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 Healthsites.io.
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 (www.mapillary.com) 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.