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: male 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 male literacy in Nigeria

Looking at the example above and the difference in coverage, you’ve probably already understand 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.


Originally published at MVAM: THE BLOG.

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