24 Hour Hackathon People

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Hackers in action at Hacking Aid (Photo: WFP/Angie Lee)

 

As some of you might have already guessed, we at mVAM LOVE hackathons. Last weekend, we had a chance to participate in another one: “Hacking Aid” which was organized by the Dutch Ministry of Foreign Affairs and PwC, together with UNHCR, OCHA and Leiden University’s Center of Innovation. This event brought together more than 70 participants from all walks of life – students, aid workers, programmers, developers, linguists, teachers, professionals from the private sector and government. A common thread linked them all: they were brimming with ideas to find digital solutions to some of the pressing challenges the humanitarian community currently faces.

The overall theme of this particular hackathon centered on finding ways to make humanitarian aid more efficient and transparent. Specifically, we looked at solutions that would enable self-reporting by affected populations, so that people in need would be able to report where, when and what type of help is needed.

In order to come up with specific challenges that could be addressed with practical solutions, we had a rapid prototyping session (a.k.a. think hard and quick) to define a problem around collecting and reporting data. This was followed by an open-mic stage, where we pitched our challenges to the hackers.

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Wael explains what we need (Photo: WFP/Angie Lee)

After working away for nearly 24 hours straight, the twelve teams submitted their final outputs for evaluation by an independent jury panel. The winner of the Hacking Aid award, Team Dream Catchers, developed an app to register complaints and feedback, even offline, from refugees in camps. The second winner, Team Seeing Hunger, proposed a solution to WFP’s challenge: a chatbot tool to pick up and verify self-initiated feedback or reports coming through social media.

A special mention went to Team Botcast, which won the Tech Award for the technically most impressive prototype with their chatbot for Dabanga radio station in Darfur. The chatbot facilitates the process of handling requests for assistance and protection. Team Transformers took home the Innovation Award for their app “Noci”, which uses audiovisual techniques to enable those whom are not able to read or write to report on their needs. 

The winners of the hackathon will have a change to travel to Geneva, where they will pitch their ideas to the board of UNHCR, and will receive support from PwC and Leiden University as they develop a prototype. All prototypes will be available open source.

We noticed that many teams proposed chatbot-based solutions to the challenges we pitched, which is exciting for us as it suggests this is a promising area for technological development. mVAM is already exploring how chatbots could be used to help WFP’s work and we hope to find ways to collaborate further with the teams from the hackathon and other partners to vet other ideas in the area of two-way communications.

Myanmar: assessing emergency needs without access

Photo: WFP/Myanmar

Photo: WFP/Myanmar

 

Late last year, an attack by an armed group on border police posts in Myanmar led to a government security sweep in Rakhine State and recurrent clashes and violence in many villages. As a result, access to a large part of the north of the state was closed off to humanitarian organizations, leaving the already highly vulnerable inhabitants of the townships to fend for themselves.

Unable to access the area since 9 October, WFP decided to use mobile surveys to conduct remote emergency assessments. While not as thorough as face-to-face assessments, mobile surveys could still provide a good snapshot of how people were coping in the areas that were closed off. Furthermore, mobile surveys serve as a means to address a critical information gap where there is little to no information about the needs of the most vulnerable and food insecure, as we have seen in complex emergency settings elsewhere such as during the Ebola crisis and Yemen. But let’s come back to Myanmar and rewind just a few years: hearing from people in these areas would have been impossible – essentially no one had mobile phones.

Myanmar’s mobile transformation

Myanmar’s telecommunication market has come a long way. Not so long ago, Myanmar was one of the “leastconnected countries in the world” – just seven years ago, SIM cards cost up to $1,500, and few people had them. In 2013, after the government awarded contracts to two foreign mobile operators, the price of a SIM card fell to $1.50 and network coverage began to roll out across the country. Once the mobile revolution began, things moved fast. Soon, mobile penetration exceeded even that of much better-off neighboring countries, such as Thailand[1]. By 2015, 96 percent of wards and 87 percent of villages in Myanmar had a mobile signal, and nearly 60 percent of households owned a mobile phone[2].

