[Marxan] cost layer from multible sources

dan segan dbsegan at gmail.com
Fri Jan 13 04:37:11 EST 2012


Hi All,


I would like second Doug’s suggestion for first identifying solutions with
individual costs layers, and then progressing to using composite cost
layers.


To standardize costs, I’ve always preferred using the relative importance
of a planning unit for each cost (use).   To calculate you simply divide
the value of the planning unit, by the summed vale of all planning units in
the planning region.  This scales all costs between zero and one, and
limits the summed influence of an individual cost to 1 (prior to weighting
for preferences).


For a good reference and additional explanation, see:

*Klein, C. J., Chan, A., Kircher, L., Cundiff, A. J., Gardner, N., Hrovat,
Y., et al. (2008). Striking a balance between biodiversity conservation and
socioeconomic viability in the design of marine protected areas.
Conservation Biology, 22(3), 691-700*


When weighting individual cost layers, I would suggest that more importance
be placed on outcome (impact to a cost layer of the network) rather than
the input weighting.  Equal weighting between two or more cost layers will
rarely produce equal outcomes for the costs.


Happy marxaning,


Dan

On Wed, Jan 11, 2012 at 5:22 PM, Doug Fischer <fischer at geog.ucsb.edu> wrote:

>  I would argue for solving first with each cost layer individually.
>
> (a) Find the lowest cost in terms of fishing rights while ignoring costs
> for oil leases. Then
> (b) find the lowest cost in terms of oils leases while ignoring fishing
> rights costs.
>
> You should be able to price out the oil cost for the minimum fishing cost
> solutions from (a) and price out the fishing costs for the minimum oil cost
> solutions from (b). This starts to tell you what the potential tradeoffs
> are in terms of total reserve network costs between the two different cost
> layers.
>
> You can then scale one or the other cost layers and add them together to
> get a combined cost layer.
>
> If you combine them a few times with different scaling factors you can
> create a few different cost layers with varying bias towards more strongly
> minimizing oil cost or more strongly minimizing fishing rights cost. It is
> often the case that you can dramatically reduce one of your costs (say, oil
> leases) for only a very small increase in the total of your other cost
> (say, fishing rights).
>
> This type of approach is described in the Good Practices Handbook (Chapter
> 8) with respect to choosing BLM values and, more thoroughly, in:
>
> Fischer D.T. & R.L. Church. 2005. The SITES reserve selection system: a
> critical review. Environmental Modeling and Assessment 10 (3): 215–228.***
> *
>
> J. Cohon, R.L. Church and D. Sheer. 1979. Generating multiobjective
> trade-offs: an algorithm for bicriterion problems, Water Resour. Res. 15:
> 1001-1010.
>
> and for more than 2 dimensions in:
>
> R. Solanki, P.A. Appino and J.L. Cohon. 1993. Approximating the
> noninferior set in multiobjective linear programming problems. Eur. J.
> Oper. Res. 68: 356-373.
>
> Best,
> --Doug
>
>
>
> On 1/11/12 1:38 PM, Leif Olson wrote:
>
>  Its also important to give some thought to the spatial autocorrelation
> of the various cost layers, in addition to the absolute values of the
> combined costs. Costs which are the product of non-stationary spatial
> functions may introduce some bias into the distribution of the final cost
> layer…****
>
> ** **
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> *From:* marxan-bounces at lists.sps.uq.edu.au [
> mailto:marxan-bounces at lists.sps.uq.edu.au<marxan-bounces at lists.sps.uq.edu.au>]
> *On Behalf Of *Matt Perry
> *Sent:* 11 January 2012 10:31
> *To:* Husam El Alqamy; marxan at lists.science.uq.edu.au
> *Subject:* Re: [Marxan] cost layer from multible sources****
>
> ** **
>
> As you mentioned, the cost data needs to be on a single scale. Combining
> two cost datasets can be handled by a) assigning actual monetary costs to
> the selection of those areas and summing the dollar amounts or b)
> normalizing the data to an arbitrary index (e.g. 0 to 100) before summing.
> Either way, you must make assumptions about the relative importance of the
> two costs in order to combine them into a single measure.****
>
> ** **
>
> Hope this helps.****
>
> ** **
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> *·´¯`·.¸. , . .·´¯`·.. ><((((º>`·.¸¸.·´¯`·.¸.·´¯`·...¸><((((º>*
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> *Matthew Perry *| Senior Applications Developer* *| *Ecotrust*****
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> *From:* marxan-bounces at lists.sps.uq.edu.au [
> mailto:marxan-bounces at lists.sps.uq.edu.au<marxan-bounces at lists.sps.uq.edu.au>]
> *On Behalf Of *Husam El Alqamy
> *Sent:* Wednesday, January 11, 2012 3:47 AM
> *To:* marxan at lists.science.uq.edu.au
> *Subject:* [Marxan] cost layer from multible sources****
>
> ** **
>
>
> Dear List****
>
> I have a question and hope to find an answer at yours. If I have 2 polygon
> data datasets (fishing rights areas  and oil fields) and I want to produce
> a cost layer using those 2 data elements, is this possible? I figured that
> converting them to raster layers and summing them would be a way around it
> but unfortunately I couldn’t account for the different types of cost on a
> single scale. Could you please kindly advise. ****
>
> Really appreciate it****
>
>
> -- ****
>
> Husam El Alqamy, B.Sc., M.Phil.
> Sr. Biodiversity GIS Analyst ,
> Environmental Information Sector, EIS
> Environmental Agency Abu Dhabi,UAE
> Antelope Specialist Group, ASG - IUCN****
>
> ** **
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>
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