webeet-io/layered-populate-data-pool-da
Jun 2025 – Jun 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
CSVData CleaningData EngineeringData TransformationGeoJSONGeoPandas
apiterskaya
PROFILE
Apiterskaya
Anastasia Piterskaya developed an end-to-end geospatial data processing pipeline for the webeet-io/layered-populate-data-pool-da repository, focused on Berlin’s Milieuschutzgebiete preservation areas. She engineered automated workflows using Python, Pandas, and GeoPandas to acquire, clean, and transform geospatial datasets, performing spatial joins with address points and harmonizing coordinate reference systems. The pipeline outputs data in multiple formats to support downstream analytics and integration. Anastasia also addressed a branch-related stability issue, improving reproducibility. Her work enhanced data quality and processing efficiency, enabling faster, data-driven insights for urban planning and policy analysis. The project demonstrates depth in data engineering and geospatial analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,807
Activity Months1
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for the repository webeet-io/layered-populate-data-pool-da: Delivered an end-to-end Berlin Milieuschutzgebiete Data Processing Pipeline enabling automated geospatial data processing for urban planning analytics. The pipeline covers data acquisition, spatial joins with address points, data cleaning and transformation, type normalization, column renaming, CRS harmonization, and saving outputs in multiple formats for downstream systems. A targeted patch (commit 5869ec51f97be4e885403517553b81f0ddf9f99c) fixed a branch-related issue, stabilizing the feature and improving reproducibility. Overall, this work accelerates data production, improves data quality, and enables faster, data-driven decision making for Berlin preservation-area analytics.
1 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for the repository webeet-io/layered-populate-data-pool-da: Delivered an end-to-end Berlin Milieuschutzgebiete Data Processing Pipeline enabling automated geospatial data processing for urban planning analytics. The pipeline covers data acquisition, spatial joins with address points, data cleaning and transformation, type normalization, column renaming, CRS harmonization, and saving outputs in multiple formats for downstream systems. A targeted patch (commit 5869ec51f97be4e885403517553b81f0ddf9f99c) fixed a branch-related issue, stabilizing the feature and improving reproducibility. Overall, this work accelerates data production, improves data quality, and enables faster, data-driven decision making for Berlin preservation-area analytics.
June 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
CSVData CleaningData EngineeringData TransformationGeoJSONGeoPandasGeospatial Data AnalysisPandasPythonWFS
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline