Tag Archives: FAST

From Crowdsourcing Crisis Information to Crowdseeding Conflict Zones (Updated)

Friends Peter van der Windt and Gregory Asmolov are two of the sharpest minds I know when it comes to crowdsourcing crisis information and crisis response. So it was a real treat to catch up with them in Berlin this past weekend during the “ICTs in Limited Statehood” workshop. An edited book of the same title is due out next year and promises to be an absolute must-read for all interested in the impact of Information and Communication Technologies (ICTs) on politics, crises and development.

I blogged about Gregory’s presentation following last year’s workshop, so this year I’ll relay Peter’s talk on research design and methodology vis-a-vis the collection of security incidents in conflict environments using SMS. Peter and mentor Macartan Humphreys completed their Voix des Kivus project in the DRC last year, which ran for just over 16 months. During this time, they received 4,783 text messages on security incidents using the FrontlineSMS platform. These messages were triaged and rerouted to several NGOs in the Kivus as well as the UN Mission there, MONUSCO.

How did they collect this information in the first place? Well, they considered crowdsourcing but quickly realized this was the wrong methodology for their project, which was to assess the impact of a major conflict mitigation program in the region. (Relaying text messages to various actors on the ground was not initially part of the plan). They needed high-quality, reliable, timely, regular and representative conflict event-data for their monitoring and evaluation project. Crowdsourcing is obviously not always the most appropriate methodology for the collection of information—as explained in this blog post.

Peter explained the pro’s and con’s of using crowdsourcing by sharing the framework above. “Knowledge” refers to the fact that only those who have knowledge of a given crowdsourcing project will know that participating is even an option. “Means” denotes whether or not an individual has the ability to participate. One would typically need access to a mobile phone and enough credit to send text messages to Voix des Kivus. In the case of the DRC, the size of subset “D” (no knowledge / no means) would easily dwarf the number of individuals comprising subset “A” (knowledge / means). In Peter’s own words:

“Crowdseeding brings the population (the crowd) from only A (what you get with crowdsourcing) to A+B+C+D: because you give phones&credit and you go to and inform the phoneholds about the project. So the crowd increases from A to A+B+C+D. And then from A+B+C+D one takes a representative sample. So two important benefits. And then a third: the relationship with the phone holder: stronger incentive to tell the truth, and no bad people hacking into the system.”

In sum, Peter and Macartan devised the concept of “crowdseeding” to increase the crowd and render that subset a representative sample of the overall population. In addition, the crowdseeding methodology they developed genera-ted more reliable information than crowdsourcing would have and did so in a way that was safer and more sustainable.

Peter traveled to 18 villages across the Kivus and in each identified three representatives to serve as the eyes and years of the village. These representatives were selected in collaboration with the elders and always included a female representative. They were each given a mobile phone and received extensive training. A code book was also shared which codified different types of security incidents. That way, the reps simply had to type the number corresponding to a given incident (or several numbers if more than one incident had taken place). Anyone in the village could approach these reps with relevant information which would then be texted to Peter and Macartan.

The table above is the first page of the codebook. Note that the numerous security risks of doing this SMS reporting were discussed at length with each community before embarking on the selection of 3 village reps. Each community decided to voted to participate despite the risks. Interestingly, not a single village voted against launching the project. However, Peter and Macartan chose not to scale the project beyond 18 villages for fear that it would get the attention of the militias operating in the region.

A local field representative would travel to the villages every two weeks or so to individually review the text messages sent out by each representative and to verify whether these incidents had actually taken place by asking others in the village for confirmation. The fact that there were 3 representatives per village also made the triangulation of some text messages possible. Because the 18 villages were randomly selected as part the randomized control trial (RCT) for the monitoring and evaluation project, the text messages were relaying a representative sample of information.

