2017 Highlights

It’s been a busy year for us here at mVAM, but some things stood out among all the rest. Here, we take you through some of our highlights from 2017:

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Staff from several countries take part in an mVAM workshop in Kigali, August 2017

1: mVAM for everyone! Our free and open online course

After four years of testing, designing and deploying remote data collection projects, we partnered with Leiden University to develop an online course to share what we’ve learned so far. Our Remote Food Security Monitoring online course was launched in May, and aims to provide a clear understanding of what remote food security monitoring entails, when it is a useful tool, and how to implement a remote food security monitoring project. The course is free and self-paced, and open to anyone who is interested in setting up a remote data collection project.

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WFP/Jean-Martin Bauer

2: Expanding across Asia and the Pacific

During 2017, we kept growing, scaling up in the Asia/Pacific region. WFP’s Nepal and Sri Lanka country offices collaborated with their respective national government partners to launch  mobile-based food security monitoring systems. Nepal’s mNekSAP was the first to use an innovative dual-mode approach to collect data from a panel of households previously surveyed during a baseline assessment, combining remote mobile data collection with traditional face-to-face methods so as to not miss out on following up with those households without a phone. This means that the data gathered through mNEKSAP is not only representative (ensuring coverage of non-phone owners), but through re-interviewing the same individuals, it also provides us with a rare panel data set, which is optimum.      

Afghanistan, Myanmar and Papua New Guinea kept busy with ongoing mobile data collection. Afghanistan now uses mVAM to conduct several different types of surveys, from conflict rapid assessments, to market monitoring, to post-distribution monitoring. Most recently, they launched their first round of nutrition data collection for the Minimum Dietary Diversity for Women (MDD-W) indicator – stay tuned for results!

Meanwhile in PNG, their 4th nationwide survey introduced the Food Insecurity Experience Scale – an official SDG 2.1.2 indicator. Our hope is that we can use mVAM to help measure progress in this area.  Also in the region, we’ve been looking at ways to use the PRISM system to better visualize mVAM data and link it to other information sources. More on that in 2018!

WFP/Maria Muraskiewicz

WFP/Maria Muraskiewicz

3: Keeping up with remote nutrition data collection

We’re also expanding in terms of the type of data we use mVAM to collect. Following the success of last year’s remote nutrition data collection pilot in Kenya, we’ve moved on testing whether this is also feasible in Malawi and Niger, and which technologies we can use to collect the data.

From October 2016 to April 2017, we worked with GeoPoll in Malawi to develop a tool and methodology for collecting MDD-W data using SMS surveys. We conducted five rounds of surveys, during which we constantly adapted the indicator to make sure it was suitable for SMS surveys. We learned that the design of the questions was especially important – simple questions, a mix of open-ended and list-based questions, and the option to take the survey in the respondent’s preferred language proved particularly helpful.

In Niger, we tested the feasibility of using CATI to collect MDD-W data in IDP camps in the conflict-affected Diffa region. Through focus groups and in-depth interviews, it became evident that despite low phone ownership rates among women, most women do have access to phones through sharing with household members or neighbours. Men had little hesitation to women in their families being called when they were informed in advance, when female operators were used, and when the operators identified themselves as calling from WFP.. We’re now analysing the data we collected through both F2F and CATI, in order to understand potential mode effects and selection bias.

(For a full overview of our nutrition work, check out Episode 12 of VAM Talks!)

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4: Responsible data (collection, storage, sharing and distribution!)

Mobile data projects come with their own particular set of risks and challenges with regards to data privacy and protection. In a time when reports of data breaches seem to occur more and more frequently, what steps should we take to ensure that we aren’t accidentally putting the very people we are trying to assist at risk? Working with the International Data Responsibility Group (IDRG) and Leiden University’s Centre for Innovation, we developed a field book for Conducting Mobile Surveys Responsibly, which outlines the main risks of mobile data collection and provides guidelines for responsible data collection, storage, processing and distribution in complex humanitarian contexts. In December, we brought together experts on three different continents for a webinar on Responsible Mobile Data Collection, in which they discussed the challenges of remote data collection projects and shared best practices, tools, and tips for adhering to privacy and protection guidelines – from the field level to the WFP context and across the broader humanitarian and development sphere.  

