Positive Deviance: Positive Outliers Matter

By Jeremy Boy, Data Scientist, UNDP Accelerator Labs together with Andreas Gluecker, Project Manager, GIZ Data Lab.

For more details on the project background, please read the first blog on this series “Launching the Data Powered Positive Deviance Initiative” written by our partners from the Data Powered Positive Deviance DPPD initiative.

Satellite image of land cover classification in Joya de los Sachas in Ecuador (2020)

A “Positive” Case Study

The Positive Deviance approach assumes that in every community, there are individuals or groups whose uncommon behaviors or coping mechanisms enable them to find better solutions to problems they face than their peers while having access to the same resources. Positive Deviance interventions aim to identify those individuals and groups and understand what attributes or traits differentiate them from others. These are then mobilized to help fellow community members overcome their problems together, by using locally accessible assets and resources. In short, instead of looking at the bad and trying to correct it, a Positive Deviance approach looks at the good and tries to collectively replicate it in situ.

Staff speaking to families in Viet Nam to identify and understand positive deviants that address malnutrition © Positive Deviance Collaborative

Big Data, Positive Deviance, and Development

Traditional Positive Deviance initiatives rely heavily on primary and secondary sources of data, i.e., data collected directly for the purpose of the study, and repurposed data (like census data), respectively. Unfortunately, these data are either very costly (primary data), too highly aggregated, or focused on variables irrelevant to positively deviant behaviors of interest (secondary data). They may also be too limited in time (a snapshot rather than an evolving view of a situation).

The Data Powered Positive Deviance is a global initiative running pilots in various countries to see — if and how — we might use big data-based positive deviance to tackle development challenges.

Ecuador: Mitigating Deforestation through Sustainable Cattle Raising

Right: Satellite Image of Joya de Los Sachas, Ecuador (2020); Left: Land cover classification in Joya de Los Sachas, Ecuador (2020). ©UNDP Ecuador.

The UNDP Ecuador Accelerator Lab together with the GIZ Data lab and partners are using satellite imagery to identify positively deviant cattle farmers who operate in zones of expansion of the agricultural frontier without further contributing to deforestation.

Using additional sources of climate, socio-economic, and ethnographic data, the team will then investigate the contextual factors and characteristics that contribute to these farmers’ forest-friendly cattle raising practices.

Mexico: Addressing Violence Against Women In Public Spaces

© Frederik Trovatten.com

Using additional sources of data — such as, sentiment analysis of social networks, socio-economic census, perception of public security, urban infrastructure — the team will then investigate the contextual factors and characteristics that contribute to lower assault rates against women in these spaces.

Niger — Ensuring Food Security Through Sustainable Agriculture

© Alex Wigan

The UNDP Niger Accelerator Lab and partners will use remote-sensing data to identify positively deviant cultivator communities that manage to accelerate their agricultural cycles, for instance by using soaking techniques, thereby shortening their land occupation and improving the quality of their crops.

Using additional sources of big data, the team will then investigate the contextual factors and characteristics that contribute to these communities’ ability to accelerate their practices, and whether this can help mitigate conflict.

Somalia/Somaliland: Supporting Pastoralist Communities Amidst Environmental Challenges

Displaced women and children onlooking the last of their herd at the peak of 2017 drought ©KieranMcConvil

The UNDP Somalia Accelerator Lab and partners will use remote sensing and mobility data to identify positively deviant pastoralist communities that continue to flourish despite the effects of climate change and avoid further congesting IDP settlements.

Using additional sources of official data from national and international organizations, the team will then investigate the contextual factors and characteristics that contribute to these communities’ ability to sustain their way of life.

What To Expect Next

The UNDP Accelerator Labs are starting to implement their pilots and are doing preliminary analysis to strengthen the implementation of their experiments. Their learnings, challenges, insights, and feedback will then be shared across the UNDP Accelerator Lab Network, and beyond.

Building the world’s largest learning network around development challenges. 91 Labs in 115 countries. http://acceleratorlabs.undp.org/

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