A case for mobile surveys in Myanmar

WFPs first mobile assessment in Myanmar took place in November 2016, with 32 key informants from 12 villages in Maungdaw and Buthidaung north, complementing face-to-face interviews of 48 WFP beneficiaries at 8 food distribution points in Buthidaung south. This was at the end of the lean season (the period between harvests when households’ food stocks tend to be the lowest), and respondents told us that due to the deteriorated security situation, people faced serious difficulties in reaching markets, were not able to go to work, nor access agricultural land and fishing areas and. Resulting crop losses could result in mid to long-term impact on food security while households’ terms of trade had decreased and posed a serious concern regarding their ability to purchase sufficient food.

Though low mobile penetration in rural areas of the country posed a challenge for phone surveys, people were nonetheless eager to participate in the survey and share their stories. In order to participate, some people even arranged to borrow phones from neighbors if they did not own one themselves.

A second phone survey in December allowed for a greater sample size and therefore a better understanding of the living conditions in the surveyed areas. WFP spoke to 116 respondents in 70 villages in Maungdaw Township. By this time, the people we spoke with mentioned that there was widespread food insecurity throughout the township. The situation was particularly problematic in the north, where markets were not functioning and access to agricultural land or fishing grounds was restricted. Livelihood opportunities were scarce and the lower demand for daily labour had had an immediate impact on the most vulnerable.

Photo: WFP/Myanmar

Photo: WFP/Myanmar

What’s next?

The data collected through the phone surveys helped WFP to get some understanding of the needs in the no-access areas, and to use this information for advocacy with the Government and humanitarian stakeholders. On 9 January 2017, after three months, WFP was finally granted access to all areas where it had operations prior to 9 October, and was able to distribute food to 35,000 people in the villages of Maungdaw north. With the area open again, WFP and its partners are now preparing for thorough assessments on the ground, which will give a fuller picture of the food security situation and also allow us to validate the findings of the phone surveys.


[1]http://lirneasia.net/wp-content/uploads/2015/07/LIRNEasia_MyanmarBaselineSurvey_DescriptiveStats_V1.pdf

[2]http://www.gsma.com/mobilefordevelopment/wp-content/uploads/2016/02/Mobile-phones-internet-and-gender-in-Myanmar.pdf

How many pizzas does it take to build a chatbot?

Hackers are hungry Photo: WFP/Pia Facultad

Hackers are hungry
Photo: WFP/Pia Facultad

This week, government, business, academia and civil society leaders will gather at Davos to discuss solutions to the world’s biggest challenges – including how new technologies can be leveraged to solve some of the most serious problems we face. At mVAM, we continue to explore how some of these technologies could be used to help eliminate chronic hunger, malnutrition and food insecurity – most recently looking at how chatbots could help collect important information during a humanitarian response.

Last week, our collaborators at Nielsen – one of the early supporters of mVAM – organized a 24-hour hackathon at the Nielsen Tech Hub in New York City. As part of ongoing efforts through Nielsen Cares, the hackathon aimed to develop an open-source humanitarian chatbot that can collect real-time information about food security. This came at the right time for WFP – we’d developed and tested a prototype of the chatbot with InSTEDD, and Nielsen’s technology and development input helped bring in important new capabilities. Ultimately, our goal is to field-test a chatbot in Haiti in the next few months to help us track food security conditions as people recover from the impacts of Hurricane Matthew.

The event was open to the public. A diverse group of students, volunteer hackers, and Nielsen staff showed up to take on the challenge, despite the wintry weather. InSTEDD’s Director of Platform Engineering, Nicolás di Tada also participated.

Much more than a chatbot

What the hackers built is much more that a chatbot: it is a bona-fide chat-based data collection and reporting system. Rather than attempt to outdo each other (as is the case in most hackathons), the teams split up to build the different components of the system. The different teams, made up of perfect strangers, communicated during the hackathon through Slack. After 24 hours, most components were fully coded up, but there were still bugs with the orchestrator and the gateway that additional post-hackathon work will resolve.

