Map-a-doodle-do

“Not only does such a dodgy map give a false sense of confidence, it very explicitly prioritises certain knowledge types. In my field the typical example is expert engineers rather than communities with lived experiences.”

That was Shaz Jameson commenting on my piece a couple of weeks ago that worried about the perils inherent in mapping conservation problems rather than conservation solutions. (And before you say “But conservationists are always mapping their protected areas. Aren’t they solutions?” ask yourself whether they really are solutions, or just wannabe solutions?)

I think Shaz is bang on about the power and knowledge inequities that are inescapable in such a situation.

“A few, biophysical variables are singled out by engineering ‘experts’ and mapped with pretty colours. The map is spat out and then somewhat paraded as legitimate.
Harsh, I know. It’s easy to say that ‘oh it depends on what you want the map for’, but as you say, it very easily slips into false confidence.”

The criticism can seem quite harsh, and I equivocated for a while about blogging my thoughts, but Shaz’s comment has helped reinforce my convictions. I am not saying, and I suspect Shaz is not saying either, that we should just stop mapping the challenges we are faced. Mapping is such a crucial communication tool, that it would be absolutely nuts to do without it. But when it is so critical, and you have the capability to create a map while someone else does not, even if they have more at stake, it does imply all sorts of ethical obligations upon the would-be mappers.

Be careful where you point that map!

Once a map has been created, it can, indeed too often will be used for purposes other than for which it was intended. On many levels that is absolutely fine: it is the great thing about knowledge that what may be a trivial output for one person may be a critical piece of data for another whose purposes were never considered by the original knowledge creator. But, as anyone who has ever been misquoted knows, knowledge users can be casual in their use of their sources.

At least in any scientific endeavour most numeric results are these days reported with a standard error or confidence limits to indicate the degree of precision with which the result should be interpreted. But maps rarely come with such cautionary notes, and even where a diligent cartographer has noted the scale of data applicability, few people will understand the significance of such a statement, and probably even fewer pay it any attention.

Take, for example, that map of carbon and biodiversity values for Tanzania that I complained about previously. At first sight it looks pretty detailed, but closer inspection will reveal the biodiversity axis is in fact reported in huge hexagons that are several hundred km across, but these are somewhat obscured under the much more detailed biomass carbon data (with a scale of 5km per pixel). Worse, if you think for a bit about what the biodiversity index itself represents you will rapidly conclude that in itself it can only be an extremely approximate measure.

Again, as with the concerns over power relations, this does not necessarily mean you should not go about creating the map. But you should certainly try to follow best cartographic practice, and assume that people will not read the accompanying small print when they decide to utilise your work. Thus, as was done with that map of Tanzania, it is not unreasonable to overlay two sets of data of quite different scales, but you definitely should beware of users who might assume that the spatial accuracy on both layers is equally high. One possible solution: consider blurring boundaries so that there are no sharp boundaries for decision makers to latch on to with undeserved confidence.

(Note: it is clear from the text of the paper in which the Tanzania map was presented that the authors were well aware of various problems and limitations of data scale, although they do not explicitly discuss the different scaled data in their sample map, which is nonetheless presented as a sample tool for guiding REDD+ investments.)

I map therefore I conserve

Another criticism I have that merits further elucidation is over the motivation of the mappers themselves. Whether it is the implied “top down planning approach” (my earlier post) or the prioritisation of “certain knowledge types” (Shaz), I get worried about not just what is put in or left out, but why some maps are created at all (the prioritisation of certain activity types).

This criticism applies not so much to academic endeavours such as that which I have critiqued in this and my previous post. Instead it is directed at the conservation BINGOs and associated entities that, of late, have developed substantial units dedicated to mapping conservation problems and their own attempts to resolve them. Precisely because they are such great communication tools (and all successful BINGOs know the importance of good communications), I fear that the maps produced can give quite misleading impressions of competence and imply levels of understanding that do not exist on the ground.

This is because, quite simply, conservation is a human endeavour: responding to problems created by people with solutions that must, necessarily, be implemented by people. Biodiversity and other physical measures are primarily just indicators of how well we are doing. (Although not without other uses in guiding management decision making.)

