Monthly Archives: May 2009

Disaster Theory for Techies

I’ve had a number of conversations over the past few weeks on the delineation between pre- and post-disaster phases. We need to move away from this linear concept of disasters, and conflicts as well for that matter. So here’s a quick introduction to “disaster theory” that goes beyond what you’ll find in the mainstream, more orthodox literature.

What is a Disaster?

There is a subtle but fundamental difference between disasters (processes) and hazards (events); a distinction that Jean-Jacques Rousseau first articulated in 1755 when Portugal was shaken by an earthquake. In a letter to Voltaire one year later, Rousseau notes that, “nature had not built  [process] the houses which collapsed and suggested that Lisbon’s high population density [process] contributed to the toll” (1).

(Incidentally, the earthquake in Portugal triggered extensive earthquake research in Europe and also served as the focus for various publications, ranging from Kant’s essays about the causes of earthquakes to Voltaire’s Poème sur le désastre de Lisbonne).

In other words, natural events are hazards and exogenous while disasters are the result of endogenous social processes. As Rousseau added in his note to Voltaire, “an earthquake occurring in wilderness would not be important to society” (2). That is, a hazard need not turn to disaster since the latter is strictly a product of social processes.

And so, while disasters were traditionally perceived as “sudden and short lived events, there is now a tendency to look upon disasters in African countries in particular, as continuous processes of gradual deterioration and growing vulnerability,” which has important “implications on the way the response to disasters ought to be made” (3).

But before we turn to the issue of response, what does the important distinction between events and processes mean for early warning?

Blast From the Past

In The Poverty of Historicism (1944), the German Philosopher Karl Popper makes a distinction between two kinds of predictions: “We may predict (a) the coming of a typhoon [event], a prediction which may be of the greatest practical value because it may enable people to take shelter in time; but we may also predict (b) that if a certain shelter is to stand up to a typhoon, it must be constructed [process] in a certain way […].”

A typhoon, like an  earthquake, is certainly a hazard, but it need not lead to disaster if shelters are appropriately built since this process culminates in minimizing social vulnerability.

In contemporary disaster research, “it is generally accepted among environmental geographers that there is no such thing as a natural disaster. In every phase and aspect of a disaster—causes, vulnerability, preparedness, results and response, and reconstruction—the contours of disaster and the difference between who lives and  dies is to a greater or lesser extent a social calculus” (4).

In other words, the term “natural disaster” is an oxymoron and “phrases such as a ‘disaster hit the city,’ ‘tornadoes kill and destroy,’ or a ‘catastrophe is known by its works’ are, in the last resort, animistic thinking” (5).

The vulnerability or resilience of a given system is not simply dependent on the outcome of future events since vulnerability is the complex product of past political, economic and social processes. When hazards such as landslides interface with social systems the risk of disasters may increase. “The role of vulnerability as a causal factor in disaster losses tends to be less well understood, however. The idea that disasters can be managed by identifying and managing specific risk factors is only recently becoming widely recognized” (6).

A Complex System

Consider an hourglass or sand clock as an illustration of vulnerability-as-causality. Grains of sand sifting through the narrowest point of the hourglass represent individual events or natural hazards. Over time a sand pile starts to form, which represents the evolution of society or the connectedness of a social network. Occasionally, a grain of sand falls on the pile and an avalanche or disaster follows.

Why does the avalanche occur? One might ascribe the cause of the avalanche to one grain of sand, i.e., a single event. On the other hand, a systems approach to vulnerability analysis would associate the avalanche with the pile’s increasing slope and to the connectedness (or population density) of the grains constituting the pile since these factors render the structure increasingly vulnerable to falling grains.

Left on its own, the sand pile’s stability, or the social network, becomes increasingly critical or vulnerable. From this perspective, “all disasters are slow onset when realistically and locally related to conditions of susceptibility”. A hazard event might be rapid-onset, but the disaster, requiring much more than a hazard, is a long-term process, not a one-off event.

We must therefore “reduce as much as we can the force of the underlying tectonic stresses in order to lower the risk of synchronous failure—that is, of catastrophic collapse that cascades across boundaries between technological, social and ecological systems” (7).

Recall Rousseau’s comment on population density as a contributing cause of the earthquake disaster and Popper’s remark that adequate shelter or resilience could offset the impact of typhoons. The sand pile at the bottom of the hourglass is constrained by the glass’s circumference. While abstract, this image mimics the growth of densely populated cities that become increasingly vulnerable to hazards, either natural or technological.

Unlike the clock’s lifeless grains of sand, however, human beings can minimize their vulnerability to exogenous shocks through disaster preparedness, mitigation and adaptation. In doing so, individuals can “flatten” the structure of the sand pile into a less hierarchical system and thereby shift or diffuse the risk of an avalanche. In conflict prevention terms, this means structural prevention, which typically focuses on local livelihoods and local capacity building.


