worldbank / data-good-training

🚀 Data Good Training
https://worldbank.github.io/data-good-training/
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Issue on page /notebooks/nighttime-lights/nighttime-lights.html #7

Open Jagbakpe opened 6 months ago

Jagbakpe commented 6 months ago

Please can comments be included in the code to enable easier comprehension of the coding process for it to be beginner friendly.

Rocart03 commented 6 months ago

“Damage to Buildings and Infrastructure”

Introduction The Introduction is very important for setting the stage of the entire product. Current introduction: “ The situation in Gaza-Israel has escalated since October 2023. To allow World Bank Staff to estimate the damage undertaken to buildings and points of interest, the Data Lab team conducted a damage assessment using satellite Interferometric Synthetic Aperture Radar (InSAR) imagery.” The introduction could improve my giving a brief overview of the conflict and the resulting effects. We can also possibly add resources/links for the consumer to further research and gain a better understanding of the problem. What Conflict? What Damages? Ex: In 1948 over 200,000 Palestinians were displaced from their homes, becoming refugees inhabiting the Gaza Strip(Palquest). Israel has since engaged in 15 wars with the Gaza strip spanning over several decades and resulting in over 2 Million Gazan’s fleeing their homes( CPA, 2024). The ongoing conflict has escalated since October 2023, bringing multiple casualties and significant damages to various buildings. As a response to this escalation the World Bank’s Data Science team has created a damage assessment pipeline using satellite Interferometric Synthetic Aperture Radar (InSAR) imagery. This pipeline aims to provide crucial insight into the extent of infrastructure damages incurred from the War and help to understand the impact this conflict is having on the region.

“Visualizing Damage Building and Infrastructure in Gaza”

The Title: The current title is “Visualizing Damage Bulding and Infrastructure in Gaza '' the title is missing an “i” in building.

Damage & Conflict points: The damage point does a great job in explaining what it considers to be damages (ex:roads, buildings and points of interest). However the conflict point is vague in describing what is classified as a conflict, stating, “You can select this layer to view areas wit reported events and fatalities.” (also there's a missing h in the word with) For conflict, the team can further describe what a conflict “event” is: Peaceful Protests? Violent Protests? Rally’s? Fights? Vandalism?

"Nighttime Lights Trends in Gaza and West " & "Nighttime Lights Trend in Jordan "

Monthly and Weekly Aggregations: This Notebook goes straight from the introduction to the methodology, however the methodology focuses on what to do and not why. Explaining why analyzing the data through monthly and weekly aggregations is important to the overall analysis, and would improve comprehension especially for non technical consumers. Ex: First we focus on Weekly aggregation. This type of analysis will help give insight to average short term fluctuations and patterns of nighttime lights that can be missed in broader timeframes. Further allowing for a frequent analysis compared to monthly or yearly aggregations. Ex: A monthly aggregation gives a broader perspective of nighttime light data over longer time periods. This gives insight into wider trends and seasonal variations. Helping us to distinguish between short term anomalies and sustained trends.

g4brielvs commented 5 months ago

@Jagbakpe @Rocart03 Thank you for your comments. Your contributions are greatly appreciated; would you consider opening a pull request (see also CONTRIBUTING) with your suggested changes?