Written by Jurg Wilbrink 510 GEO INFORMATION SERVICES

The 510 initiative is working with the Malawi Red Cross to identify the country’s most vulnerable areas. A workshop and mapping exercise were held on data collection and sharing with different stakeholders in Malawi. Vulnerability data contributes to the Red Cross’ data preparedness initiative (see also Method: Implementing Data Preparedness), yet availability is very limited. A team of twenty Malawi Red Cross Society (MRCS) volunteers, in cooperation with 510, have developed and successfully tested a new method to determine vulnerabilities based on a remoteness indicator. This method can now be deployed and fine-tuned in other countries.

‘Remoteness’ as a proxy indicator for vulnerability

For the MRCS and the Red Cross movement, the ability to find the most vulnerable communities is a priority. Vulnerability is also an important indicator used in the Community Risk Assessment & Prioritization developed by 510 as explained in two blog posts about the Efficient Aid: Through Accurate Priority Index Part 2 and Faster Reponse: With Capacity Building in Philippines Part 1). However, crucial data on vulnerability is often missing. Whereas vulnerability is regularly measured using a set of tools called the ‘Vulnerability and Capacity Assessments’ or VCAs, here we develop and test a complementary method as outlined below.

As a proxy for ‘social vulnerability’, so-called “remoteness indicators” are being developed on a community level within Malawi. Remoteness of communities is identified through several parameters: (i) distance, such as to health facilities, sanitation points, schools, and public and private facilities; (ii) geographic properties, such as ruggedness of the landscape, and (iii) density figures, of the population, houses, roads and other structures. Most indicators are created with the use of OpenStreetMap data, and can therefore be applied to more countries when proven valid. These proxy indicators can supplement areas in which the vulnerability assessments are absent or incomplete. Lack of data collaboration within Malawi is the main concern in obtaining accurate and timely data, as NGO’s and governments have different ways to collect, store and share data. This often results in data being spread out across time and space, which subsequently causes information gaps. Proxy indicators are a powerful measure to fill the gaps between these different data sets. Below is an example output of a remoteness indicator developed for access to hospitals in Malawi. Survey outcomes indicate that the analysis of travel times to hospitals was accurate for citizens travelling by car or motorbike under normal circumstances. The algorithm used is then modified to fit the average travel time including those community members travelling to hospitals by public transport, bike or foot. A more elaborate description of the indicator will follow in a separate blog post.

The remainder of this blog will focus on how we have trained enumerators for data collection, and how we used a combination of innovative tools such as Missing Maps, Mapillary and the Portable OSM server to collect the data needed to verify our remoteness indicator.


Remote data collection

The area of interest where the remoteness indicator was field tested was first remotely mapped through the Missing Maps project. We joined in on a global effort to map Malawi. During 10 mapathons in the Netherlands a total of 500 volunteers from corporate sector, universities and the Netherlands Ready2Help network have mapped over 100.000 houses and thousands of kilometres of roads and paths by tracing satellite imagery.

The outcome of the mapathon is a base map with all buildings, roads and paths (see below). Key datasets, such as the locations of hospitals, schools, and water points and sanitary facilities were collected from the Malawi Spatial Data Platform (Masdap), from Openstreetmap and from

Local data collection

Remote data collection can only give so much detail. Therefore local data collection with enumerators is needed. As part of our data preparedness mission in Malawi we provided a training on the use of digital surveying tools and a general introduction to the upcoming field work. The enumerators (surveyors using digital tooling) were trained in using OpenDataKit (ODK) and OpenMapKit (OMK): tablet- or smartphone-based applications to conduct digital surveys in the field. ODK is specifically designed to collect survey data, while OMK is used to enrich OpenStreetMap data by using the geographic locations of the buildings. This way, the survey data is linked to the geographic locations of the buildings and forms an additional source of data for the Geographic Information Systems (GIS) analysis of the future.

After two weeks of data preparedness activities and training in Lilongwe and Blantyre we were all ready to work!

