Good data cuts through the chaos in Haiti

How aggregated data sources and deep analysis are helping Haiti relief efforts

Palantir screenshot showing SMS activity in HaitiAs computer scientists and technologists, we’re used to dealing with large numbers in the abstract. But expressed in human terms, the mind-boggling numbers of the Haiti earthquake — 250,000 dead, 300,000 injured and more than 1 million people left homeless — are hard to comprehend.

The recovery from a disaster of this magnitude presents some important tasks for information technology: coordination of effort, triaging those most in need, and getting good data into the hands of decision makers and aid workers.

Here’s a partial list of aid, relief, and rescue organizations currently in Haiti, gleaned from Wikipedia:

  • An Argentine military field hospital.
  • The Red Cross/Crescent, in various forms.
  • The U.S. military.
  • Multiple U.N. agencies.
  • Remnants of the Haitian government.
  • The French navy.
  • Sri Lankan relief workers.
  • At least 2,000 rescuers from 43 different groups (along with 161 search dogs).

A wealth of collaborators like this presents unique challenges around information fusion. Unlike business competitors or opposing sides of a war, the different groups want to share as much information as possible to achieve their common goal.

Each organization has a produced a fairly detailed picture of the parts of Haiti they are interacting with. Each organization also wants to consume every other organization’s detailed knowledge of the situation. To act effectively, they need to integrate that knowledge into a common operating picture that accurately models the situation on the ground yesterday, today, and tomorrow.

Better coordination through data

Our reaction to the earthquake was to try to help in the best way we knew how. We set up a publicly available instance of our Palantir Government product, already loaded with relevant data, for use by aid workers and organizations working in Haiti. Using relevant, open-source data, we started modeling a picture of what’s going in Haiti.

Our first cut was to include the locations and names of collapsed buildings, internally displaced people (IDP) camps, and Misson 4636 SMS messages, among others. We also added in map layers that let us see what administrative zone any point on the map was located in.

Having mapped the data into this model, users have access to it through a suite of visualization, analysis, querying, and collaboration tools that allow them to get useful answers to practical questions. Here are some examples:

  • Which administrative sectors have had the most SMS requests for food in the past 24 hours?
  • What collapsed buildings are suspected to contain hazardous materials?
  • Are any IDP camps close enough to hazmat sites to warrant special precautions? Should residents be moved?

Next: Stay ahead of Haiti’s rainy season

With the infrastructure of the country destroyed, Haiti’s rain and hurricane season will be more dangerous than usual. Not only are the normal structures that protect citizens from the waters gone, but people have moved out of the ruins of Port-au-Prince to hastily constructed IDP camps, some of which are sitting in the flood plains of Haiti’s waterways.

The essential question facing relief workers is: Which of the approximately 2,500 IDP camps are most at risk from flooding?

In a place like the United States, an earthquake response and recovery team could engage the services and expertise of the U.S. Geological Survey (USGS), which maintains the National Water Information System. No such luck in Haiti, where the closest thing to the USGS is the Centre National de l’Information Géo-Spatiale. A quick look at the organization’s website shows it didn’t really make it through the earthquake.

We decided to help out. Since we’re starting from square one, we put together data from the Army Geospatial Center, the U.N., NOAA, Haiti-based NGOs, a number of academic papers, and even geo-tagged photos from Flickr. The time it took to integrate this data? About six hours. Time it took to do the analysis? About seven minutes. Amount of that work that is reusable? All of it.

The best way to improve this analysis is to add detailed information about flooding, gathered from the field. We’re looking to get new conduits of information into the Haiti instance as the rains really pick up.

If you’d like to help us, we’re accepting new data sources, analyses, and contact with
relief organizations.

Volunteers, supplies, and goodwill are only the raw ingredients to recovery. It’s the efficient and timely application of those resources to Haiti’s most pressing problems that will make recovery a reality.

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  • Jim Taylor

    By what means are you expecting people to look at this data? On cellphones? On computer? I think what you’re working on is phenomenal. Can you give an example of how it has proven helpful in one specific scenario?