Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312662 
Year of Publication: 
2024
Citation: 
[Journal:] REGION [ISSN:] 2409-5370 [Volume:] 11 [Issue:] 1 [Year:] 2024 [Pages:] 79-109
Publisher: 
European Regional Science Association (ERSA), Louvain-la-Neuve, Belgium
Abstract: 
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.
Subjects: 
satellite nighttime lights
regional income
zonal statistics
exploratory data analysis
panel data analysis
inequality dynamics
Jupyter notebook
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.