[Marxan] cost layer from multible sources

Jeff Ardron Jeff.Ardron at marine-conservation.org
Thu Jan 12 08:17:37 EST 2012


Hi Marxan list,

Adding up cost layers is really the "Achilles heel" of Marxan -at least in my opinion. In that regard Marxan with Zones can alleviate some of the problems, since what were previously spatial costs can become integrated as data layers for zone(s). Even so, there will usually be additional "costs" and the question of how to deal with them remains, regardless of whether you are using Marxan or M with Zones.

The list has already put forward some good ideas. However, I would like to put in a word of caution about adding costs together. If the costs are related to one another (e.g. different types of tourism values) then it could make sense to arithmetically add them up, or to take a mean value, depending on the situation. However, usually they are not related and are more like "apples and oranges"; i.e. being statistically orthogonal. Just like a right-angled triangle, we do not add up the orthogonal sides to calculate the hypotenuse; rather we take the square-root of the sum of squares. This is a better way to deal with quasi-orthogonal costs. It also avoids the problem that five 1's do not equal a single 5 score; i.e. five low-rated values do not equate to one very high value!

Regards,

Jeff

Jeff Ardron
President of the Board
PacMARA (Pacific Marine Analysis and Research Association)
jardron at PacMARA.org<mailto:jardron at PacMARA.org>
+1-202-460-4960
www.PacMARA.org<http://www.PacMARA.org>

From: marxan-bounces at lists.sps.uq.edu.au [mailto:marxan-bounces at lists.sps.uq.edu.au] On Behalf Of Leif Olson
Sent: Wednesday, January 11, 2012 4:38 PM
To: Matt Perry; Husam El Alqamy; marxan at lists.science.uq.edu.au
Subject: Re: [Marxan] cost layer from multible sources

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...

LEIF OLSON
Landscape Ecologist

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From: marxan-bounces at lists.sps.uq.edu.au [mailto: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.

·´¯`·.¸. , . .·´¯`·.. ><((((º>`·.¸¸.·´¯`·.¸.·´¯`·...¸><((((º>
Matthew Perry | Senior Applications Developer | Ecotrust
mperry at ecotrust.org<mailto:jtutak at ecotrust.org>  | 503.467.0817 | ecotrust.org<http://www.ecotrust.org/>


From: marxan-bounces at lists.sps.uq.edu.au [mailto: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


________________________________
Please note that my email address has changed. Our new domain is marine-conservation.org.
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