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NEED-BASED RESOURCE ALLOCATION USING GEOSPATIAL ANALYSIS
During the COVID-19 pandemic, the Lagos State government received a donation of solar home systems (SHS) as part of a COVID-19 relief response. The government asked CrossBoundary to identify the communities across the state that would most benefit from the donated units. Given the rapid spread of COVID-19, the government needed to efficiently deliver the units to each community.
To prioritize communities, CrossBoundary Data Analytics used geographic datasets and statistical software to assign a need score to every community. Our partner, Fraym, used advanced machine learning to produce datasets that included variables such as population density, access to grid electricity, household expenditure, and ownership of assets such as electrical appliances and generators.
Finally, the team conducted geospatial analyses to optimize the logistics of distributing the donated SHS from each of the 5 warehouses to the top 100 selected communities.
The team presented the results of the needs assessment and SHS allocations to the Lagos State government, providing a ranking of the top 100 most vulnerable communities along with their geographic coordinates, and the initial number of donated units to be delivered to each. As a resource for the distribution team, we also provided an interactive map marking the location of each recipient community.
Fraym is the preeminent global provider of geospatial data for understanding population dynamics. Governments and organizations around the world rely on Fraym data to make strategic and operational decisions while tackling challenges like inequity and insecurity, climate vulnerability, public health, and more. Fraym and Cross Boundary have partnered to advance a shared mission of sustainable growth in frontier markets. By combining expertise in investment, geospatial data and data analytics, the collaboration will deliver unprecedented, localized insights to inform global development policy and programmatic decision-making.