The architecture of the system, as defined by Nielsen, includes:

  • a management interface that allows an analyst to set up a questionnaire, including and skip logic, and validation rules that prompt the user when they enter a wrong answer. The interface was built using the Angular 2 JavaScript framework;
  • a gateway that is able to interact with respondents through Facebook Messenger and potentially other chat applications. The Facebook gateway was built on top of the AWS Lambda service;
  • a natural language processing engine that analyzes text on the fly. It allows the chatbot to ‘interpret’ a user’s answers. For now, the NLP engine processes English language text, although the engine includes a translation service and, by default, translates all languages to English for more advanced NLP tasks. The engine was built using the AWS Lambda service and leverages IBM Watson’s AlchemyLanguage service for text processing.;
  • a set of ‘backend APIs’ that manage respondent and survey data, route respondents from each response to the next question, and provide data to user interfaces .  The APIs were built using the Django framework for python and deploys on the AWS Elastic Beanstalk service;
  • an ‘orchestration layer’ that maintains survey status and routes messages between the end user and the various backend services. The orchestration service is built on top of the AWS Lambda service; and
  • a “reporting and data visualization engine”. Data vizzes were built using Highcharts, a JavaScript-based application. This allows an analyst to instantly see the results of the chatbot surveys.

 

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Leveraging cloud services from the Amazon Web Services product catalog, the teams were able to build a scalable, cost effective platform that can be deployed quickly to multiple locations globally.

Remember the humans

We also received tips from a chatbot specialist, Alec Lazarescu from Chatbots Magazine. He encouraged us to ‘onboard’ users with an initial message that gives people a clear idea of what the chatbot is for. He told us to avoid ‘dead ends’ and allow users to speak to a human being in case they get stuck.

We’re very grateful to Nielsen for their support and to all the participants for their energy and creativity. The next steps involve WFP and InSTEDD accessing the code and work on ironing out the kinks. We expect challenges with the natural language processing in Haitian Creole, a language that is probably under-researched. Making the different parts of the chatbot work together seamlessly also appears to be an area we will still have to work on.  And, of course, the final test will be to see whether our target group – people living in Haiti – find the chatbot engaging.

Can we reach rural women via mobile phone? Kenya case study

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WFP/Kusum Hachhethu

 

A few months ago, we published a blog post on our plans to test collecting nutrition data through SMS in Malawi and through live voice calls in Kenya. We just got back from Kenya where we conducted a large-scale mode experiment with ICRAF to compare nutrition data collected face-to-face with data collected through phone calls placed by operators at a call center. But before we started our experiment, we did a qualitative formative study to understand rural women’s phone access and use.

We traveled to 16 villages in Baringo and Kitui counties in Kenya, where we conducted focus group discussions and in-depth interviews with women. We also conducted key informant interviews with mobile phone vendors, local nutritionists, and local government leaders.

So in Kenya, can rural women be reached via mobile phone?

Here are the preliminary findings from our qualitative study:

  1. Ownership: Women’s phone ownership is high in both counties. However, ownership was higher in Kitui than Baringo, which is more pastoralist. From our focus group discussions and interviews, we estimate that 80-90% of women own phones in Kitui and 60-70% own phones in Baringo.
  1. Access: The majority of women had access to phones through inter- and intra-household sharing even if they didn’t own one themselves. This suggests that even women who don’t own a phone personally have access to phones that they may be able to use to participate in phone surveys.
  1. Usage: Women mostly use phones to make and receive calls, not send SMS. This supports our hypothesis that voice calls, not SMS, would be the optimal modality to reach women through mobile surveys.
  1. Willingness: Women were enthusiastic about participating in phone surveys during our focus group discussions and in-depth interviews, implying that they are interested in phone surveys and willing to take part.
  1. Trust: Unknown numbers create trust issues, but they are not insurmountable. Women voiced concerns about receiving phone calls from unknown numbers. Despite these trust issues, we were eventually able to successfully conduct our phone surveys after sensitizing the community, using existing community and government administration structures.
  1. Network: Poor network coverage, not gender norms or access, is the biggest barrier to phone surveys in the two counties. Women identified network coverage as the biggest barrier for communication. Some parts of the counties had poor to no network coverage. However, we found that phone ownership was high even in these areas, and women would travel to find network hotspots to make or receive phone calls.