We moved to very remote areas to put all equipment and conducted analyses to the test. We visited villages in the Thyolo district, to the south of the city of Blantyre. During the field week, we validated the travel times to hospitals and schools as two of the most important proxy indicators for vulnerability. Therefore, we gave these measures a prominent role in the field survey. Survey questions were carefully selected to fit two purposes: Validation of the proxy indicators, but also creation of initial baseline data on the Thyolo district for future MRCS projects.

The OMK application provided the enumerators a platform to add sanitation and water points to the Open Street Map dataset. As the buildings were mapped remotely, it was interesting to see that around seventy to eighty percent of the mapped buildings were correct. In those cases, where the building was either mapped in the wrong place or missing completely, it was often washed away by heavy rains and rebuilt next to its original location. In some cases, new buildings were completely missing from the mapped region. In these cases, the map is as good as the remotely sensed imagery; it may contain clouds, lack in resolution or be outdated. Outdated maps are the most frequent cause of error; new satellite imagery is therefore necessary for making accurate maps for humanitarian purposes.

Dealing with low internet connectivity

In rural Malawi, it is often a challenge to upload base maps of the visitation sites and the correct survey of the day to the 20 tablets, and to download all the data collected in the field from the tablets in an efficient manner. To mitigate dependence on internet connectivity, we brought a Portable OpenStreetMap device (POSM, developed by the American Red Cross – GIS team) to Malawi to temporarily store offline edits made during the survey. This device creates a Wi-Fi network for the tablets, enabling data to be processed on the go.  To prevent errors, collected data was checked during lunch breaks, during which everyone was in range of the POSM device, which was plugged into the car. This way, collection errors could be caught in time, and enumerators were guided in the right direction.

Collecting streetview data for remote analysis

During the field work the entire traveled path was recorded using Garmin VIRB cameras which captured the GPX points of each photo. The data collected by the cameras was uploaded to Mapillary ( and OSM to add to the open ‘street view’ images of the world – these are the first street view images to be made for Malawi, take a virtual tour here. The images will be used in our Missing Maps mapathons to enrich OSM data, as through studying the images the building material and even the function of buildings can be identified.


In Malawi, the OMK application and the POSM have proven to be a powerful combination. Availability of building location data was necessary for validation of the research as well as for creation of more accurate data on the Thyolo communities. Meanwhile, the very limited internet connectivity could be overcome by the POSM, with direct verification of data collected as one of its key strengths.

The collection of ‘street view’ imagery was easier than expected, with absence of power supplies being the largest challenge, as twenty tablets and personal phones needed charging as well.

The survey was conducted with the help of twenty volunteers and the training and mapping activities proved very successful, with all volunteers performing above expectations. For surveys, mapping activities and even IT purposes, volunteers are invaluable for the Red Cross as they form an important pillar of the local capacity.

Working with remotely sensed data is often a cost-effective way of gathering information and has proven to be a good source for analysis of remoteness indicators.

Data-responsibility & ethics

Data collected in this project was the minimum needed to verify the remoteness algorithms, and to contribute to specific key datasets, such as the locations of schools and hospitals, as well as building materials of buildings. In each village where data was collected, the village chief was consulted and permission was asked. Personally identifiable data was not collected, nor did we collect any data about the household composition and vulnerabilities. Respondents were asked about the average travel time to different locations, for all family members, reducing the need to request personal data from the family members. Photos collected for Mapillary have gone through computer vision technology that detects faces and licence plates on the photos and applies blurs to them before they are visible on Mapillary. The vulnerability product that we derive from the data was discussed with government departments and with researchers working on vulnerability mapping for Malawi, as well as with the Malawi Red Cross. Respondents were neutral to our approach and did not express concerns. This way we try to make sure we involve local partners and take their concerns seriously. We believe that neither this data, nor the vulnerability proxy will do harm to the people in Malawi. On the contrary, it can help government and NGOs to better target the most vulnerable. If you do identify a risk, please reach out to us directly.

Data & code

Key location data, such as schools and hospitals, and building material data, collected in this project has been added to Openstreetmap and is therefore publicly available. Photos collected by us in the Mapillary streetview are available here. Travel distance data collected from families is not publicly available and will only be used to calibrate the remoteness indicator. The remoteness indicator will be finalized before July 2017 and the link to the Github repository will be shared here.



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