information system to assist survivors of disasters
TRANSCRIPT
Information system to assist survivors of disastersEmergency information system designed to coordinate information flow between the
population and aid agencies during disasters
Nicolas di TadaInSTEDD
Palo Alto, [email protected]
Timothy LargeThomson Reuters Foundation
London, [email protected]
Abstract— This paper describes a free and open source information system designed to be deployed in emergencies caused by sudden onset natural disasters. The aim is to streamline the communication flow and collaboration between media, aid workers and government agencies with the affected population, to help the latter get verified, accurate and actionable information that will enable them to make decisions and recover from the disaster.
The EIS system also provides means for affected population and field workers to channel vital data back up into aid response. This tool is part of a free information service run by Thomson Reuters Foundation to help survivors of natural disasters. It will serve the affected populations, local media and relief responders by providing fast, practical and verified information in local languages through the best means available.
Keywords-component; Information, disaster, response, communication, collaboration, media, aid.
I. INTRODUCTIONUnderlying EIS is the assumption that people caught up in
disasters are not helpless victims and that life saving, actionable information is as important as blankets and tarpaulin. As first responders, they need reliable information to make decisions and minimize the impact.
‘Information is a vital form of aid in itself […] Disaster-affected people need information as much as water, food, medicine or shelter. Information can save lives, livelihoods and resources.’ [1]
II. OBJECTIVESIn line with those assumptions, the design objectives have
been to:• Collect reports originated from the very first layer of
affected population - survivors; combining that with information received from the field including aid agencies, local media and government.
• Provide fast and reliable means to filter, search and detect important information in the pool of reports.
• Streamline the flow of information inside a distributed editorial team including local translators.
• Reach out to affected population with useful and actionable information in a timely manner.
III. WORKFLOWThe overall goal of the designed flow is to streamline the
creation and maximize the availability of relevant and credible information to communities. This implies the creation and distribution of such information. The flow improves when people have not only direct access to information, but the benefit also of credible intermediaries to help discover, gather, compare, contextualize, and share information. [2]
The flow (“Fig. 1”) can be divided in 4 stages:1. Collection of raw reports from the field.
2. Filtering, processing and identification of important information.
3. Editorial process including verifying the accuracy of information, editing and translating the information into local languages.
4. Inform relevant stakeholders.
A. Subscription and CollectionCitizens can report their location by sending an SMS to
the system with the name of the village they are. That simple action registers them in the system to receive alerts targeted to that village.
Every citizen, either registered or not, can report information to the system through SMS.
The system allows submission of raw reports through several channels:
• SMS: through mobile aggregators (like Clickatell) or plugged-in phones. People can send information to a specific number, which then stores the message in the system.• Twitter: both streams from particular users or certain hash-tags can be imported.• Email: any number of email addresses can be checked by the system and directed to specific collection baskets or inboxes
• RSS: feeds can be checked periodically and new articles will appear in the system.• Web: any user with permissions can post information using the web interface.
1) Bounded and Unbounded Crowd-sourcingThrough the use of a feature we called "Working groups"
and in combination with the "Baskets", both bounded and unbounded crowd-sourcing processes can coexist in the stream.
Identified users can be assigned to "Working Groups" and the reports coming from those groups, can be configured to go to specific baskets. Therefore, some baskets can be monitored more often or by different people, depending on the working groups (bounded crowd) putting reports there. An unbounded basket could be even configured to be self-filtered by the same unbounded crowd and items with 10 or more people marking them as "alert" or a specific tag could then be pulled by another basket, which is monitored by some agency.
Using this combination of features a group of users can collaborate around a pool or flow of information sifting useful information, tagging (or correcting tags suggested by the system), classifying as urgent or alerts, adding geographic information (or correcting the one suggested by the system) and several other types of metadata that modules provide. B. Selection
Through a number of different tools, users monitoring the pool of raw reports can filter important information. The goal in this stage is to try to deal with as many situation awareness “demons” [3] as possible, being data overload and stressors
the most salient ones in these kinds of scenarios. These tools include search, tagging and automatic extraction of the location the text refers to. Other features that can be added to customize the experience and workflow include flagging documents, hiding them, commenting and relating them. The objective of these features is to aid in the process of collaboratively selecting useful information to initiate the editing workflow.C. Editing
A module called “Baskets” allows the configuration of a custom workflow tailored to the specific team and situation. The module works based on the idea of baskets. Each basket is a collection of pieces of information (items), that acts both as inbox and outbox. Each user in the system is given permissions to read, write, move in or move out items from one or more baskets.
Typically, a user would monitor one or more baskets based on his role. As new items appear, he will decide what to do, act on the item, flag it as urgent, write a comment, create a new alert or translate it to another language. He would then put the new item or the edited one in another basket for someone else to do his part of the job.D. Informing
Once an alert is ready to be sent out to relevant recipients, the system allows the user to target affected population by location through a map with references of clusters of citizens. The user sees a map with the different groups of citizens and the number of citizens in each group. “Fig. 2”
The objective of targeting by location is not only to keep the information relevant to everyone, but also to avoid that
Figure 1. Information workflow
people in a desperate situation end up travelling to a distant location just because they received a message saying there is food or blankets there.
The alert can be sent through SMS and Email, and can also be addressed to specific working groups, like aid workers, local media or government agencies.
IV. TECHNOLOGY
The EIS system was built on top of RIFF (http://instedd.org/evolve), an InSTEDD platform tool developed as a general collaboration environment for content creation, social metadata annotation, and automated analysis with potential applicability in a wide range of areas.