Testing the chatbot in Nigeria

WFP/Seokjin Han

5: Communicating both ways: WFP speaks to …

As mobile technology continued to develop, we looked at ways to use new tools to allow the people we serve to start conversations with us about their own food security situations. In addition to getting information that we can use to improve the design of food assistance programmes, we want to ensure that the line is open so that people in the communities we serve can contact us and access information that is useful to them. In 2017, we continued the development of our two 2-way communications tools – a food security chatbot, and Free Basics, a platform which allows people to access certain sites on the internet at no data cost.

The start of the year saw us in New York where one of our partners, Nielsen, organized a hackathon to design a chatbot that could help collect information during a humanitarian response. Over the course of the year, we worked on developing use cases in different contexts – in Haiti , Nigeria and Kenya – and are now developing a chatbot builder with another partner of ours, InSTEDD. We look forward to deploying the bot in the new year.

Simultaneously, we expanded Free Basics after successfully piloting it in Malawi in November 2016; sites will soon go live in Rwanda, DRC and Niger. Back in Malawi, the original site, which started out as a free website to share weekly staple food prices, is now shifting its focus to address the needs of the more than 30,000 refugees and asylum seekers hosted in the country. The majority of the group lives in two camps where WFP provides food assistance in the form of monthly in-kind distributions and cash-based transfers. As their ability to move outside of the camps where they currently live is quite limited, having information not only about food prices in their immediate area but also food stocks is incredibly helpful.

Thank you to our partners and donors, without whose support none of this would be possible, and to you – our readers – for following along! See you in 2018!

Designing a new communication channel – the Food Bot

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WFP/Lucia Casarin

After missions to several field locations (including Nigeria, Haiti, and Kenya) aimed at assessing the feasibility of deploying chatbots in WFP’s operational contexts, the mVAM team concluded that they offer great potential for both the sharing and receiving of useful information on food security.  It is now time to take a step forward and actually build a chatbot for WFP – the Food Bot!

In case you haven’t been tracking our work on chatbots (about which you can learn more here and here), here’s a quick refresher. A chatbot is a computer program designed to simulate conversation with human users over the Internet; imagine an invisible robot living inside the Internet asking you questions.

Tailoring the chatbot to its users

The first step needed in designing a new tool is to garner a strong understanding of its users – who will be using the chatbot and for what purposes?

In our case, we are working simultaneously on two levels:

  1. Chatbot builder tool: this is an interface where WFP staff will be able to design, deploy, and manage customized chatbots. The primary users of the chatbot builder tool will be WFP staff in the field, who will use the platform to design contextually-appropriate chatbots for their location. As you can imagine, each WFP Country Office envisions using the chatbot for a specific purpose. In Kenya, for example, colleagues are eager to deploy a chatbot to share updated information about WFP food and cash distributions as well as other programmatic details. In Nigeria, on the other hand, staff want to share details on how to use nutritional supplements provided by WFP.
  2. Contents within the chatbot: this refers to the information the chatbot provides and the dialogues between the chatbot and its users. Targeted users for the chatbot are people living in marginalized and food insecure communities who can use the chatbot to receive information from WFP. They can also ask us questions about WFP’s programmes in their area and provide their feedback and complaints. WFP will develop different chatbots for different locations and target populations.
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WFP/Lucia Casarin

To get to know our users better and start defining the design of the Food Bot, WFP and our technical partner InSTEDD (who has extensive experience designing innovative mobile tools) travelled once again to the Kakuma Refugee Camp, located in Western Kenya, where we spent a few days collaborating with WFP staff and refugees to understand how to create a user-friendly chatbot to meet their needs.

We first worked with a small group of refugees to better understand how they use the chatbot technology. To do so, we employed a popular prototype technique called ‘Wizard of Oz’. Under our supervision and guidance, refugees were asked to visit a Facebook page and start a conversation with what they believed was a WFP chatbot. Instead, they were actually chatting with our colleague. Through this type of human-centered approach, we were able to quickly learn what types of information the Kakuma refugees were interested in receiving as well as how they were asking questions. During the field test, we also confirmed our hypothesis that chatbot conversations need to be as light as possible (not using many pictures, menus, or emoticons) in order to minimize data charges and make conversations possible when network coverage is weak or the user is employing Messenger Lite.

