Nancy, Lela, Layal and JD all attended the first annual International Conference on Crisis Mapping (ICCM) a few week ago at John Carrol University in Cleveland, OH. Co-founded and organized by Jen Ziemke and Patrick Meier, this was a top notch conference loaded with some of the best minds in crisis early warning, humanitarian operations and logistics, software development and user interface design, and we at NiJeL were proud to be a part of this high powered dialog. In this post, I’ll be discussing my 5 minute Ignite Talk on a model we’ve developed that might be useful in conflict early waring applications, but look for future posts from Nancy, Lela, Layal. Lela will be discussing her participation on a the Crisis Mapping Visualization and Crisis Mapping Analytics panel, and Layal will be discussing her overall impression of the conference and what she took away from it.

My Ignite Talk focused on a crisis early warning predictive model that we derived after viewing data from the UNDP’s Crisis Recovery and Mapping Analysis (CRMA) team (for more information about the CRMA, take a look at Margunn Alshaikh’s ICCM Ignite Talk about CRMA’s work – fascinating!). The CRMA team led a massive participatory mapping project over the last 2 years across Sudan to better understand spatially the threats and risks to peace, actors, natural resources and other indicators of peace or conflict. Over the summer, Patrick asked us along with Andrew Turner at Geocommons to attempt to derive a model using this data that would predict where conflict might occur – the “fault lines” along which you might increase your intervention if you are confident in the model’s predictive capacity. The model we derived we shared publicly for the first time at ICCM 2009. Our slides follow:

As you can see from the slides, our model is entirely theoretical at this point – we have yet to have the opportunity to test it on either historical data or on currently acquired field data such as in the case of the CRMA project in Sudan.

The model that we derived (below) is a relatively straightforward “gravity based” model where the main operative principal is that the further you are away form a potential conflict flash point, the less of an impact that threat has on you in your current location.

In the model, each term represents a rasterized spatial data layer. Vj in each term is the value of the layer at cell j – for instance if conflict over water resources is an issue, then Vj might be a measure of  water availability at cell j. Dij is the distance from the cell i (the cell being evaluated) to cell j and -alpha, -beta…-gamma, are all distance friction coefficients, meaning that the further cell i and cell j are from each other, the less of an impact that data point will have over the calculation of Ci. These distance friction coefficients are exponential decay factors and will be empirically derived for each layer from any spatial conflict data that we can get our hands on. The coefficients A, B…X are layer weighting coefficients and should be assigned to each layer through expert local opinion. For the CRMA data, we have asked the CRMA team to rank order the data layers they think would be of importance in this model and tell us qualitatively, how “close” one layer is to another in terms of rank. Is there a wide gap between the top ranked layer and the second ranked layer?

Once we have each layer weighted correctly, we can calculate Ci – the strength of the fault line at cell i – for each cell in the grid. The resultant grid we calculate is a so called “violence risk surface.” As I said during my talk, this surface should show areas of relatively high risk for potential conflict, but clearly will not be a substitute for a trained analyst to predict where conflict might occur. I likened this resultant violence risk surface to software a radiologist uses to identify areas in an x-ray or CT scan that might be of interest for further consideration – this model might suggest areas of potential conflict that an analyst might not otherwise have noted.

Several people talked with me about this model after my talk and I’ve already connected with a few folks about potentially using historical and current data to derive the distance friction coefficients and to improve the model in other ways. Jeffrey Villaveces at UN OCHA Columbia has graciously offered to send us data on conflict between the government and the FARC, and others have shown interest as well.  For those of you who attended ICCM 2009 and would like to connect with me again on this topic, feel free to send me a message at jd ‘at’ nijel.org. Thanks!

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