We are finally getting better at predicting organized conflict

Curated on Posted on by Stefaan Verhulst

Tate Ryan-Mosley at MIT Technology Review: “People have been trying to predict conflict for hundreds, if not thousands, of years. But it’s hard, largely because scientists can’t agree on its nature or how it arises. The critical factor could be something as apparently innocuous as a booming population or a bad year for crops. Other times a spark ignites a powder keg, as with the assassination of Archduke Franz Ferdinand of Austria in the run-up to World War I.

Political scientists and mathematicians have come up with a slew of different methods for forecasting the next outbreak of violence—but no single model properly captures how conflict behaves. A study published in 2011 by the Peace Research Institute Oslo used a single model to run global conflict forecasts from 2010 to 2050. It estimated a less than .05% chance of violence in Syria. Humanitarian organizations, which could have been better prepared had the predictions been more accurate, were caught flat-footed by the outbreak of Syria’s civil war in March 2011. It has since displaced some 13 million people.

Bundling individual models to maximize their strengths and weed out weakness has resulted in big improvements. The first public ensemble model, the Early Warning Project, launched in 2013 to forecast new instances of mass killing. Run by researchers at the US Holocaust Museum and Dartmouth College, it claims 80% accuracy in its predictions.

Improvements in data gathering, translation, and machine learning have further advanced the field. A newer model called ViEWS, built by researchers at Uppsala University, provides a huge boost in granularity. Focusing on conflict in Africa, it offers monthly predictive readouts on multiple regions within a given state. Its threshold for violence is a single death.

Some researchers say there are private—and in some cases, classified—predictive models that are likely far better than anything public. Worries that making predictions public could undermine diplomacy or change the outcome of world events are not unfounded. But that is precisely the point. Public models are good enough to help direct aid to where it is needed and alert those most vulnerable to seek safety. Properly used, they could change things for the better, and save lives in the process….(More)”.

CROWD4EMS: a Crowdsourcing Platform for Gathering and Geolocating Social Media Content in Disaster Response

Curated on Posted on by Stefaan Verhulst

Paper by Ravi Shankar et al” Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location.

The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event…(More)”.

Five Ethical Principles for Humanitarian Innovation

Curated on Posted on by Stefaan Verhulst

Peter Batali, Ajoma Christopher & Katie Drew in the Stanford Social Innovation Review: “…Based on this experience, UNHCR and CTEN developed a pragmatic, refugee-led, “good enough” approach to experimentation in humanitarian contexts. We believe a wide range of organizations, including grassroots community organizations and big-tech multinationals, can apply this approach to ensure that the people they aim to help hold the reigns of the experimentation process.

1. Collaborate Authentically and Build Intentional Partnerships

Resource and information asymmetry are inherent in the humanitarian system. Refugees have long been constructed as “‘victims”’ in humanitarian response, waiting for “salvation” from heroic humanitarians. Researcher Matthew Zagor describes this construct as follows: “The genuine refugee … is the passive, coerced, patient refugee, the one waiting in the queue—the victim, anticipating our redemptive touch, defined by the very passivity which in our gaze both dehumanizes them, in that they lack all autonomy in our eyes, and romanticizes them as worthy in their potentiality.”

Such power dynamics make authentic collaboration challenging….

2. Avoid Technocratic Language

Communication can divide us or bring us together. Using exclusive or “expert” terminology (terms like “ideation,” “accelerator,” and “design thinking”) or language that reinforces power dynamics or assigns an outsider role (such as “experimenting on”) can alienate community participants. Organizations should aim to use inclusive language than everyone understands, as well as set a positive and realistic tone. Communication should focus on the need to co-develop solutions with the community, and the role that testing or trying something new can play….

3. Don’t Assume Caution Is Best

Research tells us that we feel more regret over actions that lead to negative outcomes than we do over inactions that lead to the same or worse outcomes. As a result, we tend to perceive and weigh action and inaction unequally. So while humanitarian organizations frequently consider the implications of our actions and the possible negative outcome for communities, we don’t always consider the implications of doing nothing. Is it ethical to continue an activity that we know isn’t as effective as it could be, when testing small and learning fast could reap real benefits? In some cases, taking a risk might, in fact, be the least risky path of action. We need to always ask ourselves, “Is it really ethical to do nothing?”…

4. Choose Experiment Participants Based on Values

Many humanitarian efforts identify participants based on their societal role, vulnerability, or other selection criteria. However, these methods often lead to challenges related to incentivization—the need to provide things like tea, transportation, or cash payments to keep participants engaged. Organizations should instead consider identifying participants who demonstrate the values they hope to promote—such as collaboration, transparency, inclusivity, or curiosity. These community members are well-poised to promote inclusivity, model positive behaviors, and engage participants across the diversity of your community….

5. Monitor Community Feedback and Adapt

While most humanitarian agencies know they need to listen and adapt after establishing communication channels, the process remains notoriously challenging. One reason is that community members don’t always share their feedback on experimentation formally; feedback sometimes comes from informal channels or even rumors. Yet consistent, real-time feedback is essential to experimentation. Listening is the pressure valve in humanitarian experimentation; it allows organizations to adjust or stop an experiment if the community flags a negative outcome….(More)”.

Next generation disaster data infrastructure

Curated on Posted on by Stefaan Verhulst

Report by the IRDR Working Group on DATA and the CODATA Task Group on Linked Open Data for Global Disaster Risk Research: “Based on the targets of the Sendai Framework, this white paper proposes the next generation of disaster data infrastructure, which includes both novel and the most essential information systems and services that a country or a region can depend on to successfully gather, process and display disaster data to reduce the impact of natural hazards.

Fundamental requirements of disaster data infrastructure include (1) effective multi-source big disaster data collection (2) efficient big disaster data fusion, exchange and query (3) strict big disaster data quality control and standard construction (4) real time big data analysis and decision making and (5) user-friendly big data visualization.

The rest of the paper is organized as follows: first, several future scenarios of disaster management are developed based on existing disaster management systems and communication technology. Second, fundamental requirements of next generation disaster data infrastructure inspired by the proposed scenarios are discussed. Following that, research questions and issues are highlighted. Finally, policy recommendations and conclusions are provided at the end of the paper….(More)”.

The Art of Values-Based Innovation for Humanitarian Action

Curated on Posted on by Stefaan Verhulst

Chris Earney & Aarathi Krishnan at SSIR: “Contrary to popular belief, innovation isn’t new to the humanitarian sector. Organizations like the Red Cross and Red Crescent have a long history of innovating in communities around the world. Humanitarians have worked both on a global scale—for example, to innovate financing and develop the Humanitarian Code of Conduct—and on a local level—to reduce urban fire risks in informal settlements in Kenya, for instance, and improve waste management to reduce flood risks in Indonesia.

Even in its more-bureaucratic image more than 50 years ago, the United Nations commissioned a report to better understand the role that innovation, science, and technology could play in advancing human rights and development. Titled the “Sussex Manifesto,” the report outlined how to reshape and reorganize the role of innovation and technology so that it was more relevant, equitable, and accessible to the humanitarian and development sectors. Although those who commissioned the manifesto ultimately deemed it too ambitious for its era, the effort nevertheless reflects the UN’s longstanding interest in understanding how far-reaching ideas can elicit fundamental and needed progress. It challenged the humanitarian system to be explicit about its values and understand how those values could lead to radical actions for the betterment of humanity.

Since then, 27 UN organizations have formed teams dedicated to supporting innovation. Today, the aspiration to innovate extends to NGOs and donor communities, and has led to myriad approaches to brainstorming, design thinking, co-creation, and other activities developed to support novelty.

However, in the face of a more-globalized, -connected, and -complex world, we need to, more than ever, position innovation as a bold and courageous way of doing things. It’s common for people to demote innovation as a process that tinkers around the edges of organizations, but we need to think about innovation as a tool for changing the way systems work and our practices so that they better serve communities. This matters, because humanitarian needs are only going to grow, and the resources available to us likely won’t match that need. When the values that underpin our attitudes and behaviors as humanitarians drive innovation, we can better focus our efforts and create more impact with less—and we’re going to have to…(More)”.

Guide to Mobile Data Analytics in Refugee Scenarios

Curated on Posted on by Stefaan Verhulst

Book edited Albert Ali Salah, Alex Pentland, Bruno Lepri and Emmanuel Letouzé: “After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest r efugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics is necessary.

This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific community to enable research on urgent problems concerning refugees. The database, based on anonymized mobile call detail records (CDRs) of phone calls and SMS messages of one million Turk Telekom customers, indicates the broad activity and mobility patterns of refugees and citizens in Turkey for the year 1 January to 31 December 2017. Over 100 teams from around the globe applied to take part in the challenge, and 61 teams were granted access to the data.

This book describes the challenge, and presents selected and revised project reports on the five major themes: unemployment, health, education, social integration, and safety, respectively. These are complemented by additional invited chapters describing related projects from international governmental organizations, technological infrastructure, as well as ethical aspects. The last chapter includes policy recommendations, based on the lessons learned.

The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more….(More)”.

Sharing Private Data for Public Good

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst at Project Syndicate: “After Hurricane Katrina struck New Orleans in 2005, the direct-mail marketing company Valassis shared its database with emergency agencies and volunteers to help improve aid delivery. In Santiago, Chile, analysts from Universidad del Desarrollo, ISI Foundation, UNICEF, and the GovLab collaborated with Telefónica, the city’s largest mobile operator, to study gender-based mobility patterns in order to design a more equitable transportation policy. And as part of the Yale University Open Data Access project, health-care companies Johnson & Johnson, Medtronic, and SI-BONE give researchers access to previously walled-off data from 333 clinical trials, opening the door to possible new innovations in medicine.

