VAM Talks: Episode 13

Logo2Alice Clough interviews Sarah Muir, VAM remote sensing analyst, Haidar Baqir, IT engineer, and Ariona Aubrey from WFP’s legal department about WFP’s use of satellites and drones.

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

KOICA pic 2

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. 

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.