But what was the incentive? Why did a total of 54 village representatives from 18 villages send thousands of text messages to Voix des Kivus over a year and a half? On the financial side, Peter and Macartan devised an automated way to reimburse the cost of each text message sent on a monthly basis and in addition provided an additional $1.5/month. The only ask they made of the reps was that each had to send at least one text message per week, even if that message had the code 00 which referred to “no security incident”.

The figure above depicts the number of text messages received throughout the project, which formally ended in January 2011. In Peter’s own words:

“We gave $20 at the end to say thanks but also to learn a particular thing. During the project we heard often: ‘How important is that weekly $1.5?’ ‘Would people still send messages if you only reimburse them for their sent messages (and stop giving them the weekly $1.5)?’ So at the end of the project [...] we gave the phone holder $20 and told them: the project continues exactly the same, the only difference is we can no longer send you the $1.5. We will still reimburse you for the sent messages, we will still share the bulletins, etc. While some phone holders kept on sending textmessages, most stopped. In other words, the financial incentive of $1.5 (in the form of phonecredit) was important.”

Peter and Macartan have learned a lot during this project, and I urge colleagues interested in applying their project to get in touch with them–I’m happy to provide an email introduction. I wish Swisspeace’s Early Warning System (FAST) had adopted this methodology before running out of funding several years ago. But the leadership at the time was perhaps not forward thinking enough. I’m not sure whether the Conflict Early Warning and Response Network (CEWARN) in the Horn has fared any better vis-a-vis demonstrated impact or lack thereof.

To learn more about crowdsourcing as a methodology for information collection, I recommend the following three articles:

A Brief History of Crisis Mapping (Updated)

Introduction

One of the donors I’m in contact with about the proposed crisis mapping conference wisely recommended I add a big-picture background to crisis mapping. This blog post is my first pass at providing a brief history of the field. In a way, this is a combined summary of several other posts I have written on this blog over the past 12 months plus my latest thoughts on crisis mapping.

Evidently, this account of history is very much influenced by my own experience so I may have unintentionally missed a few relevant crisis mapping projects. Note that by crisis  I refer specifically to armed conflict and human rights violations. As usual, I welcome any feedback and comments you may have so I can improve my blog posts.

From GIS to Neogeography: 2003-2005

The field of dynamic crisis mapping is new and rapidly changing. The three core drivers of this change are the increasingly available and accessible of (1) open-source, dynamic mapping tools; (2) mobile data collection technologies; and lastly (3) the development of new methodologies.

Some experts at the cutting-edge of this change call the results “Neogeography,” which is essentially about “people using and creating their own maps, on their own terms and by combining elements of an existing toolset.” The revolution in applications for user-generated content and mobile technology provides the basis for widely distributed information collection and crowdsourcing—a term coined by Wired less than three years ago. The unprecedented rise in citizen journalism is stark evidence of this revolution. New methodologies for conflict trends analysis increasingly take spatial and/or inter-annual dynamics into account and thereby reveal conflict patterns that otherwise remain hidden when using traditional methodologies.

Until recently, traditional mapping tools were expensive and highly technical geographic information systems (GIS), proprietary software that required extensive training to produce static maps.

In terms of information collection, trained experts traditionally collected conflict and human rights data and documented these using hard-copy survey forms, which typically became proprietary once completed. Scholars began coding conflict event-data but data sharing was the exception rather than the rule.

With respect to methodologies, the quantitative study of conflict trends was virtually devoid of techniques that took spatial dynamics into account because conflict data at the time was largely macro-level data constrained by the “country-year straightjacket.”

That is, conflict data was limited to the country-level and rarely updated more than once a year, which explains why methodologies did not seek to analyze sub-national and inter-annual variations for patterns of conflict and human rights abuses. In addition, scholars in the political sciences were more interested in identifying when conflict as likely to occur as opposed to where. For a more in-depth discussion of this issue, please see my paper from 2006  “On Scale and Complexity in Conflict Analysis” (PDF).