So in conclusion, yes, in Kenya it is possible to reach rural women by phone.
Our findings from Kitui and Baringo counties show that we can reach women in similar contexts with mobile methodologies to collect information on their diet as well as their child’s diet.

We are also analysing the quantitative data from our mode experiment to find out whether data on women and children’s diet collected via live phone operators gives the same results as data collected via traditional face-to-face interviews.

Crowdsourcing food prices in remote areas: a bridge too far?

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Photo: WFP

We have success using crowdsourcing to collect food prices by SMS in the refugee camps of Kenya. The experiences made us curious about trying out other methods that could help us deliver data quickly and efficiently from the remote and hard to reach geographies  where WFP works.

We found out about a startup that specializes in crowdsourced data collection. Anonymous ‘contributors’ would carry out simple data collection tasks through a dedicated smartphone app, the sales pitch went. Intrigued, we decided to pilot this system to monitor food prices in a drought-affected area of Southern Africa. We were hoping to use the data to complement the information traditional information systems produce.  What did we learn?

The anonymous ‘citizen reporter’ is a myth. The company we worked with had to go through local organizations, such as NGOs, to find people able to collect the data for us. This a far cry from the vision of sourcing data from an anonymous crowd. There is more to finding contributors than putting out some ads on social media and magically reaching masses of people. Our contributors were not really anonymous and were easily identified by traders. In the end, the activity looked a lot like traditional tablet-based data collection. The World Bank also found the same thing when they contracted a private company for crowdsourcing. You can find more on their experience in here.

Getting started is labor intensive. It’s going to be a learning process for both your organization and the company, and this will mean investing significant staff time. On our side, since we were unfamiliar with the methodology, there were a lot of iterations as we attempted to specify commodity types and data types. This is perhaps surprising because we at WFP have been collecting food prices for a long time. It turns out we needed to revisit the commodity lists, specify unit measures — a process that required patience. On the company’s side, they had limited experience in the geographies of the pilot which could lead to an overestimation of what was possible and how quickly.

Expect long ramp up times.  The ramp up to the data volumes we wanted took months, because that time was needed to set up the system and recruit the local contributors. Our roll out was planned this way. Do not expect an army of anonymous contributors to materialize out of thin air.

It’s still hard to reach remote places. The crowdsourcing model is no silver bullet when it comes to reaching the remote places we were interested in monitoring. It proved hard or even impossible to source enough data from the more remote markets when using a crowdsourcing service. This is perhaps because of low smartphone penetration in remote locations, the high cost of sending a contributor to such places, or to poor connectivity. In contrast, collecting data from larger urban areas was much easier.

High costs are a barrier to handover in resource-poor environments. It became clear that the cost of the activity was higher than lower-tech alternatives. WFP works to enable handover of information systems to national authorities or other local partners. For the moment, the cost of app-based crowdsourcing is perhaps out of the financial reach of our local partners.

After some trial and error, we were able to obtain good quality data through crowdsourcing that was helpful to our field offices. However, ultimately we returned to our mVAM strategy – using phone calls to traders to collect food prices each week. Although our approach can’t cover as many commodities as the company’s crowdsourcing activity provided, it has its own advantages. It’s lower tech – there is no fancy app to download. There is no far away company to deal with. Above all, it’s an approach we can hand over to our local partners.

