Towards a Spatially Explicit Framework for Sustainable Development Policy

Abstract

The United States Government (USG) launched Feed the Future in the wake of the 2007-2009 global food price crisis. The initiative, a collaborative effort between several countries, local stakeholders, and multilateral institutions, aimed to alleviate the severest cases of global poverty and hunger. The initiative was launched with two distinct features; (I) a focus on evidence-based, data-driven evaluation; (II) the goal of promoting “inclusive growth” which would target and asymmetrically aid women and children. To support the first feature, the USG mandated the creation of a results framework under which indicators could be produced to measure the progress and efficacy of distributed aid. The second feature ensured that Feed the Future would become one of the largest and most ambitious “sustainable development” programs ever enacted. By mapping the indicators created for Feed the Future to those produced in accordance with the United Nation’s Sustainable Development Goals (SDGs), the USG enters into a decades long methodological conversation on indicator production, benefits from and supports a broad international data sharing infrastructure and prepares indicators for analysis within a spatially explicit framework.

Given the distinct information needs present in sustainable development policy (i.e. economic, social, environmental), location provides a unique and unifying common foundation on which a shared database may be produced. Georeferencing – or denoting data collected from ground surveys with geographically explicit markers – before the production of indicators allows for emerging techniques in geospatial estimation to be applied to metrics produced by indicators, reducing several key constraints of current surveying practices; namely, (I) the temporal limitations imposed by the frequency (or lack thereof) of surveys; (II) the resource limitations imposed by the challenges of conducting surveys in developing countries; (III) the necessity of stating clearly and concisely the causal pathways between indicators and outcomes – as opposed to the potential for novel estimation using machine learning models.