Neogeography is Born: 2005

The pivotal year for dynamic crisis mapping was 2005. This is the year that Google rolled out Google Earth. The application marks an important milestone in Neogeography because the free, user-friendly platform drastically reduced the cost of dynamic and interactive mapping—cost in terms of both availability and accessibility. Microsoft has since launched Virual Earth to compete with Google Earth and other  potential contenders.

Interest in dynamic crisis mapping did exist prior to the availability of Google Earth. This is evidenced by the dynamic mapping initiatives I took at Swisspeace in 2003. I proposed that the organization use GIS tools to visualize, animate and analyze the geo-referenced conflict event-data collected by local Swisspeace field monitors in conflict-ridden countries—a project called FAST. In a 2003 proposal, I defined dynamic crisis maps as follows:

FAST Maps are interactive geographic information systems that enable users of leading agencies to depict a multitude of complex interdependent indicators on a user-friendly and accessible two-dimensional map. […] Users have the option of selecting among a host of single and composite events and event types to investigate linkages [between events]. Events and event types can be superimposed and visualized through time using FAST Map’s animation feature. This enables users to go beyond studying a static picture of linkages to a more realistic dynamic visualization.

I just managed to dig up old documents from 2003 and found the interface I had designed for FAST Maps using the template at the time for Swisspeace’s website.

fast-map1

fast-map2

However, GIS software was (and still is) prohibitively expensive and highly technical. To this end, Swisspeace was not compelled to make the necessary investments in 2004 to develop the first crisis mapping platform for producing dynamic crisis maps using geo-referenced conflict data. In hindsight, this was the right decision since Google Earth was rolled out the following year.

Enter PRIO and GROW-net: 2006-2007

With the arrival of Google Earth, a variety of dynamic crisis maps quickly emerged. In fact, one if not the first application of Google Earth for crisis mapping was carried out in 2006 by Jen Ziemke and I. We independently used Google Earth and newly available data from the Peace Research Institute, Oslo (PRIO) to visualize conflict data over time and space. (Note that both Jen and I were researchers at PRIO between 2006-2007).

Jen used Google Earth to explain the dynamics and spatio-temporal variation in violence during the Angolan war. To do this, she first coded nearly 10,000 battle and massacre events as reported in the Portuguese press that took place over a 40 year period.

Meanwhile, I produced additional dynamic crisis maps of the conflict in the Democratic Republic of the Congo (DRC) for PRIO and of the Colombian civil war for the Conflict Analysis Resource Center (CARC) in Bogota. At the time, researchers in Oslo and Bogota used proprietary GIS software to produce static maps (PDF) of their newly geo-referenced conflict data. PRIO eventually used Google Earth but only to publicize the novelty of their new geo-referenced historical conflict datasets.

Since then, PRIO has continued to play an important role in analyzing the spatial dynamics of armed conflict by applying new quantitative methodologies. Together with universities in Europe, the Institute formed the Geographic Representations of War-net (GROW-net) in 2006, with the goal of “uncovering the causal mechanisms that generate civil violence within relevant historical and geographical and historical configurations.” In 2007, the Swiss Federal Institute of Technology in Zurich (ETH), a member of GROW-net, produced dynamic crisis maps using Google Earth for a project called WarViews.

Crisis Mapping Evolves: 2007-2008

More recently, Automated Crisis Mapping (ACM), real-time and automated information collection mechanisms using natural language processing (NLP) have been developed for the automated and dynamic mapping of disaster and health-related events. Examples of such platforms include the Global Disaster Alert and Crisis System (GDACS), CrisisWire, Havaria and HealthMap. Similar platforms have been developed for  automated mapping of other news events, such as Global Incident Map, BuzzTracker, Development Seed’s Managing the News, and the Joint Research Center’s European Media Monitor.

Equally recent is the development of Mobile Crisis Mapping (MCM), mobile crowdsourcing platforms designed for the dynamic mapping of conflict and human rights data as exemplified by Ushahidi (with FrontLineSMS) and the Humanitarian Sensor Web (SensorWeb).

