Tag Archives: Complexity

Beyond the Dot: Building Visual DNA for Crisis Mapping

Crisis mapping is often referred to as dots on a map. Perhaps the time has come to move beyond the dot. After all, what’s in a dot? A heck of a lot, as it turns out. When we add data to a map using a dot, we are collapsing important attributes and multiple dimensions into just one single dimension. This reduces entropy but information as well. Of course, simplification is important but this should be optional and not hard-wired in the form of static dot on a map. This is why I’m a big fan of GeoTime, i.e., 3D immersive mapping, which unpacks the temporal dimension by adding a Z-axis to dynamic crisis maps, i.e.,  time “flows upwards.”

This is a definite improvement in that the GeoTime map gives a more immediate at-a-glance understanding by uncollapsing dots into more dimensions and attributes. The icons still “hide” additional information, however. So how do we unpack as many attributes and dimensions as possible? How do we visualize the underlying DNA of a dot on a crisis map? I recently spoke to a colleague who may have an answer, which looks something like this:

And this:

No longer dots on map. Here, the geometric shapes, sizes, colors, relative distances, etc., all convey information unpacked from a single dot. Tags on steroids basically, especially since they don’t sit still, i.e., they all move or can be made to vibrate at various speeds referencing further information that is other-wise hidden in a collapsed dot. In other words, the toroids can represent live data from the field. Additional toroids and geometric shapes can be added to a “dot” to represent more attributes and temporal elements.

Unpacking dots in this way leads to more perceptivity and discoverability. Patterns that are not otherwise discernible as static dots emerge as curious geometric shapes that beg to be explained. When “flying through” the map below, for example, it was very clear that conflict events had very distinct geometric shapes and constructs that were simply not discernible when in the form of dots. New questions that we didn’t know to ask can now be asked and followed up on with hypothesis testing. This type of visual DNA also allows one to go beyond natural languages and use a common geometric language. Users can also compare their perceptions using objects rather than natural languages.

Reading these maps does require learning a new kind of language, but one that is perhaps easier and more intuitive to learn, not to mention customizable. The above is just a glimpse of the evolving work and the team behind it is not making any claims about anything just yet. The visualization code will be released as open source software in the near future. In the meantime, a big thanks to my colleague Jen Ziemke for putting me in touch with the team behind this remarkable tool.

The Mathematics of War: On Earthquakes and Conflicts

A conversation with my colleague Sinan Aral at PopTech 2011 reminded me of some earlier research I had carried out on the mathematics of war. So this is a good time to share some of the findings from this research. The story begins some 60 years ago, when British physicist Lewis Fry Richardson found that international wars follow what is called a power law distribution. A power law distribution relates the frequency and “magnitude” of events. For example, the Richter scale, relates the size of earthquakes to their frequency. Richardson found that the frequency of international wars and the number of causalities each produced followed a power law.

More recently, my colleague Erik-Lars Cederman sought to explain Richardson’s findings in his 2003 peer-reviewed publication “Modeling the Size of Wars: From Billiard Balls to Sandpiles.” However, Lars used an invalid statistical technique to test for power law distributions. In 2005, I began collaborating with Pro-fessors Neil Johnson and Michael Spagat on related research after I came across their fascinating co-authored study that tested casualty distributions in new wars (internal conflicts) for power laws. Though he was not a co-author on the 2005 study, my colleague Sean Gourely presented this research at TED in 2009.

In any case, I invited Michael to present his research at The Fletcher School in the Fall of 2005 to generate interest here. Shortly after, I suggested to Michael that we test whether conflict events, in addition to casualties, followed a power law distribution. I had access to an otherwise proprietary dataset on conflict events that spanned a longer time period than the casualty datasets that he and Neils were working off. I also suggested we try to test whether casualties from natural disasters follow a power law distribution.

We chose to pursue the latter first and I submitted an abstract to the 2006 American Political Science Association (APSA) conference to present our findings. Soon after, I was accepted to the Santa Fe Institute’s Complex Systems Summer Institute for PhD students and took the opportunity to pursue my original research in testing conflict events for power law distributions with my colleague Dr. Ryan Woodard.

The APSA paper, presented in August 2006, was entitled “Natural Disasters, Casualties and Power Laws:  A Comparative Analysis with Armed Conflict” (PDF). Here is the paper’s abstract and findings:

Power-law relationships, relating events with magnitudes to their frequency, are common in natural disasters and violent conflict. Compared to many statistical distributions, power laws drop off more gradually, i.e. they have “fat tails”. Existing studies on natural disaster power laws are mostly confined to physical measurements, e.g., the Richter scale, and seldom cover casualty distributions. Drawing on the Center for Research on the Epidemiology of Disasters (CRED) International Disaster Database, 1980 to 2005, we find strong evidence for power laws in casualty distributions for all disasters combined, both globally and by continent except for North America and non-EU Europe. This finding is timely and gives useful guidance for disaster preparedness and response since natural catastrophes are increasing in frequency and affecting larger numbers of people.  We also find that the slopes of the disaster casualty power laws are much smaller than those for modern wars and terrorism, raising an open question of how to explain the differences. We show that many standard risk quantification methods fail in the case of natural disasters.


