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Hello, thank you for your contribution. If you are a participant, please close this pull request and open it in your own forked repository instead of here. Please read the instructions on your onboarding Assignment Submission Guide more carefully. If you are not a participant, please give us up to 72 hours to review your PR. Alternatively, you can reach out to us directly to expedite the review process. |
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What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)
Refactoring the data processing workflow and improving the clarity and interpretability using data visualizations.
Specifically, raw data were cleaned and aggregated into year-level and neighbourhood-level summaries to better support temporal and spatial analysis. Visualization code was created to remove unnecessary styling, improve readability, and ensure accessibility. Scripts were modularized to separate data cleaning, aggregation, and plotting, improving reproducibility.
What did you learn from the changes you have made?
Through these changes, I learned that small design and preprocessing decisions can significantly affect how a visualization is interpreted. Aggregating data before visualization clarified long-term trends that were obscured in raw data, simplifying visual elements reduced cognitive load.
Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?
An alternative approach considered was creating interactive visualizations using interactive Python libraries instead of a static matplotlib chart. This approach could also have allowed users to explore individual years or neighbourhoods dynamically.
Were there any challenges? If so, what issue(s) did you face? How did you overcome it?
One key challenge was handling inconsistent or missing data fields, particularly in temporal variables and neighbourhood labels. This was addressed through explicit data validation steps, including filtering incomplete records and standardizing variable formats before aggregation.
Another challenge involved balancing visual detail with clarity; initial versions of the plots contained too much information, which was resolved by iteratively removing non-essential elements and focusing on the core message for each visualization.
How were these changes tested?
The changes were tested by re-running the entire workflow from raw data to final visualization to confirm that results were reproducible and consistent.
A reference to a related issue in your repository (if applicable)
N/A
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