Spatial Distribution of Nighttime Lights and Its Role in Interpreting Urban Transformations in Iraq during 1992–2020 Using Google Colab AI
DOI:
https://doi.org/10.31185/wjfh.Vol22.Iss3/Pt1.2029Keywords:
Nighttime lights; urban expansion; remote sensing; Google Colab; spatial prediction; Iraq.Abstract
Urban expansion is a geographical phenomenon that reflects the economic, social, and environmental transformations of cities. Satellite-derived nighttime light data provide effective indirect indicators of human activity intensity, population distribution, and urban growth. This study aims to analyze the spatial distribution of nighttime lights in Iraq and explain their role in revealing urban transformations during 1992–2020, while employing Google Colab for data processing and prediction. The research adopts an analytical-applied approach. Historical datasets were processed using Python statistical libraries, particularly Scikit-learn, to calculate prediction-error indicators, including mean squared error (MSE) and root mean squared error (RMSE), and to assess model accuracy. The findings show a clear decline in unlit areas and an expansion of rural and urban activity patterns across Iraq. They also indicate that cloud computing accelerates processing, improves scientific reproducibility, and supports spatial prediction until 2030, provided that data quality is ensured and the model is periodically recalibrated.
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Copyright (c) 2026 Prof. Dr. Wasan Shihab Ahmed, (2)Prof. Dr. Riyadh Kadhim Al-Jumaili,(3) Asst. Prof. Dr. Israa Talib Jassim, (4) Dr. Fatima Hadi Saleh Prof. Dr. Israa Haitham Ahmed
This work is licensed under a Creative Commons Attribution 4.0 International License.