Dr. Woodard and I presented our research on power laws and conflict events at SFI in June 2006. We produced a paper in August of that year entitled “Concerning Critical Correlations in Conflict, Cooperation and Casualties” (PDF). As the title implies, we also tested whether cooperative events followed a power law. As far as I know, we were the first to test conflict events not to mention cooperative events for power laws. In addition, we looked at conflict/cooperation (C/C) events in Western countries.

The abstract and some findings are included below:

Knowing that the number of casualties of war are distributed as a power law and given a rich data set of conflict and cooperation (C/C) events, we ask: Are there correlations among C/C events? Is there a correlation between C/C events and war casualties? Can C/C data be used as proxy for (potentially) less reliable casualty data? Can C/C data be used in conflict early warning systems? To begin to answer these questions we analyze the distribution of C/C event data for the period 1990–2004 in Afghanistan, Colombia, Iran, Iraq, North Korea, Switzerland, UK and USA. We find that the distributions of individual C/C event types scale as power laws, but only over approximately a single decade, leaving open the possibility of a more appropriate fit (for which we have not yet tested). However, the average exponent of the power law (2.5) is the same as that found in recent studies of casualties of war. We find low levels of correlations between C/C events in Iraq and Afghanistan but not in the other countries studied. We find that the distribution of the sum of all conflict or cooperation events scales exponentially. Finally, we find low levels of correlations between a two year time series of casualties in Afghanistan and the corresponding conflict events.


I’m looking to discuss all this further with Sinan and learning more about his fascinating area of research.

Failing Gracefully in Complex Systems: A Note on Resilience

Macbeth’s castle, Act 1, Scene VII. Macbeth and Lady Macbeth are plotting Duncan’s death.

Macbeth: If we should fail?

Lady Macbeth: Then we fail! But screw your courage to the sticking place, And we’ll not fail.

Complex dynamic systems tend to veer towards critical change. This is explained by the process of Self-Organized Criticality (SEO). Over time, non-equilibrium systems with extended degrees of freedom and a high level of nonlinearity become increasingly vulnerable to collapse. As the Santa Fe Institute (SFI) notes,

“The archetype of a self-organized critical system is a sand pile. Sand is slowly dropped onto a surface, forming a pile. As the pile grows, avalanches occur which carry sand from the top to the bottom of the pile.”

Scholars like Thomas Homer-Dixon argue that we are becoming increasingly prone to domino effects or cascading changes across systems, thus increasing the likelihood of total synchronous failure. “A long view of human history reveals not regular change but spasmodic, catastrophic disruptions followed by long periods of reinvention and development.”

That doesn’t mean we’re necessarily done for, however. As Homer-Dixon notes, we can “build resilience into all systems critical to our well-being. A resilience system can absorb large disturbances without changing its fundamental nature.”

“Resilience is an emergent property of a system–it’s not a result of any one of the system’s parts but of the synergy between all of its parts.  So as a rough and ready rule, boosting the ability of each part to take care of itself in a crisis boosts overall resilience.”

This is where Homer-Dixon’s notion of “failing gracefully” comes in: “somehow we have to find the middle ground between dangerous rigidity and catastrophic collapse.”

“In our organizations, social and political systems, and individual lives, we need to create the possibility for what computer programmers and disaster planners call ‘graceful’ failure. When a system fails gracefully, damage is limited, and options for recovery are preserved. Also, the part of the system that has been damaged recovers by drawing resources and information from undamaged parts.”

“Breakdown is probably something that human social systems must go through to adapt successfully to changing conditions over the long term. But if we want to have any control over our direction in breakdown’s aftermath, we must keep breakdown constrained. Reducing as much as we can the force of underlying tectonic stresses helps, as does making our societies more resilient. We have to do other things too, and advance planning for breakdown is undoubtedly the most important.”

Planning for breakdown is not defeatist or passive. Quite on the contrary, it is wise and pro-active. Our hubris all too often clouds our better judgment and rarely do we—as the humanitarian/development community—seriously ask ourselves what we would do “if we should fail.” The answer: “then we fail” is an option. But are we, like Macbeth, prepared to live with the consequences?

Patrick Philippe Meier