Our 5 mVAM Highs from 2016

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1. Awards for Remote Mobile Data Collection Work

At the Humanitarian Technology 2016 conference, our paper Knowing Just in Time Knowing Just in Time’ won Best Paper for Outstanding Impact. In the paper, we assessed mVAM’s contribution to decision-making by looking at use cases for mVAM in camps, conflict settings and vulnerable geographies. Check out our blog Tech for Humanity for more on it and our other conference paper  mVAM: a New Contribution to the Information Ecology of Humanitarian Work

To close the year, we had a nice surprise from Nominet Trust, the UK’s leading tech for good funder. We made their 100 most inspiring social innovations using digital technology to drive social change around the world.  

2. New Tech

In this day and age there’s a lot of buzz around data visualization. We’ve been honing our skills with Tableau. Check out the data visualizations we did for Yemen and Haiti.

We’re also in the era of Big Data. We partnered with Flowminder, experts in analyzing call detail records, to track displacement in Haiti after Hurricane Matthew.  Find out more in ‘After the storm: using big data to track displacement in Haiti

We’re also super excited about the chatbot we started developing for messaging apps and our roll out of Free Basics in Malawi which is allowing us to share the food prices we collect in mVAM surveys with people in Malawi With mVAM, our main focus has been reaching people on their simple feature phones. But we know that smartphone ownership is only going to increase. Contacting people through internet-enabled phones opens up loads of new forms of communication and data collection. is still reaching people on their -free basics

3. Expansion!

mVAM expanded to 16 new countries facing a wide set of challenges: conflict, El Nino drought, hurricanes, extremely remote geographies. We’ve been tracking and learning about what remote mobile data collection can add to food security monitoring systems and what its limits are in different contexts. For some of the highlights, check out our blogs on Afghanistan, Democratic Republic of Congo, Haiti, Nigeria, Papua New Guinea, and  El Nino in Southern Africa,

4. Dynamic Partnerships

To have a lasting impact, we need to work with governments. We are really proud of our partnership with CAID, the Cellule d’Analyses des Indicateurs du Développement  under the Prime Minister’s Office in the Democratic Republic of Congo. We collaborated on setting up a national market monitoring system- mKengela that they are now running. We’ve had intensive technical sessions with the CAID team in Rome and Kinshasa to work on solutions that will fit their data management and analysis needs. The CAID team even traveled to Johannesburg to share their remote mobile data experience with other African countries and help other governments use this technology.

We’re also working with Leiden University. Bouncing ideas off of their team at the Centre for Innovation helps us move forward on tricky challenges. We’re also collaborating with them to develop an online course where we’re going to share our methodologies and how to use remote technology to monitor food security. Check out Welcome to Vamistan for more.

We are in the field of tech. So we can’t do our job well without partnering with the private sector. It’s definitely a dynamic area, and also one where we at mVAM are learning what works best in melding our humanitarian goals with the exciting private tech potential out there. Check out our blog From the Rift Valley to Silicon Valley and our hackathon with Data Mission for more.

5. Learning- the neverending process

In addition to trying out new technology, we’ve been trying to answer some important questions about the live calls, SMS, and IVR surveys which make up the bulk of mVAM data collection.  We’re also doing mode experiments to understand how people answer differently based on which mode we use to contact them. Check out our first Mind the Mode article with more coming in 2017. In Kenya, we are looking into whether we can ask nutrition indicators through mVAM methods. A major challenge is reaching women through phone surveys so we organized a gender webinar with partners to learn from what they are doing- check out our key gender takeaways. These are key questions and they can’t be resolved overnight. But we’re making steady progress in understanding them, and we’re excited for what more we’ll find out in 2017.

Thanks to everyone who has supported our work this year and kept up with our blog!

mVAM recognized for innovation in the 2016 ‘Nominet 100’

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We’re pleased to announce that mVAM has been recognised as one of 2016’s 100 most inspiring social innovations using digital technology to drive social change around the world. The competition, the NT100, is run by the Nominet Trust, the UK’s leading tech for good funder.

