Spatiotemporal Analysis of Venezuela's Nighttime Light During the Socioeconomic Crisis

Abstract

The lack of official statistics makes it difficult to assess Venezuela's economic situation during the socioeconomic crisis. In this article, we used Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light images to evaluate Venezuela's crisis. The Hodrick-Prescott filter was used to decompose the sum of urban light (SUL) into two components: the sum of urban light trend (SULT) and the SUL cycle. Then, we proposed an index of nighttime light change ratio to estimate Venezuelan nighttime light dynamics. We found that Venezuela has lost 30.37% of its SULT from April 2012 to December 2018. The regression analysis shows that Venezuelan SULT had a strong relationship to a number of socioeconomic indicators: the SULT was positively correlated to crude oil production with R² of 0.9159, negatively correlated to dollar exchange rate with R² of 0.9516, and negatively correlated to the number of asylum seekers with R 2 of 0.8384. We also found that among the three states with a largest nighttime light decrease, the economy of two states was dominated by agriculture and that of one state was dominated by the oil industry. In the pixel analysis, compared with the urban cores, the suburbs of urban cores of 12 main cities had a higher percentage of SULT increased areas. Around the Venezuela-Colombia border, the SULT decreased in the Venezuelan side but increased in the Colombian side. Our analysis suggests that nighttime light imagery can help to assess Venezuela's situation during the crisis.

Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data

“…Therefore, such data are widely used in many fields. NTL data are effective for assessing long-term socioeconomic parameters such as gross domestic product (GDP) [2] , [3] , human population [4] , [5] , electrical power consumption [6] , [7] , greenhouse gas emissions [8] , and poverty [9] ; for quantifying urbanization [10] – [15] and urban sprawl [16] – [18] ; for monitoring the impacts of unexpected events, such as war [19] – [21] , natural disasters (e.g., typhoons, earthquakes, and floods) [22] , [23] , large-scale power outages [24] ; for tracing fishery [25] ; and for analyzing environmental effects [26] , [27] and assessing issues such as light pollution [28] , [29] , animals’ living habits [30] – [32] , and human physiological and psychological health [33] – [38] , etc.…”

The Dimming of Lights in India during the COVID-19 Pandemic

“…The usage of VIIRS DNB data to study economic landscapes of countries at the granular level is well established, and there have been studies specific to India [9][10][11]. Low-light imaging satellite sensors have also been used to detect dimming and recovery of lights after natural disasters [12][13][14], wars [15,16], other humanitarian crisis [17], and economic collapse [18]. With this background information, this paper explores the effect of the COVID-19 pandemic in India through the percentage change in the brightness difference images of monthly VIIRS DNB data.…”

Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

“…As data that can reflect the differences in urban spatial surface information, urban remote sensing has gradually replaced the use of traditional statistical data, such as administrative indicators, economic, and population statistical data, due to their advantages, including a larger spatial scale, wider collection range, and more connected time scale [ 16 , 18 , 19 ]. As one of the most widely used remote sensing data, NTL data mainly reflect the difference between urban and rural development levels by representing the light brightness of urban buildings and infrastructure at night [ 20 , 21 ].…”