Harnessing the Power of Data for Progress on the SDGs
By Steph Pietras
This article was published by Geospatial World.
The image above shows the signature of the fire at 10:25 p.m. local time on August 8, 2023, as observed by the Operational Land Imager (OLI) on the Landsat 8 satellite. Photo by NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey.
With more than two-thirds of the Sustainable Development Goals (SDGs) off-track, high-quality, timely data to measure and monitor progress is more important than ever before. And to collect and produce this data, strong national statistical systems are needed. However, the SDGs present a complicated monitoring challenge for national governments. Traditional data sources, such as official censuses and surveys, are often outdated and/or lacking data, which creates gaps in SDG reporting. As such, non-traditional data sources, including big data, citizen science, and Earth observation (EO) are becoming increasingly important to complement official statistics. Adopting these innovative data sources will improve SDG monitoring, reporting, and progress, and will lead to better informed, data-driven decision making.
The State of SDG Data
The 17 SDGs include 231 indicators for tracking progress across 169 targets. While data exists for many of these indicators as reflected in the United Nations’ 2023 Sustainable Development Goals Report, which highlights that the number of indicators with good country coverage has increased from 36% in 2016 to 66% in 2022, many gaps still remain. Currently, National Statistics Offices (NSOs) play a vital role in data collection, coordination, validation, and quality assurance for SDG monitoring. However, they face increasing demands for data from users while simultaneously contending with declining budgets and rising data collection costs. More broadly, these data gaps are the result of a plethora of economic, social, and political factors, including limited financing, resources, and capacity, as well as climate change, conflict, the lasting effects of the COVID-19 pandemic, and policy issues.
Timeliness and disaggregation are also key areas of concern according to the 2023 Sustainable Development Goals Report. For example, less than 30% of the latest available data is produced between 2022 and 2023, while over half of the latest data comes from between 2020 and 2021. These data gaps affect countries of all income levels, and capacity building to collect and produce this data is critical. In order to bridge these gaps, innovative data sources like big data, citizen science, and EO are needed to provide more reliable and timely data for sustainable development monitoring, reporting, and implementation.
Filling Data Gaps With Non-Traditional Data Sources and Methods
Non-traditional data sources, especially big data, citizen science, and EO, are being more frequently used to improve data gaps. Big data, or large volumes of high velocity, complex, and variable data, can improve the timeliness and relevance of SDG indicators without compromising their impartiality and methodological soundness. It encompasses different types of data like administrative, commercial, sensor, tracking or mobile phone data, and behavioral or opinion data. Countries using big data can improve the granularity of their official estimates while reducing production costs and respondent burden. For example, mobile phone data was used during the COVID-19 pandemic to help governments track populations’ mobility and migration patterns. Ghana used mobile phone data to help deliver mobility insights and determine the efficacy of its lockdown measures, while South Korea used smartphone data in several of its cities to track individuals infected with the virus and map their contacts. These examples illustrate how mobile phone data can be utilized more broadly to monitor SDG indicator 10.7.2, the number of countries with policies to facilitate orderly, safe, regular and responsible migration and mobility of people. Since mobile phones are used by large parts of the population around the world, mobile phone data has the potential to fill several data gaps. As a result, nearly 80% of NSOs consider mobile phone data a priority in the years ahead.
Citizen science is another non-traditional source of data that offers great promise for SDG data. For instance, recent research demonstrates that citizen science data is already contributing to or could contribute to the monitoring of around 33% of the SDG indicators. This innovative approach can provide a cost-effective mechanism for collecting data on the SDGs while increasing community awareness and engagement on relevant issues. For example, the Ghana Statistical Service was struggling to provide continuous data related to the monitoring of marine litter. To address this issue, they partnered with key stakeholders to use citizen science to monitor SDG 14.1.1b, the indicator related to floating plastic debris density. As part of the Data for Now initiative, this project aimed to close the data gaps related to marine litter by incorporating citizen science data with national statistics and as a result, Ghana became the first country to report on this SDG indicator using citizen science data.
EO data also has an important role to play in improving data coverage and granularity. According to a recent study, existing EO systems could generate data for 33 SDG indicators across 14 goals. Fortunately, over the past few years, technological advancements along with new technologies, such as Geographic Information Systems (GIS), image processing, and satellite imagery have led to a drastic increase in available EO data. For example, satellite imagery is becoming increasingly important in disaster response and recovery. The recent wildfires on the Hawaiian island of Maui have become the deadliest US wildfires in more than a century, with 115 confirmed fatalities and over 1,000 people still missing as of August 23. The National Aeronautics and Space Administration’s (NASA) Earth Applied Sciences Disasters program has been working closely with stakeholders, including the Federal Emergency Management Agency (FEMA), the World Central Kitchen, and the Pacific Disaster Center, to support response and recovery efforts. Using EO data, like maps and satellite imagery, state officials have been able to estimate that neary 3,000 homes and businesses have been destroyed or damaged as a result of the wildfires, totalling to USD $6 billion in losses. Responders have also been able to use these maps to determine where to best allocate their resources for response efforts and humanitarian relief. This crisis demonstrates the value of satellite imagery to track the spread of wildfires, assess the damage, and help coordinate relief efforts, and how EO can be used to support SDG indicators such as 1.5.2 (direct economic loss attributed to disasters) and 13.1.1 (number of deaths, missing persons, and directly affected persons attributed to disasters).
The Way Forward
Non-traditional data sources are being employed to complement national statistics. Governments should continue to invest in these innovative approaches to improve access to high-quality, timely data for decision-making on sustainable development, leading to more accurate SDG reporting and monitoring and increased progress on the 2030 Agenda. As we near the midpoint of Agenda 2030, by harnessing the power of non-traditional data, national governments can create better-informed decision-making that can drive effective, efficient, and responsible actions towards achieving the SDGs.