Rogers, GrantKoper, PatrycjaRuktanonchai, CoriRuktanonchai, NickUtazi, EdsonWoods, DorotheaCunningham, AlexanderTatem, Andrew J.Steele, JessicaLai, ShengjieSorichetta, Alessandro2023-09-082023-09-082023-08-30Rogers, G.; Koper, P.; Ruktanonchai, C.; Ruktanonchai, N.; Utazi, E.; Woods, D.; Cunningham, A.; Tatem, A.J.; Steele, J.; Lai, S.; Sorichetta, A. Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa. Remote Sens. 2023, 15, 4252.http://hdl.handle.net/10919/116249Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.application/pdfenCreative Commons Attribution 4.0 Internationalnight-time lightsGoogle Aggregated Mobility Research Datasethuman mobilityAfricarural and urban classificationArticle - Refereed2023-09-08https://doi.org/10.3390/rs15174252