[R-sig-Geo] Converting spatialPointsDataFrame into ppp

Roger Bivand Roger.Bivand at nhh.no
Sun Sep 18 11:06:21 CEST 2011

On Sat, 17 Sep 2011, GodinA wrote:

> Thank you very much for this quick reply Dr. Bivand.
> I am taking good notes of your suggestions.
> I'm a little concern about your comments that my data are not a marked point
> process and would like to verify with you a few points. You are correct that
> my points - the catch positions are essentially fishermen decisions.

Are you studying the spatial decisions of the fishermen, that is, is it 
the clustering of catch data in space that is your main concern? You say 
that you have a sample - in general point patterns should be complete, not 
samples, because the sampling process could obscure the underlying spatial 

> However, I am using at-sea observer data, which are (technically) random
> sampling of a subset of all fishing effort (usually between 5-15
> percent/year, although it varies between years, fisheries, and regions). As
> a starting point, I would like to reproduce what Gardner et al. (2008) have
> done. [Gardner B, Sullivan PJ, Morreale SJ, Epperly SP. Spatial and temporal
> statistical analysis of bycatch data: patterns of sea turtle bycatch in the
> North Atlantic. Canadian Journal of Fisheries and Aquatic Sciences.
> 2008;65(11):2461-2470. Available from:
> http://www.nrcresearchpress.com/doi/abs/10.1139/F08-152].

Only for subscribing institutions, I think. The fact that something has 
been published doesn't necessarily make it sensible (I don't have access 
to this case, speaking generally; from the abstract they seem to be 
looking at bycatch spatial patterns using point pattern analysis, the 
abstract doesn't say if they had access to all bycatch data).

> However, my data are somewhat more complex since I am dealing with different
> fishing gears (over 15 types) over the spend of 15 years (1995-2010). I
> would like to go beyond describing the space-time clustering of catch events
> and try to model these events to oceanographical variables, such as
> temperature and ocean-color derived chlorophyll concentration.

This implies inhomogeneity in the spatial process induced by the 
space-time-varying covariates, so is rather ambitious in a point process 
setting. It feels much more like a GLMM or GAMM, not least because the 
paper you cite deals with bycatch (so clustering is arguably not caused by 
the decisions of the fishermen), and you are dealing with catch, which is 
caused by the decisions, and you only have a sample.

I'm not a fisheries person, it just struck me that it didn't seem obvious 
that a marked point process was a helpful representation of your problem. 
I'm sure others on this list have comments and ideas.

Hope this helps,


> I would be grateful for any of your thoughts, comments, and/or suggestions.
> Thank you very much for your time,
> Best,
> ~~Aurelie
> -----
> Aurelie Cosandey-Godin
> Ph.D. Candidate, Department of Biology
> Industrial Graduate Fellow, WWF-Canada
> Dalhousie University | Biology Dept. |Halifax, NS, Canada
> Email: godina at dal.ca | Web: wormlab.biology.dal.ca
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Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no

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