The 2016 NT100 was selected from 700 projects reviewed by Nominet Trust and a panel of partner organisations including: Big Lottery Fund, Cancer Research UK, Comic Relief, Nominet, Oxfam, Telefonica O2 and Skoll Centre for Social Entrepreneurship.

mVAM has been recognised for its contribution to humanitarian interventions by leveraging mobile technology to provide frequent, lower cost food security data.

If you want to find out more about other NT100 projects check out their Social Tech Guide, a comprehensive collection of inspiring ways tech pioneers are changing lives, communities and our world for the better.

After the storm: using big data to track displacement in Haiti

Photo: Igor Rugwiza – UN/MINUSTAH


This week’s blog is a guest entry by Gabriela Alvarado, the WFP Regional IT Officer for Latin America and the Caribbean. In the aftermath of Hurricane Matthew, Gaby lead the IT Working Group in Haiti, which provided support to the humanitarian response through the provision of
ETC Connectivity Services. The team from the Regional Bureau worked with mVAM and Flowminder to supply valuable time-bound information to the operation.

 

Supporting Emergencies through Technology & Joint Efforts

It’s now been just over a month since Hurricane Matthew made landfall in Haiti, devastating the western side of the country. The hurricane has affected an estimated 2.1 million people, leaving 1.4 million in need of humanitarian assistance.

In the days following the hurricane, a rapid food security assessment was carried out to determine the impact of the hurricane on the food security of households and communities in the affected areas.  In the most-affected areas, the départements of Grande-Anse and Sud, people reported that crops and livestock, as well as agricultural and fishing equipment, were almost entirely destroyed.

 

Credit: WFP

Credit: WFP


We all know the challenges we face at WFP when looking to collect information, in order to determine what would be the best response under the circumstances on the ground.  In the aftermath of the hurricane, which had destroyed infrastructure, caused flooding, and temporarily knocked out telecommunications, gathering information from affected areas was especially difficult. So, WFP’s Information Technology team in the Regional Bureau for Latin America and the Caribbean reached out to Flowminder, a non-profit organization that uses big data analysis to answer questions that would be operationally relevant for government and aid agencies trying to respond to emergencies. Thanks to an existing agreement between WFP and Flowminder, WFP was able to quickly establish a working group and start data collection one day after the hurricane struck Haiti.

 

An aerial view of Jérémie following the passage of Hurricane Matthew (photo: Logan Abassi - UN/MINUSTAH)

An aerial view of Jérémie following the passage of Hurricane Matthew
(photo: Logan Abassi – UN/MINUSTAH)

Flowminder aggregates, integrates and analyses anonymous mobile operator data (call detail records), satellite and household survey data, which helps to estimate population displacements following a crisis. Displaced people are some of the most vulnerable following a hurricane, and knowing where people have gone helps to provide more effective assistance.

By 24 October 2016, Flowminder estimated that 260,500 people had been displaced within the Grande Anse, Sud, and Nippes départements. In Les Cayes, the major city in Sud, the population grew by an estimated 42% in the aftermath of Hurricane Matthew according to Flowminder analysis. In fact, Flowminder analyses suggest that many people moved toward cities, even Jérémie and Les Cayes, which were severely damaged by the hurricane.

 

Flowminder.org

Flowminder.org

So how exactly did Flowminder make these estimates with so many areas barely accessible? By analysing anonymized call detail records from Digicel, one of Haiti’s major cell phone network providers, and comparing where people placed calls before and after the hurricane, Flowminder was able provide an estimate of the number of displaced people. Flowminder uses algorithms that look at where the last “transaction” (phone call or sms) took place each day in order to identify the place where people were living before the hurricane and then subsequently moved afterwards. . It makes sense – the last few calls or texts you make at night are often from your home. While Flowminder does not get exact locations from the call data records, they can identify a general home location using the closest cell phone tower. After identifying the home location, Flowminder needs to determine how many people each phone represents. In poorer areas, not everyone may own a phone, or many people may not be able to charge and use their phones after a natural disaster like a hurricane. Flowminder uses formulas which takes these factors into account, and translates the number of phones into an estimate of the number of people who are displaced.

