Inferring Nighttime Satellite Imagery from Human Mobility

Abstract

Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location. This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come. In fact, since human mobility is far more directly related to the socioeconomic variables being predicted, it has an even greater potential. Additionally, since cell phone locations can be aggregated in real time while preserving individual user privacy, it will be possible to conduct studies that would previously have been impossible because they require data from the present. Of course, it will take quite some time to establish the new techniques necessary to apply human mobility data to problems traditionally studied with satellite imagery and to conceptualize and develop new real time applications. In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. We also show that these artificial maps can be used to infer socioeconomic variables, often with greater accuracy than using actual satellite imagery. Along the way, we find that the relationship between mobility and light emissions is both nonlinear and varies considerably around the globe. Finally, we show that models based on human mobility can significantly improve our understanding of society at a global scale.

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

“…So far, using Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data, urban studies have focused on the following areas: (1) the relationship between NPP-VIIRS nighttime lights data (NTL) and socioeconomic indicators, such as population, GDP, and housing vacancy rate [50,51];…”

Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa

“…The GAMRD Inward Flow metric for 2018-2019 showed moderate positive correlations in the rural group (11,12,13 in Figure 3) and dense-urban class (23 in Figure 3) with weak to moderate correlations in the urban (30 in Figure 3) and peri-urban (21 in Figure 3) classes. For 2020, the correlations were considerably lower in the very-low-density rural (11 in Figure 3) class, marginally less in the rural (12, 13 in Figure 3) group and considerably less in the urban group (21,23,30 in Figure 3).…”

“…The GAMRD Outward Flow metric for 2018-2019 showed moderate negative correlations in the rural group (11,12,13 in Figure 3) and dense-urban (23 in Figure 3) class with weak to moderate correlations in the urban (30 in Figure 3) and peri-urban (21 in Figure 3) classes. For 2020, the correlations were considerably less in the very-low-density rural (11 in Figure 3) class, marginally less in the rural group (12, 13 in Figure 3) and considerably less in the urban group (21,23,30 in Figure 3). The Spearman's correlation coefficients for each rural/urban classification were placed on a bar chart with the relative proportions of each rural/urban classification included for reference as shown in Figure 6.…”

“…By following these criteria, twelve countries for 2018, 2019, and 2020 were selected for a correlative analysis between the three GAMRD flow metrics and the corresponding NTL SoL values calculated for each level 2 administrative unit. In addition, a previous global study (Dickinson et al, 2020) [30] found that in different parts of the world, the relationship between mobility and light production differed considerably, and analyses should account for such regional variations. The twelve selected countries spanned a broad geographic range across the African continent and provided a convenient means for grouping for subsequent analysis according to the United Nations Geoscheme for Africa (UN Statistics Division, 2022) [40] which separates African countries according to cardinal direction as shown in Figure 1.…”

“…The Google Aggregated Mobility Research Dataset (GAMRD) data, providing a measurement of human movements as quantized flow metrics, are principally derived from smartphones and represent the result of anonymous and aggregated phone locations for users who have opted into Google's Location History feature, which is off by default (Ruktanonchai et al, 2018) [26]. Previous research has indicated that there is a strong nonlinear relationship between GAMRD and NTL data (Dickinson et al, 2020) [30]. However, these studies only obtained and analysed mobility data for a short time period (e.g., 6-12 months) in a single country.…”