How Data-Poor Countries Remain Data Poor: Underestimation of Human Settlements in Burkina Faso as Observed from Nighttime Light Data

Abstract

The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.

Which night lights data should we use in economics, and where?

“…VIIRS has better detection rates but the elasticity of gross cell product with respect to VIIRS lights still depends on population density (Chen and Nordhaus, 2015). Even just for cities and towns in Africa, the detection rates using the DMSP and VIIRS satellites are only 40-45% (Andersson et al, 2019). Other examples from Africa, Asia and the Pacific of low density areas that are home to up to 70% of the population not being detected by either DMSP or VIIRS, even when more than half of households in those areas use electric light, are given by Gibson et al (2019).…”

Does decentralization encourage pro-poor targeting? Evidence from Kenya’s constituencies development fund

“…Bruederle and Hodler (2018) reports that over half of the continent shows no stable visible light at all. Also see Andersson et al (2019) and Maatta and Lessmann (2019) on this topic.…”

The Fertility Transition in Sub-Saharan Africa: The Role of Structural Change

“…In other cases, such a selfemployment in the service sector, it is not possible to clearly differentiate between the formal and informal sector. 8 The inability of satellites to detect small, low density, settlements is also seen in Andersson, Hall and Archila (2019); out of 147 geo-referenced cities and towns in Burkina Faso, ranging in population from 7000 to 1.6 million, 83 of these communities (the largest of which had a population of 32,000) went undetected over the entire 21 years of satellite recordings. activity such as traditional farming, small rural shops and markets, but rather larger-scale modern industrial and service activities.…”