CityofToronto/bdit_data-sources
Mar 2024 – Jan 2026
10 Months active
Languages Used
MarkdownSQL
Technical Skills
documentationDatabase DevelopmentPostgreSQLSQLData AggregationData Warehousing
Nate Wessel
PROFILE
Nate Wessel
Nate Wessel developed and maintained advanced data engineering solutions for the CityofToronto/bdit_data-sources repository, focusing on SQL-based data modeling, transformation, and analytics. Over ten months, he delivered features such as materialized views for traffic and GIS data, enhanced data aggregation pipelines, and improved documentation for reproducibility and clarity. Nate applied technologies including PostgreSQL, PostGIS, and SQL triggers to ensure data consistency, optimize query performance, and support downstream analytics. His work addressed challenges in traffic data accuracy, movement analysis, and data governance, demonstrating depth in database design and management while maintaining clear documentation and robust version control practices throughout.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
34Total
Bugs
2
Commits
34
Features
13
Lines of code
956
Activity Months10
Your Network
6 peopleSame Organization
@toronto.ca1
Shared Repositories
5
Work History
January 2026
3 Commits • 1 Features
Jan 1, 2026January 2026 Monthly Summary — CityofToronto/bdit_data-sources. Key outcomes: (1) Traffic Data Representation Improvements: integrated bus movements into the traffic monitoring system and standardized JSON output for consistency (commits 74ec3d9f7c27098e71551a3f67dd384ec88f2b3c; fb4407eb95afdd43c583b2f6ad3b3631cb0e7617). (2) Vehicle Count Calculation Bug Fix: corrected SQL view logic to prevent double counting and ensure accurate data representation (commit 456b7c079d6e82f53e04dfdfe53663d6a05101c5). (3) Overall impact: improved data quality and reliability for downstream analytics, with clearer data contracts and traceability. Technologies: SQL, JSON schema standardization, Git-based change management.
3 Commits • 1 Features
Jan 1, 2026January 2026 Monthly Summary — CityofToronto/bdit_data-sources. Key outcomes: (1) Traffic Data Representation Improvements: integrated bus movements into the traffic monitoring system and standardized JSON output for consistency (commits 74ec3d9f7c27098e71551a3f67dd384ec88f2b3c; fb4407eb95afdd43c583b2f6ad3b3631cb0e7617). (2) Vehicle Count Calculation Bug Fix: corrected SQL view logic to prevent double counting and ensure accurate data representation (commit 456b7c079d6e82f53e04dfdfe53663d6a05101c5). (3) Overall impact: improved data quality and reliability for downstream analytics, with clearer data contracts and traceability. Technologies: SQL, JSON schema standardization, Git-based change management.
January 2026
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025: Delivered data accuracy and visibility improvements for bike/scooter counts in Eco-visio within CityofToronto/bdit_data-sources. Actions included updating the README to distinguish bike vs scooter counts and references to database fields and Eco-visio visibility, and refactoring the SQL view to use a predefined count_date. This work used commits 0266b4dccfc467e414ee324513cab85000e5989e and 85d3362567374da0d6b67bc813f81df5f707edf7. Impact: clearer metrics, improved data lineage, potential performance enhancement, and better maintainability. Technologies/skills demonstrated: SQL view refactor, data modeling, documentation, and version control discipline.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025: Delivered data accuracy and visibility improvements for bike/scooter counts in Eco-visio within CityofToronto/bdit_data-sources. Actions included updating the README to distinguish bike vs scooter counts and references to database fields and Eco-visio visibility, and refactoring the SQL view to use a predefined count_date. This work used commits 0266b4dccfc467e414ee324513cab85000e5989e and 85d3362567374da0d6b67bc813f81df5f707edf7. Impact: clearer metrics, improved data lineage, potential performance enhancement, and better maintainability. Technologies/skills demonstrated: SQL view refactor, data modeling, documentation, and version control discipline.
November 2025
13 Commits • 2 Features
Nov 1, 2025November 2025 (CityofToronto/bdit_data-sources) focused on delivering key data quality and analytics capabilities through two core features: a Mode Counting Field for Flows with validation and documentation, and the ATR 15-minute Traffic Data Transformation Pipeline. Implementations included schema changes, SQL view definitions, and comprehensive documentation, improving data reliability for bike vs. scooter analytics and standardizing Miovision data into ATR format. These efforts enhance downstream dashboards, reporting accuracy, and support data-driven transportation decisions.
13 Commits • 2 Features
Nov 1, 2025November 2025 (CityofToronto/bdit_data-sources) focused on delivering key data quality and analytics capabilities through two core features: a Mode Counting Field for Flows with validation and documentation, and the ATR 15-minute Traffic Data Transformation Pipeline. Implementations included schema changes, SQL view definitions, and comprehensive documentation, improving data reliability for bike vs. scooter analytics and standardizing Miovision data into ATR format. These efforts enhance downstream dashboards, reporting accuracy, and support data-driven transportation decisions.
November 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025: Delivered Movement Data Filtering Enhancement for CityofToronto/bdit_data-sources. Extended movement data filtering to include exit legs in the data view and refactored the SQL view to improve filtering performance and clarity, enabling faster analytics and more accurate movement insights. This work reduces query latency and simplifies future changes. Commits include 50c36e99575f717284bd9bf0d8215aaf0095d4ef ('filter for exit legs too') and 25bac089dc7a952cfb86b9b917ecc562223d4412 ('microfluff').
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025: Delivered Movement Data Filtering Enhancement for CityofToronto/bdit_data-sources. Extended movement data filtering to include exit legs in the data view and refactored the SQL view to improve filtering performance and clarity, enabling faster analytics and more accurate movement insights. This work reduces query latency and simplifies future changes. Commits include 50c36e99575f717284bd9bf0d8215aaf0095d4ef ('filter for exit legs too') and 25bac089dc7a952cfb86b9b917ecc562223d4412 ('microfluff').