What worries me is that faced with exceedingly difficult, sometimes almost intractable, and nearly always highly complex conservation problems, mapping becomes a displacement activity. The motivations may be honourable: what better way to start to grapple with such complexities than by mapping them? However, even with the necessary skilled staff, producing such maps is not easy: data availability and quality can pose significant challenges. Significant that is, but not insuperable to a well-resourced institution like a good BINGO. So effort is piled into the mapping initiative, and everybody feels good because they can see progress is being made: by the end of the project they’ll have some top-notch PowerPoint slides. If only the action on the ground were half as good …

Alas dagger-in-the-back politics and brutal economic forces cannot be tamed by a GIS program.

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The false confidence conferred by a dodgy map

Last Year Morten Jerven called into question the quality of statistics produced by (African) developing countries. In his musings on the political fallout from his publication, Professor Jerven summed up the situation as “governance by ignorance.” It also is clear that many stakeholders have a similar view but are reluctant to say so publicly for fear of the ‘anti-neo-colonialist’ backlash that Prof Jerven experienced.

Although I am slightly shocked that a statistic with as high a profile as GDP is so poorly computed, I really shouldn’t be surprised. Some of the government estimates for forest cover that the FAO collates each year in its annual Forest Resources Assessment are known to be extremely shaky; in some cases they are reportedly based on data years out of date, in others on little more than expert guesses. As with GDP, again many stakeholders are aware of this, and, in the case of donors, have money to throw at the problem.

Unfortunately this fits with a wider pattern in conservation: that we are getting better and better at identifying and measuring the biodiversity we are losing, but not much better at halting those losses. This is not to say that new knowledge is a bad thing; it is a vanishingly rare occurrence when an addition to our total body of knowledge does not increase the public good. But, as many researchers are all too aware, new knowledge can easily be misinterpreted or, worse, abused.

A paper last year from Gardner et al. (A framework for integrating biodiversity concerns into national REDD+ programmes) showed how biodiversity could be incorporated into REDD+ planning, and illustrated this with the map reproduced below that overlays biodiversity and carbon values for the case of Tanzania.

As I understand it, the map is a fair reflection of the current state of knowledge, and no-one should infer any particular agenda on the part of the paper authors. As a simplified guide to national decision making it looks eminently useful.

The trouble is, from what I gathered on a recent visit to the country, that this map may not be a good guide as to where are the best opportunities for effective REDD+ projects. The map highlights the Eastern Arc mountains as an area of “high opportunity (strong positive correlation in carbon and biodiversity values)” for REDD+ intervention. Most of these forests are, in theory, already protected in national parks and forest reserves, but which are threatened by encroachment and illegal resource extraction. A long developed strategy of Joint Forest Management (JFM) is intended to help resolve this, by giving local communities a share of forest revenue, except that this has been held up by the failure of the Tanzanian Government to agree a benefit sharing mechanism for JFM. I.e. potential REDD+ project developers would be advised to steer well clear of JFM in the Eastern Arc Mountains until the benefit sharing mechanism has been agreed and tested in practice.

Such policy issues cannot easily be represented on maps, and I would not expect an overlay to do so. However, theoretical exercises like this can be dangerous in how they may give policy makers and donors the illusion of agency: invest money in the sweet spots and best return on investment will be achieved. This omits the critical step that one needs credible potential solutions before investing money in actual projects. However, the reality of national planning in developing countries, often donor supported, and which this paper purports to assist, is that high level decisions may too easily be made without all the necessary information. Maps such as these are therefore potentially dangerous in implying a higher level of decision-directing knowledge than in fact exists.

(A second criticism is that such mapping exercises can sometimes almost imply a terra nullius – no-one’s land – attitude in which the wishes of existing inhabitants and forest users are irrelevant.)

In conclusion I would question the wisdom of a top down planning approach at all. Rather than prioritising problems I suggest we might be better off prioritising solutions. In which case the value of such maps are rather less than might be first supposed.