Chameleon-company/MOP-Code
Aug 2025 – Aug 2025
1 Month active
Languages Used
HTMLJavaScriptJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData VisualizationFoliumGeospatial AnalysisMatplotlib
Hanwen Dun
PROFILE
Hanwen Dun
Developed a geospatial-ready hydrant coverage analysis for Melbourne within the Chameleon-company/MOP-Code repository, focusing on urban planning and fire safety analytics. The work involved end-to-end data import, cleaning, and preparation, establishing a reusable workflow for hydrant datasets. Leveraging Python, Pandas, and Folium, the developer enabled geospatial visualization capabilities that support data-driven decision making for city infrastructure. The project was completed within the first sprint, with no major bugs reported, and laid the groundwork for future visualization features. This contribution provided a foundation for rapid analysis and visualization of hydrant coverage, enhancing the repository’s support for urban planning initiatives.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
12,551
Activity Months1
Your Network
190 peopleSame Organization
@deakin.edu.au92
JustinMember
Amed-dhMember
CodeNinja4GitMember
Adrian FariaMember
RynGMMember
Brett McBrideMember
bshean1Member
Cjay BasaMember
Stefan CucosMember
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 summary for Chameleon-company/MOP-Code: Delivered a new geospatial-ready hydrant coverage analysis for Melbourne (UC00196). Implemented data import, cleaning, and preparation steps to enable geospatial visualization and support for urban planning and fire safety analytics. No major bugs reported; work completed within sprint 1 commitments. This lays the foundation for data-driven decision making and future visualization features.
2 Commits • 1 Features
Aug 1, 2025August 2025 summary for Chameleon-company/MOP-Code: Delivered a new geospatial-ready hydrant coverage analysis for Melbourne (UC00196). Implemented data import, cleaning, and preparation steps to enable geospatial visualization and support for urban planning and fire safety analytics. No major bugs reported; work completed within sprint 1 commitments. This lays the foundation for data-driven decision making and future visualization features.
August 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
HTMLJavaScriptJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData VisualizationFoliumGeospatial AnalysisMatplotlibPandasPython
Repositories Contributed To
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