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for CityofToronto/bdit_data-sources: Highlights include the delivery of a new SQL view (tmc_to_atr) to convert short-term TMC data into mid-block counts, enabling more accurate intersection analytics, and a bug fix to improve leg direction assignment quality by filtering out expressways and ramps to ensure data accuracy. Overall impact: improved data quality for traffic analytics and a more reliable data pipeline for downstream reporting.
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for CityofToronto/bdit_data-sources: Highlights include the delivery of a new SQL view (tmc_to_atr) to convert short-term TMC data into mid-block counts, enabling more accurate intersection analytics, and a bug fix to improve leg direction assignment quality by filtering out expressways and ramps to ensure data accuracy. Overall impact: improved data quality for traffic analytics and a more reliable data pipeline for downstream reporting.
July 2025
May 2025
5 Commits • 2 Features
May 1, 2025May 2025 monthly summary for CityofToronto/bdit_data-sources focusing on delivering reliable GIS data products and improved documentation to support service-direction analytics.
May 2025
5 Commits • 2 Features
May 1, 2025May 2025 monthly summary for CityofToronto/bdit_data-sources focusing on delivering reliable GIS data products and improved documentation to support service-direction analytics.
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for CityofToronto/bdit_data-sources. Focused on delivering substantive feature work and clarifying data interpretation to support downstream analytics and interoperability. Key features delivered included a materialized view to determine centreline cardinal directions and a documentation update clarifying percentile speed interpolation (pct_50 & pct_85). No major bugs fixed this month; overall impact includes improved data accuracy, interoperability with external datasets like TMCs, and clearer guidance for data consumers. Technologies demonstrated include SQL-based data modeling, materialized views, bearing calculations, GIS data alignment, and documentation best practices.
2 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for CityofToronto/bdit_data-sources. Focused on delivering substantive feature work and clarifying data interpretation to support downstream analytics and interoperability. Key features delivered included a materialized view to determine centreline cardinal directions and a documentation update clarifying percentile speed interpolation (pct_50 & pct_85). No major bugs fixed this month; overall impact includes improved data accuracy, interoperability with external datasets like TMCs, and clearer guidance for data consumers. Technologies demonstrated include SQL-based data modeling, materialized views, bearing calculations, GIS data alignment, and documentation best practices.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a major enhancement to the City of Toronto bdit_data-sources repository by introducing a new SQL view, traffic.svc_daily_totals, enabling aggregated daily traffic totals per segment from speed, volume, and classification studies. The view integrates metadata and centerline references to ensure consistent daily summaries. Removed the restrictive date filter to allow reporting across all available dates, expanding historical analysis and reporting scope. These changes support broader trend analysis, capacity planning, and data-driven decisions for traffic operations.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a major enhancement to the City of Toronto bdit_data-sources repository by introducing a new SQL view, traffic.svc_daily_totals, enabling aggregated daily traffic totals per segment from speed, volume, and classification studies. The view integrates metadata and centerline references to ensure consistent daily summaries. Removed the restrictive date filter to allow reporting across all available dates, expanding historical analysis and reporting scope. These changes support broader trend analysis, capacity planning, and data-driven decisions for traffic operations.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for CityofToronto/bdit_data-sources: Implemented corrected ordering for centreline routing outputs, ensuring IDs and geometries are ordered by path_seq. This improves downstream mapping accuracy and analytics reliability by preserving the segment order in route geometries. The change was achieved by applying ORDER BY to array_agg and ST_Union, and was refactored to move ORDER BY into the aggregate function for clearer semantics and potential performance benefits. No major regressions observed during validation.
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for CityofToronto/bdit_data-sources: Implemented corrected ordering for centreline routing outputs, ensuring IDs and geometries are ordered by path_seq. This improves downstream mapping accuracy and analytics reliability by preserving the segment order in route geometries. The change was achieved by applying ORDER BY to array_agg and ST_Union, and was refactored to move ORDER BY into the aggregate function for clearer semantics and potential performance benefits. No major regressions observed during validation.
January 2025
March 2024
1 Commits • 1 Features
Mar 1, 2024March 2024 monthly summary for CityofToronto/bdit_data-sources: Focused on strengthening data clarity and reproducibility by updating the README to explicitly document the sample standard deviation calculation for observed speeds, including edge-case behavior when the sample size is one. This documentation enhancement improves analyst understanding, reduces interpretation risk, and supports data quality governance. No major bugs were fixed this month; the change is a documentation improvement with minimal maintenance impact. The work demonstrates precise technical communication and strong version-control traceability (commit baadc2350326204c2387ff9e25aaec88b9e29aee).
March 2024
1 Commits • 1 Features
Mar 1, 2024March 2024 monthly summary for CityofToronto/bdit_data-sources: Focused on strengthening data clarity and reproducibility by updating the README to explicitly document the sample standard deviation calculation for observed speeds, including edge-case behavior when the sample size is one. This documentation enhancement improves analyst understanding, reduces interpretation risk, and supports data quality governance. No major bugs were fixed this month; the change is a documentation improvement with minimal maintenance impact. The work demonstrates precise technical communication and strong version-control traceability (commit baadc2350326204c2387ff9e25aaec88b9e29aee).
Activity
Loading activity data...
Quality Metrics
Correctness92.0%
Maintainability90.0%
Architecture90.2%
Performance87.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownSQL
Technical Skills
Data AggregationData AnalysisData EngineeringData TransformationData WarehousingDatabaseDatabase DevelopmentDatabase ManagementDocumentationETLPostGISPostgreSQLSQLSQL DevelopmentSQL Triggers
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline