Postcard from Dakar

mVAM workshop participants all smiles after learning more about IVR WFP/Lucia Casarn

mVAM workshop participants all smiles after learning more about IVR WFP/Lucia Casarn

During the last week of June, staff from WFP HQ’s mVAM team, the West and Central Africa Regional Bureau, and Nigeria and Niger Country Offices met in beautiful Dakar to work together on Interactive Voice Response (IVR) systems for two-way communication. (If you want to dig deep into all details IVR-related, check out the lesson in our mVAM online course!)

We’ve previously blogged about how WFP is responding to the needs of people who have been displaced due to Boko Haram insurgencies in both Nigeria and Niger. When we implemented these operations we also put communication channels in place so beneficiaries are able to contact WFP. In Nigeria, the Maiduguri Field Office created a hotline. Their operators receive an average of 100 calls per day from beneficiaries asking food security-related questions and providing feedback on the operations. The problem is the hotline is only available during working hours and has a limited number of people who can call in at the same time. To work around this they’re therefore looking at how an IVR system can support the call operators who are dealing with high volumes and better manage calls that take place outside of normal office hours. WFP Niger wants to set up a similar hotline system but without full time phone operators. Beneficiaries will call in to an automated IVR system and their queries and feedback recorded by the system and followed up by the Country Office. 

A Nigeria IT Officer working to install a GSM gateway for IVR usage in Maiduguri WFP/Lucia Casarin

A Nigeria IT Officer working to install a GSM gateway for IVR usage in Maiduguri WFP/Lucia Casarin

During the workshop participants were trained by InSTEDD on how to physically deploy IVR using a GSM gateway (a fancy tool that automatically places phone calls) and Verboice, the free open source software they’ve developed to manage these systems. The team also discussed the nitty gritty technical aspects of the system, including creating and modifying call flows (the sequencing of questions), scheduling calls and downloading collected call logs and recordings. Most importantly, participants had the opportunity to share their experiences and challenges with experts in this field and discuss best practices, alternative deployments and technical solutions.

The Country Office staff have now returned to Niger and Nigeria and they’ve already started testing the use of the IVR machines. We’re eager to begin logging data and hearing more from our beneficiaries. So stay tuned!

 

 

 

Mind the mode …. and the non-response

How voice and face-to-face survey data compares in Mali

This is the most recent entry in our ‘Mind the Mode’ series on the mVAM blog. We are constantly assessing our data collection modalities to better understand what produces the most-accurate results and what biases may be present. One of our recent experiments took us to Mali, where we were comparing the food consumption score between face-to-face (F2F) interviews versus mVAM live calls.

It’s all in the details
To do this, in February and March, the WFP team first conducted a baseline assessment in four regions of the country. As part of the baseline, we collected phone numbers from participants. Approximately 7-10 days later, we then re-contacted those households who had phones, reaching roughly half of those encountered during the face-to-face survey. We weren’t able to contact the other households. To ensure the validity of the results, we made sure the questionnaire was the exact same between the F2F and telephone interviews. Any differences in wording or changes in the way in which the questions were asked could adversely affect our analysis.

The findings from our analysis were quite interesting. We found that food consumption scores (FCS) collected via the mVAM survey tended to be slightly higher than those collected via the face-to-face survey. The graph below illustrates this shift to higher scores between the two rounds. Higher FCS via mVAM versus F2F surveys is not atypical to Mali. We’ve observed similar outcomes in South Sudan and other countries where mVAM studies have taken place.

mali dist

 

Why could this be? There are two main reasons that could explain this difference. Either it might be due to the data collection modality (i.e., people report higher food consumption scores on the phone)? Or, a perhaps a selection bias is occurring? Remember that we were only able to contact roughly half of the participants from the F2F survey during the telephone calls. So, it’s possible that people who responded to the phone calls are less food insecure, which could make sense, since we often see that the poorest of the poor either don’t own a phone or have limited economic means to charge their phone or purchase phone credit.

To test these hypotheses, we dug a bit deeper.

Same same…
Are people telling the same story on the phone versus face-to-face? Based on our results, the answer is yes! If we compare the same pool of respondents who participated in both the F2F and telephone survey rounds, their food security indicators are more or less the same. For example, the mean mVAM FCS was 56.21 while the mean F2F FCS was 55.65, with no statistically significant difference between the two.

But different…
So what about selection bias? In the F2F round, there are essentially three groups of people: 1) those who own phones and participated in both the F2F and mVAM survey; 2) people who own phones but didn’t participate in the mVAM survey, because they either didn’t answer the calls or their phone was off; and 3) people who do not own a phone and thus couldn’t participate in the mVAM survey.

People who replied to the mVAM survey have overall higher FCS than those that we were unable to contact. What we learned from this experiment is that bias does not only come from the households that do not own a phone but also from non-respondents (those households who shared their phone number and gave consent but then were not reachable later on for the phone interview). Possible reasons why they were not reachable could be that they have less access to electricity to charge their phone or that they live in areas with bad network coverage. The graph below illustrates the distribution by respondent type and their respective FCS.

mali boxp

When you compare the demographics of people in these three groups based on the data collected in the baseline, you can see that there are significant differences, as per the example below. Notice that the education levels of respondents varies amongst the three groups—those without a phone tend to be less educated than those who own a phone and participated in the mVAM survey.

mali profile

This study taught us a valuable lesson. While we are confident that there is no statistically significant difference between face-to-face and phone responses within the Mali context, there is a selection bias in mVAM-collected data. By not including those without phones as well as those who did not respond, we are missing an important (and likely poorer) subset of the population, meaning that the reported FCS is likely higher than it may be if these groups were included. One way to account for this bias is to ensure that telephone operators attempt to contact the households numerous times, over the course of several days. It’s important that they really try to reach them. The team is also studying how to account for this bias in our data analyses.

Trial and Error: How we found a way to monitor nutrition through SMS in Malawi

WFP/Alice Clough

WFP/Alice Clough

Over the last ten months we have been testing if we can use mobile phones to collect nutrition indicators. One of these experiments involved using SMS to ask questions about women’s diet quality via the Minimum Dietary Diversity – Women (MDD-W) indicator.  The MDD-W involves asking simple questions about whether women of reproductive age (15-49 years) consumed at least five out of ten defined food groups. We were interested in using SMS surveys to measure MDD-W, because SMS offers an opportunity to collect data regularly at scale and at low cost.

From October 2016 to April 2017, we worked with GeoPoll to conduct five survey rounds on MDD-W and find a way to adapt the indicator to SMS. We analysed data from each round, identified gaps and refined the survey instrument. We were able to collect data quickly and identify strengths and weaknesses to make revisions through an iterative process. Through this process, we believe that we have successfully designed an instrument that can be used to monitor MDD-W trends by SMS. Here’s a short summary of what we learned:

1. Using a mix of open-ended and list-based questions helped people better understand our questions.

By using a mix of open-ended and list-based questions, we were able to significantly improve data quality. MDD-W round 1In the first few rounds, we had an unusually high number of respondents who either scored “0” or “10” on the MDD-W score, which are both unlikely under normal circumstances. A score of “0” means that the respondent did not consume food items from any of the 10 core food groups the previous day or night, while a score of “10” means that the respondent consumed food items from all food groups. In the first round, scores of “0” or “10” accounted for 29 percent of all MDD-Wrespondents, but by Round 5 these scores represented only 3 percent of responses. It seems that having respondents reflect about what they ate in the open-ended questions we introduced in later rounds helps them  recall the food items they consumed and answer the subsequent list-based questions more accurately.

2. Keep questions simple.

We originally asked people by SMS whether they ate food items from the core food groups that comprise the MDD-W score. For example, “Yesterday, did you eat any Vitamin A-rich fruits and vegetables such as mangos, carrots, pumpkin, …….” Perhaps respondents thought that they needed to consume food items from both the fruit and vegetable groups in order to reply “yes” to this question. So instead, we split that question into two separate questions (one on Vitamin A-rich fruits and the other on Vitamin A-rich vegetables) to make it easier for the respondent to answer. We did the same for some of the other questions and found a very low percentage of women scoring “0” or “10” on the MDD-W score. Of course there is a trade-off here, and splitting too many questions might lead to a long and unwieldy questionnaire that could frustrate respondents.

3. Let respondents take the survey in their preferred language.

Comprehension remains a challenge in automated surveys, so helping respondents by asking questions in their own language will ensure data quality and limit non-response. In the Malawi study, translating food items into the local language (Chichewa), while keeping the rest of the questionnaire in English, improved comprehension. We recommend providing the respondent with the option to take the survey in their preferred language.

