The Netherlands Red Cross data initiative – 510 –  is focussing on prototyping innovative solutions for disaster preparedness. However, when a disaster strikes, there is a more direct need for Surge Information Management support. Netherlands Red Cross, the youngest member of the IFRC Surge Information Management Systems (IFRC SIMS) is building its capacity to respond to the immediate disaster, with valuable operational support, both in the field and remote. In October and November, in light of two major disasters happening, our team assigned some of its resources to this urgent line of work. Remote support was given to field teams in Haiti, to help them make sense of the damage done by hurricane Matthew response in Haiti. Our team member Jannis Visser was deployed to the Philippines to assist the Philippine Red Cross in the response to super typhoon Haima. In below blog-post you can read his detailed account.

Joint effort for typhoon Haima

Our team’s involvement with typhoon Haima started a few days before it made landfall on October 19th in the Philippines. As the typhoon was quickly gaining in strength over the Pacific Ocean and making its way to northern Luzon region, the team was mobilized to be on stand-by. All necessary data and algorithms were put in place to run the newly developed Priority Index model, to predict the extent of damage per municipality within 12 hours of the typhoon. The results were quickly picked up by UN OCHA and the Philippine Red Cross (PRC), as it gave them an overview of the geographic distribution of damage when there were no other sources or reports available yet. (More information on the model can be found here and here).

This is not where our involvement ended however. As more assessment reports were coming in, in the days after the typhoon, the devastating extent of the damage become apparent. In the worst hit municipalities almost all houses were at least partly damaged, while simultaneously entire crop fields were turned to waste, right on the eve of harvesting season. The Disaster Management Services team of the PRC, already stretched to its limits by two earlier smaller typhoons, issued a request for information management support. Tasks involved collecting, streamlining and cleaning data sources, analyzing them, providing visualizations, and more, all aimed to contribute to a more efficient and targeted response. Via the International Federation of the Red Cross (IFRC) and its Surge Information Management System (SIMS) the alert led to the deployment of Jannis for the month of November to PRC’s headquarter in Manila.

The assistance offered to the PRC during this month consisted of many different smaller and larger tasks. Below, three aspects of the work are discussed in detail.

Creating a visual overview of all Red Cross response activities

One of the first important tasks upon arrival was to create an overview of all PRC response activities. Many emergency activities are automatically started according to certain procedures, but it proves hard to keep an overview of what the PRC is doing where exactly and how many people they are reaching with these activities. By making maps as well as an interactive dashboard, visualizing the response activities this challenge was addressed.

3W (Who does What Where) dashboard

The dashboard allows users to browse total number of activities and reached beneficiaries and to filter on offered services (e.g. food, sleeping kits, health support, rescued persons, etc.), on the supporting  organization (which allows the IFRC to identify their own activities for example) and on municipality or province. Internally, this overview, that is updated three times a week, is important for operational steerage and is an essential building block of any following gap analysis on where more relief is needed. Externally, this overview is valuable for coordinating with government and other agencies, as well as to display the PRC response to the outside world.

Setting up a fact-based prioritization of municipalities

Another information need was already provided by our Priority Index model, which predicted priority areas on the day of the typhoon. This identification of priority areas remained important for decision-making weeks after the typhoon as well. As government-counted damage figures per municipality were coming in, we merged them with the predicted figures, to keep the overall view as up-to-date as possible. After a few weeks, the predicted figures had largely been replaced by actual figures. Early assessments had been done, and the PRC had by now decided to focus on Shelter (rebuilding houses) and Livelihood (because of crop failures). The follow up task is to decide at a lower level where to deliver these humanitarian services.

The impact of Typhoon Haima in terms of casualties was relatively low (as compared to hurricane Matthew for example), but the impact in terms of damage to shelter and livelihood was tremendous. The lack of casualties implied less media coverage: in Dutch media at least the typhoon was – with Matthew still filling the news – barely picked up. The coverage of the necessary funds from international donors was low as well. The PRC had insufficient funds to carry out programs for all identified needs. Good prioritization of where relief was needed most became even more important.

To this end, we gathered multiple data sources and integrated damage data with vulnerability indicators: with equal damage, more socially vulnerable populations are more in need than others. By taking post-disaster damage data and pre-disaster vulnerability data together, we arrived at a priority ranking of which municipalities were in most need of assistance. This was visualized in a dashboard . The ranking was used by the PRC to decide in which municipalities to start its Shelter Early Recovery programs.

Municipality Priority Overview

During Jannis’ time in Manila we identified with PRC the need for a more formal approach to Community Risk Assessment and  Priority Selection, in the form of a toolbox. This toolbox should integrate and visualize different sub-national data sources for the Philippines on hazard exposure, vulnerability and coping capacity. (A beta version of this visualization is available here). These three components together identify at-risk areas and can therefore be used to community selection for DRR projects. Once a disaster strikes, the expected hazards is replaced, first by predicted damage figures – through integration of our Priority Index model – and later by actual government damage figures. At all times, the hazard or damage figures are combined with pre-disaster vulnerability and coping capacity to identify priority areas at each stage of the disaster cycle. In the coming year we will likely see an intensification of this cooperation, in order to fully implement the Community Risk Assessment and  Priority Toolbox in the operations of the PRC. The 510 team is already constructing this same toolbox for more countries besides the Philippines, with an eventual global scope in mind.

General reflections

Deployments like this one offer a good opportunity for humanitarian data analysts to contribute in a very direct way. Whereas with disaster preparedness work the impact is more long-term and therefore less visible, the impact of disaster response is immediate and observable. Some general reflections on such deployments are noted here:

The work partly consists of specific request by the PRC. But it also offers plenty of room for initiative: by walking around and talking to team members, you automatically start to get a feel for the information needs which you can act on. The priority ranking of municipalities was a good example of this. The PRC are very receptive to new ideas.

A very valuable and noteworthy aspect of the setup of this deployment comes from the SIMS ( network. Namely, through this system it is very easy to get remote assistance and advice from other SIMS members back in in the British, American and Netherlands Red Cross Societies.

All over the Red Cross movement we are building our capacity on data and information management. Deployments of data minded people are valuable for both the receiving organisation and the sending organisation alike. For instance, maintenance and updating of dashboards have been embedded in PRC operations, together with other IM best practices, gathered throughout the movement. At the same time, first hand experiences within an operational response are also invaluable for humanitarian data analysts and innnovators, who aim to build new solutions for humanitarian response.

Our champions

510An initiative of the Netherlands Red Cross. We want to shape the future of humanitarian aid by converting data into understanding, and put it in the hands of humanitarian relief workers, decision makers and people affected, so that they can better prepare for and cope with disasters and crises. Among our data scientists are many volunteers and their input to our work is highly appreciated.

Want to join us and have an impact in humanitarian aid through the use of data? Contact us.

Netherlands Red Cross a humanitarian aid organization

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