Applications of nighttime light data in international development research

Applications of nighttime light data in international development research

3ie and New Light Technologies co-led a series of capacity-building workshops with 10 researchers from the African Population and Health Research Center (APHRC) on the potential to use remotely-sensed geospatial data for impact evaluations. This blog is the fourth in a series of four in which workshop participants reflect on the uses of remotely-sensed and geospatial data.

The increasing availability of remotely-sensed measurements of nighttime light intensity across space and time opens the door to new possibilities to understand how the Earth is changing. These insights can improve decision-making to guide policy, deliver services, and improve governance in near real-time. However, accelerated human modifications of the landscape and human activities are profoundly affecting the processes on the Earth's surface, both locally and globally, creating a variety of challenges for scientists and policymakers in understanding global change and its repercussions.

Images of the Earth at night obtained from satellite data have become a spatially clear worldwide depiction of human presence and activity on the globe. One source of such data is the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS). Several proxy metrics of human socio-economic activity have been developed using this night-time imagery. Nighttime lights offer an alternative, effective, and visually impactful method of identifying under-developed areas and facilitating more optimal allocation of resources.

Nighttime light (NTL) imagery is unique among remote sensing data sources because it provides a uniquely ‘human’ view of the Earth’s surface. The presence of lighting at night across the globe is almost entirely due to some form of human activity. Nighttime lights are being used to measure the extent and characteristics of urbanization processes; estimate economic growth at both national and subnational levels; map global poverty as well as population density, migration, and mobility patterns; track local household wealth, education, and health; understand armed conflicts; measure access to electricity and electrification as well as community resilience, fishing activity, coral reef health, and more. Recently, researchers have also shown that nighttime lights can even help explain brain development and human behavior.

Nighttime light observations are becoming a primary source of data for tracking the progress of the pandemic and its impacts. The data provides a compelling and striking picture of the large-scale impacts of COVID-19 on Earth, from the impacts of the pandemic on businesses and transportation networks to monitoring the gradual recovery of cities around the world.

Lighting changes between January 19 and February 4, 2020, in Jianghan District, a commercial area of Wuhan, China, as retrieved by the Visible Infrared Imaging Radiometer Suite (VIIRS). Source: Christopher D. Elvidge


Changes in the intensity of nighttime lights can be used to illustrate 
the pace of recovery. These images show changes in nighttime lights between March 2020 and 
February 2020. Cyan = lighting brightened, Red = lighting dimmed. 
Source: Elvidge et al., 2020. The Payne Institute for Public Policy.

Nighttime lights can also be used to approximate some socioeconomic indicators when no other reliable data exist. The data allow countries with weak statistics to estimate various social and economic indicators at regular time intervals. For instance, areas that lack economic accounting systems can still make an estimate of economic growth based on nighttime luminosity data. They can also be used to provide a reckoning of human population distribution in absence of a proper census. Countries that are already compiling sufficient socioeconomic statistics can further improve the timeliness and relevance of this information by complementing it with data from nighttime lights. A multitude of experts have applied nighttime light analysis in different fields to monitor urbanization, explore the impact of conflicts and disasters, evaluate aid-effectiveness, track fishery activities, evaluate carbon dioxide emissions and assess poverty.

Several international development research efforts have used nighttime light data as a Proxy Measure of human well-being, to measure changes in human activityto map the urban areas of cities across the globe, to monitor economic development from space, as a good proxy measure for economic activity, as a proxy for human development at the local level, and to measure regional inequality. Furthermore, nighttime light remote sensing has been demonstrated to be particularly beneficial for a variety of natural science and social science applications and has been widely employed in socio-economic research, as well as having significant promise for monitoring specific SDG indicators.

Nighttime light data also provides insight into the social, economic, and cultural patterns and behaviors within urban environments, from electrification, conflict-induced migration, holidays, and more.

The World Bank Group, in partnership with the University of Michigan and the National Oceanic and Atmospheric Administration (NOAA), has launched the World Bank – Light Every Night archive (New Light Technologies has supported the development of these tutorials). The Light Every Night dataset is published on the Amazon Web Services Registry of Open Data and is designed from the ground up to be analysis-ready. The data archive includes nighttime lights imagery for DMSP-OLS and VIIRS-DNB and supplemental data quality layers for creating masks against cloud coverage, lunar illuminance, and stray light. More details are available on the technical documentation.

Applications for our workstreams at APHRC

Research has shown that nighttime light data can be used to support some of  APHRC’s research focus areas such as urbanization and wellbeing, migration, poverty, and health dynamics. The nighttime light data can be also used as a complementary approach along with other innovative tools the APHRC is working on, such as the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The system is the first urban-based longitudinal health and demographic surveillance platform in sub-Saharan Africa (SSA) to investigate the long-term effects of urban slum residences on health and socioeconomic outcomes, mobility patterns, and local household wealth. Moreover, nighttime light data may be useful for longitudinal population-based health data, particularly for the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), and  Longitudinal population studies (LPSs), which provide robust data that could answer questions on health, population dynamics, and development

With the launch of the new generation of nighttime light detection sensors, challenges and opportunities exist for future applications based on nighttime light images. A transparent platform that provides different versions of post-processed images, promotes communication and discussion of different detection algorithms, and synthesizes results at different spatial and temporal scales would benefit the academic world and beyond.

However, due to the limitations of the capability of sensors for detecting low lighting at night and the unsolved issues of algorithms and technologies in data generation and processing, challenges still exist in accessing long-term consistent high-quality nighttime light data. These challenges hinder the further applications of nighttime light remote sensing for perceiving the changing world. Future research on satellite remote sensing of nighttime light requires improved sensors and data products for acquiring accurate and rich information.

Call to Action: With the increased use of technology, population health research institutions need to invest in the collection of nighttime data which can be used for estimating population dynamics such as urbanization and migration, as well as to monitor disasters, conflicts, and diseases.

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Authors

Moussa Bagayoko Moussa BagayokoAssociate Research Scientist, APHRC
Damazo Kadengye Damazo KadengyeHead of Data, Measurement and Evaluation, APHRC
Cynthia Runyenje Cynthia Runyenje Monitoring and Evaluation officer, APHRC
Aayush Malik Aayush MalikData Science Associate

About

Evidence Matters is 3ie’s blog. It primarily features contributions from staff and board members. Guest blogs are by invitation.

3ie publishes blogs in the form received from the authors. Any errors or omissions are the sole responsibility of the authors. Views expressed are their own and do not represent the opinions of 3ie, its board of commissioners or supporters.

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Authors

Moussa Bagayoko Moussa BagayokoAssociate Research Scientist, APHRC
Damazo Kadengye Damazo KadengyeHead of Data, Measurement and Evaluation, APHRC
Cynthia Runyenje Cynthia Runyenje Monitoring and Evaluation officer, APHRC
Aayush Malik Aayush MalikData Science Associate
Ran Goldblatt Ran GoldblattGeographic Information System (GIS) and Remote Sensing expert

About

Evidence Matters is 3ie’s blog. It primarily features contributions from staff and board members. Guest blogs are by invitation.

3ie publishes blogs in the form received from the authors. Any errors or omissions are the sole responsibility of the authors. Views expressed are their own and do not represent the opinions of 3ie, its board of commissioners or supporters.

Archives