Another important development around this time is the practice of participatory GIS preceded by the recognition that social maps and conflict maps can empower local communities and be used for conflict resolution. Like maps of natural disasters and environmental degradation, these can be developed and discussed at the community level to engage conversation and joint decision-making. This is a critical component since one of the goals of crisis mapping is to empower individuals to take better decisions.

HHI’s Crisis Mapping Project: 2007-2009

The Harvard Humanitarian Initiative (HHI) is currently playing a pivotal role in crafting the new field of dynamic crisis mapping. Coordinated by Jennifer Leaning and myself, HHI is completing a two-year applied research project on Crisis Mapping and Early Warning. This project comprised a critical and comprehensive evaluation of the field and the documentation of lessons learned, best practices as well as alternative and innovative approaches to crisis mapping and early warning.

HHI also acts as an incubator for new projects and  supported the conceptual development of new crisis mapping platforms like Ushahidi and the SensorWeb. In addition, HHI produced the first comparative and dynamic crisis map of Kenya by drawing on reports from the mainstream media, citizen journalists and Ushahidi to analyze spatial and temporal patterns of conflict events and communication flows during a crisis.

HHI’s Sets a Research Agenda: 2009

HHI has articulated an action-oriented research agenda for the future of crisis mapping based on the findings from the two-year crisis mapping project. This research agenda can be categorized into the following three areas, which were coined by HHI:

  1. Crisis Map Sourcing
  2. Mobile Crisis Mapping
  3. Crisis Mapping Analytics

1) Crisis Map Sourcing (CMS) seeks to further research on the challenge of visualizing disparate sets of data ranging from structural and dynamic data to automated and mobile crisis mapping data. The challenge of CMS is to develop appropriate methods and best practices for mashing data from Automated Crisis Mapping (ACM) tools and Mobile Crisis Mapping platforms (see below) to add value to Crisis Mapping Analytics (also below).

2) The purpose of setting an applied-research agenda for Mobile Crisis Mapping, or MCM, is to recognize that the future of distributed information collection and crowdsourcing will be increasingly driven by mobile technologies and new information ecosystems. This presents the crisis mapping community with a host of pressing challenges ranging from data validation and manipulation to data security.

These hurdles need to be addressed directly by the crisis mapping community so that new and creative solutions can be applied earlier rather than later. If the persistent problem of data quality is not adequately resolved, then policy makers may question the reliability of crisis mapping for conflict prevention, rapid response and the documentation of human rights violations. Worse still, inaccurate data may put lives at risk.

3) Crisis Mapping Analytics (CMA) is the third critical area of research set by HHI. CMA is becoming increasingly important given the unprecedented volume of geo-referenced data that is rapidly becoming available. Existing academic platforms like WarViews and operational MCM platforms like Ushahidi do not include features that allow practitioners, scholars and the public to query the data and to visually analyze and identify the underlying spatial dynamics of the conflict and human rights data. This is largely true of Automated Crisis Mapping (ACM) tools as well.

In other words, new and informative metrics are need to be developed to identify patterns in human rights abuses and violent conflict both retrospectively and in real-time. In addition, existing techniques from spatial econometrics need to be rendered more accessible to non-statisticians and built into existing dynamic crisis mapping platforms.

Conclusion

Jen Ziemke and I thus conclude that the most pressing need in the field of crisis mapping is to bridge the gap between scholars and practitioners who self-identify as crisis mappers. This is the most pressing issue because bridging that divide will enable the field of crisis mapping to effectively and efficiently move forward by pursuing the three research agendas set out by the Harvard Humanitarian Initiative (HHI).

We think this is key to moving the crisis-mapping field into more mainstream humanitarian and human rights work—i.e., operational response. But doing so first requires that leading crisis mapping scholars and practitioners proactively bridge the existing gap. This is the core goal of the crisis mapping conference that we propose to organize.

Patrick Philippe Meier