Clearly, early warning should seek to monitor both the falling grains and the vulnerability of the sand pile to determine the risk and magnitude of an avalanche. In more formalistic language, a dual approach is important because it is not always clear a priori whether a disaster is due to a strong exogenous shock, to the internal dynamics of the system or a combination of both (8).

As the disaster management community has learned, in “support[ing] good decision-making, the issue is not one of being able to predict the unpredictable. Rather, the fundamental question is that, given that we cannot have reliable predictions of future outcomes, how can we prevent excessive hazard levels today and in the future in a cost-effective manner?”

More on resilience:

  • Disaster Response, Self-Organization and Resilience [Link]
  • On Technology and Building Resilient Societies to Mitigate the Impact of Disasters [Link]
  • Social Media = Social Capital = Disaster Resilience? [Link]
  • Failing Gracefully in Complex Systems: A Note on Resilience [Link]
  • Towards a for Economic Resilience [Link]

Moving Forward with Swift River

This is an update on the latest Swift River open group meeting that took place this morning at the InSTEDD office in Palo Alto. Ushahidi colleague Kaushal Jhalla first proposed the idea behind Swift River after the terrorist attacks on Mumbai last November. Ushahidi has since taken on the initiative as a core project since the goal of Swift River is central to the group’s mission: the crowdsourcing of crisis information.

Kaushal and Chris Blow gave the first formal presentation of Swift River during our first Ushahidi strategy meeting in Orlando last March where we formally established the Swift River group, which includes Andrew Turner, Sean Gourely, Erik Hersman and myself in addition to Kaushal and Chris. Andrew has played a pivotal role in getting Swift River and Vote Report India off the ground and I highly recommend reading his blog post on the initiative.

The group now includes several new friends of Ushahidi, a number of whom kindly shared their time and insights this morning after Chris kicked off the meeting to bring everyone up to speed.  The purpose of this blog post is to outline how I hope Swift River moves forward based on this morning’s fruitful session. Please see my previous blog post for an overview of the basic methodology.

The purpose of the Swift River platform, as I proposed this morning, is to provide two core services. The first, to borrow Guarva Mishra‘s description, is to crowdsource the tagging of crisis information. The second is to triangulate the tagged information to assign reality scores to individual events. Confused? Not to worry, it’s actually really straightforward.

Crowdsourcing Tagging

Information on a developing crisis can be captured from several text-based sources such articles from online news media, Tweets and SMS, for example. Of course, video footage, pictures and satellite imagery can also provide important information, but we’re more interested in text-based data for now.

The first point to note is that information can range from being very structured to highly unstructured. The word structure is simply another way of describing how organized information is. A few examples are in order vis-a-vis text-based information.

A book is generally highly structured information. Why? Well, because the author hopefully used page numbers, chapter headings, paragraphs, punctuation, an index and table of contents. The fact that the book is structured makes it easier for the reader to find the information she is looking for. The other end of the “structure spectrum” would be a run-on sentence with nospacesandpunctuation. Not terribly helpful.

Below is a slide from a seminar I taught on disaster and conflict early warning back in 2006; ignore the (c).


The slide above depicts the tradeoff between control and structure. We can impose structure on data collected if we control the data entry process. Surveys are an example of a high-control process that yields high-structure. We want high structure because this allows us to find and analyze the data more easily (c.f. entropy). This has generally been the preferred approach, particularly amongst academics.

If we give up control, as one does when crowdsourcing crisis information, we open ourselves up to the possibility of having to deal with a range of structured and unstructured information. To make sense of this information typically requires data mining and natural language processing (NLP) techniques that can identify structure in said information. For example, we would want to identify nouns, verbs, places and dates in order to extra event-data.

One way to do this would be to automatically tag an article with the parameters “who, what, where and when.” A number of platforms such as Open Calais and Virtual Research Associate’s FORECITE already do this. However, these platforms are not customized for crowdsourcing of crisis information and most are entirely closed. (Note: I did consulting work for VRA many years ago).

So we need to draw (and modify) relevant algorithms that are publically available and provide and a user-friendly interface for human oversight of the automated tagging (what we also referred to as crowdsourcing the filter). Here’s a proposed interface that Chris recently designed for Swift River.


The idea would be to develop an algorithm that parses the text (on the left) and auto-suggests answers for the tags (on the right). The user would then confirm or correct the suggested tags and the algorithm would learn from it’s mistakes. In other words, the algorithm would become more accurate over time and the need for human oversight would decrease. In short, we’d be developing a data-driven ontology backed up by Freebase to provide semantic linkages.

VRA already does this but, (1) the data validation is carried out by one (poor) individual, (2) the articles were restricted to the headlines from Reuters and Agence France Press (AFP) newswires, and (3) the project did not draw on semantic analysis. The validation component entailed making sure that events described in the headlines were correctly coded by the parser and ensuring there were no duplicates. See VRA’s patent for the full methodology (PDF).