We then spent some time with our WFP colleagues in the Kakuma and Nairobi Offices brainstorming the ways in which the chatbot could complement existing activities and provide useful information for our work.

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WFP/Lucia Casarin

An iterative design approach

We are now dedicating the next few months to developing the chatbot builder and refining the chatbot contents for a larger pilot project in Kenya. Building a new platform will require a lot of trial and error, and we know that we’ll not get everything right on the first try. For this reason, we have now begun an interactive, iterative design approach, meaning that we will carry out multiple field tests along the way to further refine our product. This will allow us to collect valuable feedback from users at each stage of development so that we can mitigate potential issues early on.

Stay tuned during the coming months as we share additional information on the development of our very first Food Bot!

New places, new tools: what’s up next for mVAM?

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We’ve just got back from Rwanda where we were holding a workshop on using mVAM to expand real-time food security and nutrition monitoring with Internally Displaced Persons (IDPs) and refugee populations. The project, which is made possible by the support of the Korean International Cooperation Agency (KOICA), will be implemented in ten countries in sub-Saharan Africa where WFP works.

What’s the project?

The KOICA project has two aims. First, it aims to empower information exchange with marginalized populations, specifically IDPs and Refugees. Secondly, it supports the collection of food security and nutrition data using the latest mobile and satellite technologies. This will happen in ten countries in Sub-Saharan Africa: the Central African Republic (CAR),The Democratic Republic of Congo (DRC), Kenya, Malawi, Niger, Nigeria, Rwanda, Somalia, South Sudan and Uganda.

How are we going to do this?

As you know, two-way communication systems are an important part of our work. As well as getting information that we can use to inform WFP programmes, we want to ensure that the line is open so that people in the communities we serve can contact us and access information that is useful to them. We’ve already been using Interactive Voice Response and live calls to share information with affected populations, and are now expanding our toolbox to include new technologies: Free Basics and a chatbot.

Remote data collection isn’t just done by mobile phones – VAM already uses other sources, such as  satellite imagery analysis – to understand the food security situation on the ground.  Under this project, we’ll also help countries incorporate similar analysis which will complement two-way communication systems to provide a fuller picture of the food security situation.

Finally, we’re going to harness our knowledge of Call Detail Records analysis: de-identified metadata collected via cell phone towers about the number of calls or messages people are sending and which towers they are using. We have already used this technique in Haiti to track displacement after Hurricane Matthew, and we’re really excited to transfer these ideas to another context to ensure we get up-to-date information on where affected communities are so we can better target food assistance in the right locations.

What happened at the workshop?

Representatives from all 10 country offices, three regional bureaus and staff from HQ came together to discuss the three main project components. During the workshop, the different country offices had the chance to learn more from members of the mVAM team about the specific tools they can harness and ensure their collected data is high quality, standardised and communicated effectively. However, the best part about bringing everyone together was that country teams could share their experiences about how they are already using mVAM tools. We heard from the Malawi country office about their Free Basics pilot, and Niger and Nigeria explained how they’re implementing IVR so affected communities can easily contact WFP, even after work hours. Sharing their different experiences and learning about how different tools have worked in each context not only gave everyone an overview of what mVAM is doing so far, it also helped everyone understand the implementation challenges and how to overcome them.

What’s next for the KOICA project?

We’re really excited for the next stage of the project. Each country office has now planned what tools they’re going to use to increase their communications with affected communities and how they will improve their existing data collection systems. It’s going to be great to see the impact these tools will have not only on WFP’s response, but also how they will empower the communities we’re serving. 

mVAM for nutrition: findings from Kenya

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Photo: WFP/Kusum Hachhethu

We’ve used mVAM to collect data on a range of things that impact food security – so what about information on nutrition? Back in October, we went to Kenya to conduct a study on whether we could use remote mobile data collection to gather information on women and children’s nutrition.

The summary of our findings from the case study are now available in a new report from mVAM and our partners in the study, WFP’s Nutrition Division and the World Agroforestry Centre (ICRAF).

Read more:

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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.