4. Pre-stratify and pre-target to ensure representativeness.

SMS surveys tend to be biased towards people who have mobile phones; we reach a lot of younger, urban men, and relatively few women of reproductive age, our target group for surveys on women’s diet. To ensure we are reaching them, an MDD-W SMS survey should be designed or ‘pre-stratified’ to include a diverse group of respondents. In Malawi, we were able to pre-stratify according to variables that included age, level of education, location and wealth. This allowed us to include women from all walks of life.

5. Post-calibrate to produce estimates that are more comparable to face-to-face surveys.

The MDD-W SMS surveys we conducted produced higher point estimates than those we would expect in face-to-face surveys. This suggests we may wish to consider calibration to adjust for sampling bias, the likely cause for the discrepancy. Calibration is the process of maintaining instrument accuracy by minimizing factors that cause inaccurate measurements. We’re still working on this and hope to find a solution soon. In the meantime, we think we are able to track trends in MDD-W by SMS with some reliability.

 

If you’re not human then who are you?

Experimenting with chatbots in Nigeria and Haiti

WFP/Lucia Casarin

Testing the bot in Haiti – WFP/Lucia Casarin

Readers of this blog know that the team has been experimenting with chatbots to communicate with disaster-affected communities – read our previous posts about our prototype and the Nielsen Hackathon.

As part of this effort, during recent missions to Haiti and Nigeria, our team went out to talk to communities to find out whether a chatbot would be right for them.

Would a chatbot be a stretch in these communities?

Well it’s not that much of a stretch.

In North East Nigeria, most displaced people live in Maiduguri, a city of over 1 million people. In this ‘urban’ setting connectivity is good, most people own cell phones and many young people use social media and messaging apps. Mobile operators have been offering services that allow people to access the internet by selling ‘social bundles’ (unlimited social media access sold in very small increments) and offer some services for free, including Facebook Light and Facebook Messenger.

In Haiti, three-quarters of the population live in the capital, Port-au-Prince, where 3G connectivity is good and most people use messaging apps to communicate with friends and family. Even in rural and difficult-to-reach communities, leaders and young people own smartphones and connect to the internet. There is a lot of competition between mobile operators so the prices for mobile data are very low. This means that most people can afford to access the internet either via their own smartphone or from shared smartphones.

A

Mobile phones charging station on the road from Léogane Peri to Port-au-Prince WFP/Lucia Casarin

A bare-bones demo

In both countries we tested a simple chatbot that asks people about food prices and what the food security is like in their community. The survey we used was much more basic than our usual mobile questionnaires as we felt it was important to keep things simple at this stage.

For Nigeria, the bot demo was initially in English but we soon translated it into Hausa, the primary language spoken by displaced persons in Maiduguri. In Haiti we made it available both in Creole and French. The chatbot was very responsive on 3G and it even worked with slower 2G connections so the technology works in these contexts. But this was only the starting point, what we really wanted to know was what ‘real’ people thought about the bot.

We organized focus group discussions with displaced people in Maiduguri and with community representatives in Haiti. We helped people access the WFP bot via their Facebook accounts, and they began chatting away.

Sounds cool, but what are the limitations?

Here’s what people said:

First of all, people thought the bot is a convenient, quick, and easy way to get in touch directly with WFP and they really liked that the bot allows them to speak to WFP without intermediaries. They had lot to tell us particularly through the open-ended question where they typed out detailed responses.

In Nigeria, they did tell us that our (somewhat wordy) English-language demo should be translated into Hausa because it would make it easier for everyone to use. Our first group of testers were young people who were already Facebook users and so were familiar with Messenger. It was therefore no surprise that they were interacting smoothly with the bot and able to go through our questionnaire in minutes.

WFP/Jean-Martin Bauer

Testing the bot in Nigeria – WFP/Jean-Martin Bauer

In Haiti, people started interacting with the bot as if it was a human rather than an automated questionnaire so they got stuck pretty fast when it wasn’t as naturally responsive as they’d expected. This means that either we give clearer instructions to people or we add Natural Language Processing capabilities to our bot.

There are of course other barriers. In both countries women appeared to be less likely to own a smartphone. This means that bot users will likely be overwhelmingly young, male and better educated than other people – hardly ‘representative’ of WFP’s target affected population. The free version of the bot is also not always available: in Nigeria only Airtel subscribers can access it, while in Haiti the free service doesn’t exist yet.

This means that the bot would need to be a complement to the other tools we have. We might use data from the bot to obtain a quick situation update, but we will continue relying on other sources for more representative data.

mVAM for nutrition: findings from Kenya

2WFP-Kusum_Hachhethu

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:

kenya-report

What we found at the market: using Free Basics in Malawi

FreeBasicsAd_Chichewa

We wrote to you back in November about one of our new innovations – our Free Basics website ‘Za Pamsika’ where we’re posting commodity prices using the weekly price data we’re collecting through our mVAM operators on a free website. We said that the project had the potential to reach millions of Malawians – well, a lot has happened since then.

Rather than continuing to willfully upload prices while watching our user statistics go up and down, we went to Malawi to carry out a short ground truth study and get some first hand user feedback.  The aim of the mission was to investigate the best way of using the website and interrogate the assumptions we’d made when designing it.

With this in mind, we tried to answer two big questions:

  1. Who can access our website – what are the potential barriers and how can we work around them?
  2. Do Malawians really want a website where they can find out maize and beans prices?

So we went to rural and urban markets in the Central and Southern regions to speak to the mVAM traders and the consumers in their markets about their mobile phone usage and market activity and to get their feedback on the website.

What kind of answers did we get?

First – access issues. While you don’t need a smartphone to access the website we knew that mobile penetration in Malawi is low. So we were most worried about the prevalence of internet-enabled phones and network coverage. From our study we found out that while we aren’t going to be able to reach everyone in Malawi via a website, we can still communicate with people. Network coverage was a problem in some areas. However, overall we found that most of the traders had internet enabled phones or wanted to buy one. We also found that Malawi’s MNOs have been recently trying to push out better network coverage. All good news for future reach of the website.

Actually the biggest barrier was language and literacy. While English is the national language of Malawi, most of the literate people we spoke to were much more comfortable reading and writing in Chichewa because that’s what they were taught in. While they were very enthusiastic about the website content when it was explained to them, they found the initial design (all in English and text heavy) confusing and difficult to use. Luckily this is an easy change to make so we did a quick redesign of the website and translated it into Chichewa:

malawiblog1

With our new design we headed back into the markets and got much better feedback. Rather than just saying that they liked the website content they could really interact with it and were making comments on the different maize and beans prices.

The second barrier we found was digital literacy. Many of the people we spoke to had internet-enabled phones but either didn’t know how to use them or didn’t even realise that they had the internet on them! Unlike the language change this is not a quick fix. This was particularly prominent amongst the women we spoke to, none of whom were comfortable with mobile internet. We’re therefore going to partner with civil society organisations promoting digital literacy. WFP has a network of partners and farmers on the ground who they reach out to with climate information so we’re going to try and use these focal points to communicate our prices with vulnerable populations and communities who have limited access to information.

IMG_1205

But do Malawians really want a ‘Za Pamsika’ website?

It turned out that maize and beans prices really are something that people want to see on the website. The recent drought was on everyone’s minds and they were really emphasising how much of a difference getting a good price could make. People were also already using their phones to get prices – by calling their friends or other traders in different areas and were quite enthusiastic about the possibility of getting this information for free.

With these learnings in mind and feeling confident with our website redesign and excited to be working closely with the country office, we embarked on our next steps. We now have a new focal point in the Lilongwe office who’s looking after the project and in a much better placed position than us in Rome to reach out to millions of Malawians. By this point over 25,000 people had already visited the Za Pamsika website but we knew our reach could be much further. We therefore started experimenting with ways of advertising the website.

malawiblog2

First – we decided to take out a Facebook ad to try and raise the site profile so we created our own ‘Za Pamsika’ page on Facebook and put out some ads in English and Chichewa. We were pretty excited when they started showing up on Malawian colleagues’ Facebook newsfeeds and within 10 days we’d reached more than 130,000 people and got 650 likes to our Facebook page.

