But clouds got in my way: Bias and bias correction of VIIRS nighttime lights data in the presence of clouds

Patnaik, Ayush; Shah, Ajay; Tayal, Anshul; Thomas, Susan

doi:10.2139/ssrn.3957319

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raschky (2020) has provided Python functions for the same purpose. Patnaik et al (2023) have assembled a Julia library that specializes in processing nightlight imagery. With the same motivation, our contribution lies in presenting an alternative, yet complementary, approach for processing and analyzing satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Exploring economic activity from outer space: A Python notebook for processing and analyzing satellite nighttime lights

Patnaik,

Mendez

2024

REGION

View full text Add to dashboard Cite

Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%