Triangulation and Scoring

The above tagging process would yield a highly structured event dataset like the example depicted below.


We could then use simple machine analysis to cluster the same events together and thereby do away with any duplicate event-data. The four records above would then be collapsed into one record:


But that’s not all. We would use a simple weighting or scoring schema to assign a reality score to determine the probability that the event reported really happened. I already described this schema in my previous post so will just give one example: An event that is reported by more than one source is more likely to have happened. This increases the reality score of the event above and pushes it higher up the list. One could also score an event by the geographical proximity of the source to the reported event, and so on. These scores could be combined to give an overall score.

Compelling Visualization

The database output above is not exactly compelling to most people. This is where we need some creative visualization techniques to render the information more intuitive and interesting. Here are a few thoughts. We could draw on Gapminder to visualize the triangulated event-data over time. We could also use the idea of a volume equalizer display.


This is not the best equalizer interface around for sure, but hopefully gets the point across. Instead of decibels on the Y-axis, we’d have probability scores that an event really happened. Instead of frequencies on the X-axis, we’d have the individual events. Since the data coming in is not static, the bars would bounce up and down as more articles/tweets get tagged and dumped into the event database.

I think this would be an elegant way to visualize the data, not least because the animation would resemble the flow or waves of a swift river but the idea of using a volume equalizer could be used as analogy to quiet the unwanted noise. For the actual Swift River interface, I’d prefer using more colors to denote different characteristics about the event and would provide the user with the option of double-clicking on a bar to drill down to the event sources and underlying text.

Patrick Philippe Meier

Mobile Crisis Mapping (MCM)

I first blogged about Mobile Crisis Mapping (MCM) back in October 2008 and several times since. The purpose of this post to put together the big picture. What do I mean by MCM? Why is it important? And how would I like to see MCM evolve?

Classical MCM

When I coined the term Mobile Crisis Mapping last October, I wrote that MCM was the next logical step in the field of crisis mapping. One month later, at the first Crisis Mappers Meeting, I emphasized the need to think of maps as communication tools and once again referred to MCM. In my posts on the Crisis Mapping Conference Proposal and A Brief History of Crisis Mapping, I referred to MCM but only in passing.

More recently, I noted the MCM component of the UN’s Threat and Risk Mapping (TRMA) project in the Sudan and referred to two projects presented at the ICTD2009 conference in Doha—one on quality of data collected using mobile phones and the second on a community-based mapping iniative called Folksomaps.

So what is Mobile Crisis Mapping? The most obvious answer is that MCM is the collection of georeferenced crisis information using peer to peer (P2P) mobile technology. Related to MCM are the challenges of data validation, communication security and so on.

Extending MCM

But there’s more. P2P communication is bi-directional, e.g., two-way SMS broadcasting. This means that MCM is also about the ability of the end-user in the field being to query a crisis map using an SMS and/or voice-based interface. Therein lies the combined value of MCM: collection and query.

The Folksomaps case study comes closest to what I have in mind. The project uses binary operators to categorize relationships between objects mapped to render queries possible. For instance, ‘is towards left of’ could be characterized as <Libya, Egypt>.

The methodology draws on the Web Ontology Language (OWL) to model the categorical characteristics of an object (e.g., direction, proximity, etc), and thence infer new relationships not explicitly specified by users of the system. In other words, Folksomaps provides an ontology of locations.

Once this ontology is created, the map can actually be queried at a distance. That’s what I consider to be the truly innovative and unique aspect of MCM. The potential added value is huge, and James BonTempo describes exactly how huge MCM could be in his superb presentation on extending FrontlineSMS.

An initiative related to Folksomaps and very much in line with my thinking about MCM is Cartagen. This project uses string-based geocoding (e.g. “map Bhagalpur, India”) to allow users in the field to produce and search their own maps by using the most basic of mobile phones. “This widens participation to 4 billion cell phone users worldwide, as well as to rural regions outside the reach of the internet. Geographic mapping with text messages has applications in disaster response and health care.”

MCM Scenario

The query functionality is thus key to Mobile Crisis Mapping. One should be able to “mobile-query” a crisis map by SMS or voice.

If I’m interfacing with an Ushahidi deployment in the Sudan, I should be able to send an SMS to find out where, relative to my location, an IDP camp is located; or where the closest airfield is, etc. Query results can be texted back to the mobile phone and the user can forward that result to others. I should also be able to call up a designated number and walk through a simple Interactive Voice Response (IVR) interface to get the same answer.

Once these basic search queries are made available, more complex, nested queries can be developed—again, see James BonTempo’s presentation to get a sense of the tremendous potential of MCM.

The reason I see MCM as the next logical step in the field of crisis mapping is because more individuals have access to mobile phones in humanitarian crises than a computer connected to the Web. In short, the point of Mobile Crisis Mapping is to bring Crisis Mapping Analytics (CMA) to the mobile phone.

Patrick Philippe Meier