These are just three examples of “data collaboratives,” an emerging form of partnership in which participants exchange data for the public good. Such tie-ups typically involve public bodies using data from corporations and other private-sector entities to benefit society. But data collaboratives can help companies, too – pharmaceutical firms share data on biomarkers to accelerate their own drug-research efforts, for example. Data-sharing initiatives also have huge potential to improve artificial intelligence (AI). But they must be designed responsibly and take data-privacy concerns into account.

Understanding the societal and business case for data collaboratives, as well as the forms they can take, is critical to gaining a deeper appreciation the potential and limitations of such ventures. The GovLab has identified over 150 data collaboratives spanning continents and sectors; they include companies such as Air France, Zillow, and Facebook. Our research suggests that such partnerships can create value in three main ways….(More)”.

This High-Tech Solution to Disaster Response May Be Too Good to Be True

Curated on Posted on by Stefaan Verhulst

Sheri Fink in The New York Times: “The company called One Concern has all the characteristics of a buzzy and promising Silicon Valley start-up: young founders from Stanford, tens of millions of dollars in venture capital and a board with prominent names.

Its particular niche is disaster response. And it markets a way to use artificial intelligence to address one of the most vexing issues facing emergency responders in disasters: figuring out where people need help in time to save them.

That promise to bring new smarts and resources to an anachronistic field has generated excitement. Arizona, Pennsylvania and the World Bank have entered into contracts with One Concern over the past year. New York City and San Jose, Calif., are in talks with the company. And a Japanese city recently became One Concern’s first overseas client.

But when T.J. McDonald, who works for Seattle’s office of emergency management, reviewed a simulated earthquake on the company’s damage prediction platform, he spotted problems. A popular big-box store was grayed out on the web-based map, meaning there was no analysis of the conditions there, and shoppers and workers who might be in danger would not receive immediate help if rescuers relied on One Concern’s results.

“If that Costco collapses in the middle of the day, there’s going to be a lot of people who are hurt,” he said.

The error? The simulation, the company acknowledged, missed many commercial areas because damage calculations relied largely on residential census data.

One Concern has marketed its products as lifesaving tools for emergency responders after earthquakes, floods and, soon, wildfires. But interviews and documents show the company has often exaggerated its tools’ abilities and has kept outside experts from reviewing its methodology. In addition, some product features are available elsewhere at no charge, and data-hungry insurance companies — whose interests can diverge from those of emergency workers — are among One Concern’s biggest investors and customers.

Some critics even suggest that shortcomings in One Concern’s approach could jeopardize lives….(More)”.

Guidance Note: Statistical Disclosure Control

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Centre for Humanitarian Data: “Survey and needs assessment data, or what is known as ‘microdata’, is essential for providing adequate response to crisis-affected people. However, collecting this information does present risks. Even as great effort is taken to remove unique identifiers such as names and phone numbers from microdata so no individual persons or communities are exposed, combining key variables such as location or ethnicity can still allow for re-identification of individual respondents. Statistical Disclosure Control (SDC) is one method for reducing this risk. 

The Centre has developed a Guidance Note on Statistical Disclosure Control that outlines the steps involved in the SDC process, potential applications for its use, case studies and key actions for humanitarian data practitioners to take when managing sensitive microdata. Along with an overview of what SDC is and what tools are available, the Guidance Note outlines how the Centre is using this process to mitigate risk for datasets shared on HDX. …(More)”.

Stop Surveillance Humanitarianism

Curated on Posted on by Stefaan Verhulst

Mark Latonero at The New York Times: “A standoff between the United Nations World Food Program and Houthi rebels in control of the capital region is threatening the lives of hundreds of thousands of civilians in Yemen.

Alarmed by reports that food is being diverted to support the rebels, the aid program is demanding that Houthi officials allow them to deploy biometric technologies like iris scans and digital fingerprints to monitor suspected fraud during food distribution.

The Houthis have reportedly blocked food delivery, painting the biometric effort as an intelligence operation, and have demanded access to the personal data on beneficiaries of the aid. The impasse led the aid organization to the decision last month to suspend food aid to parts of the starving population — once thought of as a last resort — unless the Houthis allow biometrics.

With program officials saying their staff is prevented from doing its essential jobs, turning to a technological solution is tempting. But biometrics deployed in crises can lead to a form of surveillance humanitarianism that can exacerbate risks to privacy and security.

By surveillance humanitarianism, I mean the enormous data collection systems deployed by aid organizations that inadvertently increase the vulnerability of people in urgent need….(More)”.