RIFF is a modular and extensible free open source platform built in C#. The platform tool was developed during 2008 and 2009 as an effort to provide an environment that could collect heterogeneous information sources and leverage the power of human expert collaboration with machine learning and visualization aids.
RIFF is extensible through several different means:
5. Developers can add new types of information sources, even custom or proprietary
6. Modules can be developed to add new filters, provide additional functionality around classifying or tagging items, extend the model of each item by adding new fields or properties, create new visualizations and automatically extract information during the import phase. Most of the basic functionality is developed through modules.
7. Plugging the output of RIFF to another system. The tool allows the extraction of tailored and filtered RSS feeds, email subscriptions or push selected data to custom endpoints.
A complete picture of the platform architecture and interactions can be seen in Figure 3.
The choice of creating a new tool was based on the following observations:
8. Existing free open source information aggregators were based on more generic solutions like content management systems. This creates the problem of a platform too generic and abstract to provide a productive and friendly environment to develop extensions. Riff is collaboration tool based around information aggregation and provides specific extension points that allow the developer to focus only in the intended task or feature.
9. Machine learning classifiers are usually stand-alone ad-hoc applications or libraries, without a context integrated with information aggregation and collective analytics.
10. Many powerful analytic environments are either proprietary or commercial.
EIS was designed and implemented as a set of extensions to RIFF that allow receiving SMS messages, creating custom workflows for information and targeting outgoing SMS messages by location and working groups.
The choice of SMS as a communication channel was based on:
• Provides a two-way channel: citizens can report as well as receive information.• Allows for information to be targeted to citizens based on their reported location and needs, while still allowing for bulk sending.• Allows for semi-structured reports to be parsed: locations can be extracted, keywords can be identified and other entities like names or organizations can be
Figure 2. Sending information to citizens
Figure 3. RIFF Architecture
extracted.A. Server Deployment
EIS is running right now on an Amazon EC2 instance, providing a very easy way for 3rd parties to quickly deploy and manage their own instances.
V. RESULTS
As of the moment of presenting this paper, the system was deployed with much success in Haiti:
• The population response was very high, getting more than 5.000 subscribers in less than 10 days. Currently above 15.000.
• More than 1.000 incoming SMS per day from the survivors, including emergencies, needs or information requests to a total of more than 50.000 messages processed inside the system.
• Several agencies, under the UN umbrella, are both receiving targeted information from citizens, and sending out messages regarding where to find food, register missing people, get shelter and health measures.
• More than 20 users from different agencies and groups.
Within hours of the earthquake, most of Haiti’s cell phone towers were still operational and text messages were getting though in most of the country but Port-Au-Prince. After 2 or 3 days of work, the 2 main telephone companies could get most of the capital antennas back to work. The EIS team was deployed less than 48hs after the earthquake and, in partnership with other organizations like Ushahidi and FronlineSMS:Medic, quickly began negotiations with the telephone companies to establish a shortcode for citizens to text-in and for the system to be able to send messages out. A diagram of the final ecosystem architecture of applications and organizations interacting around the shortcode and EIS can be seen in Figure 4.More at: http://www.alertnet.org/db/blogs/
1564/2010/00/24-120746-1.htmBefore the Haiti deployment, a pilot of the system was
conducted in Tomohon, North Sulawesi Indonesia, in partnership with the Local Red Cross and the local Search and Rescue Team, has proved the validity of our assumptions. Even if the incoming stream of reports from the field contained (by design) a low signal to noise ratio, the editorial
team managed to process the information, select relevant topics and going through a quick but reliable loop with local translators could provide alerts with verified data.
A short summary of the pilot can be seen at: http://www.alertnet.org/thefacts/reliefresources/125907780276.htm
VI. CONCLUSION
We believe that we have built a tool that can handle information collection, flow and reach out in a wide range of deployment scenarios in disaster situations, providing the support for channeling information up and down as needed among a heterogeneous ecosystem of stakeholders. The system will serve as the basis for further learning, development and work on the subject.
VII. ACKNOWLEDGMENTSWe would like to thank to all the people that worked in the
inception, design, development and testing of the system: Monique Villa, Timothy Large, Astrid Zweynert, Joanne Tomkinson, Thin Lei Win (Thomson Reuters Foundation); Eric Rasmussen, Robert Kirkpatrick, Eduardo Jezierski, Luke Beckman (InSTEDD); Juan Wajnerman, Brian Cardiff, Martín Verzilli, Ary Borenszweig, Andres Taraciuk, Mariana Galvan, Martin Scebba (Manas Technology Solutions).
The help and feedback of the Palang Merah Indonesia (Indonesian Red Cross) was essential to conduct the pilot and improve the system.
A special thank to Willie Smits who hosted us and helped us with the logistics in North Sulawesi.
VIII. REFERENCES1. International Federation of Red Cross and Red Crescent
(2005) World Disasters Report 2005: Focus on information in disasters, Kumarian Press, http://www.ifrc.org/publicat/wdr2005/index.asp
2. The Knight Comission (2009) Informing Communities: Sustaining Democracy in the Digital Age, http://report.knightcomm.org/
3. Mica R. Endsley (2003) Designing for Situation Awareness: An approach to user-centered design, CRC
4. Ushahidi: Project 4636 revisited, http://blog.ushahidi.com/index.php/2010/02/11/project-4636-revisited-the-updated-info-graphic/
Figure 4. Haiti deployment ecosystem of applications around the shortcode and EIS [4]