What we didn’t expect was the organic reaction we’d get to our page. Within 3 days we’d not only reached more than 80,000 people with our post, we’d also seen that people started having conversations about maize prices on our advert.  People have also started messaging us about whether we can add their market to our website. We’re also getting comments about what other commodities we should add, for instance more seasonal foods such as groundnuts or soya. Most excitingly we even had someone knock on the door of the sub-office to inquire about the website after seeing our advert!

On a second mission in April we went out to the markets in Lilongwe again armed with our new ‘Za Pamsika’ posters. We were putting them up in the trader’s shops and were pretty quickly swamped with people excited about the website and how it could save them money. But again – everyone was asking us to add more food prices to the site – it seems like Malawians just keep wanting to know more about ‘things you find in the market’!

IMG_1201

So what’s next for Za Pamsika?

We’ve got our new focal point Khataza on board who’s taking charge of the website. First up, taking requests into account, we will be adding other seasonal commodities to the website. We’re going to continue experimenting with our Facebook ads and start using our Facebook page to reach out and engage with people about what they’d like on the page. We’ve also got some new partnerships coming up with civil society organisations who are keen to spread the word about ‘Za Pamsika’ and who we can work with to break down access barriers to this information.

Are millions in Malawi being reached? Not yet – but we’re getting there.

Ain’t no resolution high enough

One of the major challenges we currently face is that while our survey results provide a detailed picture of the food security situation at the regional level, they are only able to provide representative food security estimates at a larger geographic scale, and don’t always tell us where smaller hotspots or pockets of food insecurity are. So we want to find a way to produce the most accurate, up-to-date and granular representations of food insecurity as possible, to help inform our decision making.

Recently some of our team had the great chance to go to Southampton – a peaceful city in the south of the UK – where we loaded up on shortbread and started working on a type of dynamic high-resolution mapping known as Geostatistical Mapping.

The purpose of the trip was to work with and learn from Flowminder/WorldPop. As you might remember, we’ve worked with them in the past to do things like tracking population displacement in Haiti after Hurricane Matthew. They’ve also developed a way to produce high-resolution maps of population demographics and characteristics. We believe these methods can be applied to create high resolution maps of food security indicators.

We collect information at a cluster level (LEFT) - a village, for example. This is relevant at state level (RIGHT)

We collect information at a cluster level (left) – a village, for example. This is relevant at state level (right)

 

As modelling techniques and data processing capability have evolved, and as high resolution satellite imagery has become more available, creating more granular maps than ever before is possible. This is where Flowminder/WorldPop comes into play. Their aim is to provide estimates of population demographics and characteristics for low and middle income countries by integrating census, survey, satellite and GIS datasets, in a flexible machine-learning framework.

So, how does it work? (if you’re not a satistician, skip to the pictures!)

Basically, these high-resolution maps use one or more geolocated data sets, such as rainfall, vegetation or accessibility to markets, and look at the correlation between these secondary sources of geospatial data and something else, say, a particular food security indicator from a household survey in sampled areas (for this reason, high resolution mapping is also referred to as geospatial mapping) . Once we understand the relationship between the two variables in sampled areas, we can make more accurate predictions about the food security situation in non-sampled areas. If available, mobile phone metadata (Call Detail Records) can also be used as an additional covariate, especially in urban areas where the mobile network is dense.

 

How it is now: male literacy rates in Nigeria (shown at cluster level)

How it is now: literacy rates in Nigeria (shown at cluster level)

How we want it to be: high-resolution map of male literacy in Nigeria

How we want it to be: high-resolution map of literacy in Nigeria

 

 

 

 

 

Looking at the example above and the difference in coverage, you’ve probably already understood how appealing high-resolution maps are as a tool for better planning. But we don’t want to stop here – we’re young and full of dreams! If you noticed, we spoke at the beginning of this post about dynamic high resolution maps. We just discussed how to get a static map for more detailed spatial information, but the next step is actually to update this map each time we have new data. This is a great opportunity, because some satellite imagery already provides new data every ten days or so. This means that we could have maps representing the situation in near real-time.

To take this step, we have to bring in data that is available on a high-frequency basis, such as  mobile surveys. These can be used to highlight some areas of our map on regular basis, or to assess the accuracy of the map by checking hotspots with a quick mobile survey.

24 Hour Hackathon People

IMG_9646

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.

IMG_9645

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.

 

chatbot

 

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.