Case Study

Participatory mapping in the Maldives

What problem were they solving? 

The Maldives islands are one of the lowest-lying countries in the world. Like other large ocean island states, they are vulnerable to a range of climate change impacts: rising sea levels and extreme weather events which are becoming more frequent and intense. These changes threaten the islands’ infrastructure, erode shorelines and contaminate freshwater sources. They also pose a risk to livelihood areas including tourism, fisheries and farming.  

The archipelago has produced decentralized strategies for disaster risk planning but has not developed detailed local plans for each of its over 200 inhabited islands. The National Disasters Management Authority (NDMA) works with the local councils to plan for disaster risk management at the national level, however, granular data about vulnerable building infrastructure and community assets is not always available at the island level. This lack of high-quality information available at the place and time of need – a data gap – can result in poor coordination between the central authority in charge of disaster risk planning and the communities in the smaller islands – a doing gap. Islanders are aware of, and concerned about, extreme weather events, yet many feel unprepared and helpless when it comes to disaster preparedness and response.  

What did they do? 

The UNDP Accelerator Lab in Maldives prototyped a participatory mapping approach to try to fill the data gap for vulnerability to extreme weather. Working with the local council of the Maafaru island in the Noonu atoll, the Accelerator Lab engaged 12 local residents to create a map of island infrastructure to be used for more effective disaster management. To do this, volunteers realized they first needed to improve the base layers of the map, including Maafaru’s detailed road network and building footprints. This base layer was then used to develop what is known as a Hazard, Vulnerabilities and Capacities (HVCA map). Like many islands in the archipelago, Maafaru did not have a detailed geographic information system (GIS) baseline map.  

Recruiting volunteers through social media and local networks, like NGOs and island women development committees, enabled higher levels of engagement from young people and women. Using easily accessible digital tools like Mapillary and OpenStreetMap alongside paper maps, they collected data on basic infrastructure to inform and develop a basemap of the island. In addition to the infrastructure basemap, they also created an HVCA map to visualize risk exposure and locally available physical and human resources, for example, stronger buildings which could act as assembly points. The UNDP Accelerator Lab aims to scale their methodology to other islands to institutionalize the approach within government. In the meantime, the existing maps are used to formulate island-level disaster management plans and shared with stakeholders. This open dataset can also be used to support more accurate risk modeling and loss and damage projections by others.     

What was the benefit of using collective intelligence for this issue?  

The data from the participatory mapping activities helped to identify household-level risks, which has informed the local disaster response plan. Involving community members in data collection and validation improved the granularity and accuracy of the data. Using digital tools, participants not only drew the exact building polygons but were able to add additional attributes like building material, which gives an indication of household vulnerability. Using local knowledge to capture information about exposure to multiple different hazards (tropical storms, swells, beach erosion), =added to the dataset’s versatility. The data collection and validation were achieved in the space of two days by local residents: they already possessed most of the information intrinsically and used the time to encode it on the map and validate it.  

This approach laid the foundation for stronger connections between local institutions and communities. It can help improve coordination between officials and residents during future emergencies as well as developing trust and building capacity amongst local residents to take more appropriate actions which are vital for reducing doing gaps. In particular, the volunteers involved in data collection can act as leaders in future emergency response efforts. 

What does this experience tell us about collective intelligence for climate action? 

This experience demonstrates the challenges around matching locally relevant information to effective local planning. Its participatory approach ensured that the data collected were in principle useful for decision makers at both the local and national levels, as well as local residents; and it attempted to help build the internal capacity of local authorities to deliver and sustain these types of initiatives. In reality, many island councils in Maldives lack the technical capacity to work with GIS data. They use maps of their islands which are not compatible with GIS applications, nor integrated into national systems for risk planning. This lack of capacity risks making locally collected data irrelevant: closing the data gap does not automatically translate into shrinking the decision-making gap.

NDMA, which has this capacity, is better positioned to translate the data into disaster management plans. For this reason, the Lab designed the process so that local collective intelligence would be mobilized to contribute to decision making at the national level, and to engage the existing administrative pipeline for processing the data. This case study exemplifies the value of positioning collective intelligence where it can generate the highest impact, rather than assuming that better information will automatically lead to better decisions. 

CLIMATE ACTION GAPS ADDRESSEDData Gap, Doing Gap, Decision-Making Gap
COLLECTIVE INTELLIGENCE USE CASEAnticipating, Monitoring And Adapting To Systemic Risks
IPCC CATEGORYAdaptation, Disaster Risk Management
PEOPLELocal Residents, Local Councils
DATAGeospatial Data, Crowdsourced Observations
TECHNOLOGYMapillary, Open Street Map (OSM), QGIS