area projection to maintain a reasonable
>> representation of
>> spatial dispersion.
>>
>> This doesn't sound to me like ***point pattern*** data.  It sounds
>> like
>> you have taken measurements of ``chemical burdens'' in a particular
>> organism, at a number of ***chosen*** sites over a number (40) of
>> years.
>>
>> Thus the observation points are deterministic, not random, and so
>> point
>> pattern analysis doesn't come into it.
>>
>> It may be the case that some sort of combination of kriging and
>> time series
>> or repeated measures analysis might be called for.  But here I
>> speak of
>> that of which I know nothing.
>>
>>         cheers,
>>
>>                 Rolf Turner
>>
>>
>>
>>
>> -- 
>> David Depew
>> Postdoctoral Fellow
>> School of Environmental Studies
>> Queen's University
>> Kingston, Ontario
>> K7L 3N6
>>
>> david.depew at queensu.ca
>> P: (613) 533-6000 x77831
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