How will this further help?

With the information provided by Flowminder, WFP is able to estimate:

  • possible gaps in assistance in areas of the country which were not damaged by Hurricane Matthew, but which are experiencing an influx of people in need of food assistance following the hurricane;
  • use and community ‘acceptance’ of the use of mobile money (one aspect is the availability of the service, while the other aspect is if it is being used in that area);
  • the prevalence and spread of diseases (including Cholera, which continues to pose a risk in the aftermath of the hurricane).

It has been a very challenging yet incredible opportunity to see where and how technology can be used to further support an emergency response under difficult conditions and to ensure that WFP can reach the most vulnerable after a disaster.

Welcome to Vamistan

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WFP/mVAM

Since we started in 2012, mVAM has worked to share our knowledge with others — this was encouraged by the Humanitarian Innovation Fund, our first donor, and this blog is part of that commitment to sharing and documenting what we do. In the last few years, we have set up a learning lab to scale up capacities in data collection and analysis but also to share data and learning with the broader community. In this regard, we’ve seen others inside WFP and other organizations reuse the methodologies and lessons learned we have been sharing on our resource center.  But we wanted to go even further in our information sharing. In this spirit, we’re now working to set up a fully fledged online course how to implement a remote mobile data collection system. We want to allow anyone anywhere to learn about using remote mobile data collection for food security monitoring and then use it in their own work.

So, what can you or your colleague, or a friend who has never heard of mVAM before, learn by taking the course? The course walks you through the overall life cycle of remote food security data collection and covers specific issues such as designing a questionnaire or an appropriate sample frame. By the end of the course you’ll be familiar with remote data collection approaches and tools. As well as understanding where and when it is appropriate to use these tools you’ll be able to design and implement short remote mobile-based surveys using SMS, voice and IVR technologies. You’ll even get your own certificate once you’ve completed the course. Pretty exciting stuff, we know.

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Our fictional country Vamistan

The course is completely free. We’ve done our best to mix it up, using videos and presentations and online resources to share our knowledge and make the course as interactive as possible. We’ve even invented a fictional case study country ‘Vamistan’ that participants can follow to really reflect on how they can harness mobile technology.

We’ve partnered before with Leiden University and we have been lucky enough to have the support of their Online Learning Lab when designing the course. In August we had a visit from one of their online learning experts, an Instructional Designer who gave us tips on course design and didactics. This week one of Leiden’s ‘video experts’ came to Rome to film those members of the team who appeared in the videos.

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WFP/Alice Clough

The lab has a lot of experience in this field, their courses are reaching over 480,000 participants in 196 countries. So we’ve been working with leaders in the field to deliver a high quality online course. We’ve been having fun making it and we’re looking forward to sharing the finished product. It should be up and running in the near future so stay tuned!  We’ll tell you exactly how to sign up and share the news. We hope you’ll check it out!

Prince Charming: A Triplex Tale

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Welcome to “Sorland”! (Photo: WFP/Jennifer Browning)

The mVAM team sent a team member, Jen, to Triplex, the largest humanitarian emergency simulation in the world. mVAM was thrilled to join over 400 military, UN, government and NGO participants who travelled to Lista, Norway, for training in how to respond to a humanitarian emergency. In the pre-exercise stage, we presented our work on mVAM, and we hope that our participation will help to increase our engagement with such a diverse group of partners. There were also interesting presentations on shelter, supply chain, data analysis, and new tools. 

Our favorite session was on smart assessments. Lars Peter Nissen, Director of ACAPS, offered important wisdom that we should always strive to follow with mVAM. He warned against getting trapped in your own small study and losing what he termed “situational awareness,” or the bigger picture.

His three rules for humanitarian analysts to live by:

  1. “Know what you need to know.”
  2. “Make sense, not data.”
  3. “Don’t be precisely wrong, be approximately right.”

In thinking about how we can apply these three gems to our work on remote data collection, we need to make a constant effort to collect data that will really help improve humanitarian responses. Like all data nerds, we can sometimes get bogged down in calculating exact bias estimates or making sample size calculations, risking losing sight of the bigger picture from down in the weeds of our small mVAM survey in one country. But we need to remember to look at the wider situation to ensure we are collecting useful information.

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Presenting mVAM (Photo: WFP/Lucy Styles)

Then we need to make sense of our data by triangulating with what others are doing and what we already know. In our mVAM bulletins, we need to communicate clearly in a way that makes data quickly understandable to decision-makers. We need to pay attention to what the trends from our mVAM data are telling us, while not forgetting the limitations of the remote mobile data collection methodology.

After a couple days of introspection, or as we would find out later, the calm before the storm, the two-day pre-exercise ended and we embarked on the natural disaster simulation phase. We boarded buses or “flights” and travelled to Base Camp in “Sorland”, a fictional developing country that had just been hit by a hurricane and where the simulation would take place.  For the next 72 hours we would do our best to respond, learning along the way.  

The organizers made a herculean effort to have the 72 hours be as realistic as possible. We were sleeping in (admittedly high tech) tents and crossing a road jammed with huge supply trucks and lines of land rovers. The scale was impressive. Prince Harry even flew a helicopter in to observe the exercise and play the role of a Minister from the Sorland government. The organizers couldn’t have planned it, but at one point, the winds became dangerously high, almost making it necessary to really evacuate us.

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The Minister of “Sorland” played by Prince Harry (Photo: WFP/Jennifer Browning)

In these conditions as in any real life emergency, it was inevitable that we would run into problems. We had planned to deploy mVAM quickly. The organizers had provided us with a list of phone numbers of IDPs in “Sorland,” actually students from the United Nations University in Bonn who did a great job role playing throughout the simulation. We wanted to contact them via SMS, using Pollit, the in-house SMS survey tool developed by InStedd. We have used Pollit successfully in Goma to collect food prices, but for Pollit to work, you need a WiFi connection. (For more on Pollit, see our blog entries Pollit Customized and Ready to Go and Working with DRC Youth to Text Back Market Prices).  At Triplex,  WiFi was supposed to be up and running the first evening, but conditions on the ground made it difficult to establish a connection. We didn’t get WiFi until the last night of the exercise, which was too late for us to use Pollit.

So instead, we participated in OCHA-led face-to-face surveys and in focus group discussions. Sometimes we get so caught up in remote data collection that these other data collection exercises can fall off our radar screen, but there is so much we learn from talking to local communities face-to-face and from coordinating with other partner agencies as they plan their own data collection. So perhaps because WiFi was such a problem, Triplex turned into a great experience to keep our coordination and face-to-face data collection skills sharp.

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The Logistics Cluster explains access constraints (Photo: WFP/Ricardo Gonzalez)

In addition to collaborating with different organizations, working within a diverse team of WFP colleagues from different units pushed us to consult closely and understand what information they needed most. At WFP headquarters, we don’t generally have the same opportunity to work this closely on a daily basis with colleagues from other branches like logistics, procurement, and cash-based transfers. As WFP considered a potential cash-based transfer response for the fictional Sorland, it became clear that operationally, information on market functioning and food availability was very important. This meant that  while we were not able to use existing mVAM tools per se, we recognized clear demand within WFP to address this critical information gap. For next time, we will keep these information needs, i.e. “knowing what we need to know,” clearly in mind. And we’ll also make sure to prepare for all types of scenarios, think about the limitations of our technology, and do our best to have a Plan B.

Even without WiFi and Pollit, the Triplex simulation ended up being very relevant and provided a great brainstorming session for what came later. During the 72 hour simulation, colleagues from Haiti and Cuba were receiving increasingly grim alerts about the approach of Hurricane Matthew. Through Triplex, we’d already identified some of the information that could be most relevant in responding to a hurricane. So our practice in Sorland turned out to be very useful in quickly deciding what questions to ask in Haiti where we are rolling out a remote market assessment. Stay tuned for more!