Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Cascade Tuholske,Andrew Kruczkiewicz,Charles Huyck,Alessandro Sorichetta,Greg Yetman,Agathe Bucherie,Carolynne Hultquist,Andrea E Gaughan,Forrest Stevens,Alex De Sherbinin

doi:10.3390/su13137329

Abstract

Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

Highlights

The United Nations Sustainable Development Goals (SDGs) aim to end global poverty by 2030 and ensure a sustainable future [1]
Data focus on three geographies of interest—Nepal, the region of Mozambique, We focus three geographies of interest—Nepal, the1)—to region explore of Mozambique, Malawi, and on Zimbabwe (MMZ), and Ecuador (Figure how the Malawi, gridded and Zimbabwe (MMZ),measure and Ecuador (Figure 1)—to explore how 11.5 the gridded population products populations related to SDG
For intensities less than 7 (Figure 5), we found a broad range of population estimates across the gridded population products

Summary

Introduction

The United Nations Sustainable Development Goals (SDGs) aim to end global poverty by 2030 and ensure a sustainable future [1]. The SDGs were designed to overcome the measurement challenges of the Millennium Development Goals [4] by outlining a clear set of indicators to track progress. Such as the Sustainable Development Solutions Network [5] and the Global Partnership for Sustainable Development Data [6]—were established to provide countries with best practices to monitor SDG indicators and to support decision making to achieve the SDGs [7]. These partnerships increasingly advocate for countries to leverage the considerable amount of Earth observations (EO) data to track SDG indicators. Researchers, practitioners, and decision makers collectively lack guidance on how to best utilize wide-ranging and context-specific EO data to monitor SDG indicators

Methods

Results

Discussion

Conclusion