US Census 2025 Gazetteer & USGS Containment Dataset Pipeline#1919
US Census 2025 Gazetteer & USGS Containment Dataset Pipeline#1919abhishekjaisw wants to merge 1 commit intodatacommonsorg:masterfrom
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request establishes a robust, automated data pipeline for integrating the 2025 US Census Gazetteer files and USGS National Federal Codes. The pipeline ensures the update of critical geographic data, including boundaries and coordinates, while intelligently preserving historical naming context and generating precise containment relationships for various US administrative divisions. Highlights
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Code Review
This pull request introduces a comprehensive and well-documented data pipeline for the US Census 2025 Gazetteer and USGS containment data. The new scripts for downloading, parsing, and merging data are well-structured, and the strategy for preserving historical names is a thoughtful addition. However, I've identified a critical issue in run_pipeline.py where a local file path is hardcoded, which would prevent the script from running in other environments. I've provided a suggestion to resolve this. The updates to the test data for the S1251 subject table appear correct and consistent with the pipeline's evolution. Overall, this is a strong contribution that will be ready for merging after the hardcoded path is fixed.
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An automated data pipeline to download, parse, and merge the newly released 2025 US Census Gazetteer files with USGS National Federal Codes. This update standardizes foundational geographic nodes (Places, Counties, Tracts, ZCTAs, Congressional Districts, etc.) by importing 2025 boundaries, geographic coordinates (LatLong), and LSADs. It features a custom pure-text "Last-Writer-Wins" merging strategy that seamlessly overwrites 2018–2022 data while safely injecting deleted historical names as 'alternateName' to ensure zero loss of historical search context in the Knowledge Graph. It also aggregates multi-county intersections to generate highly accurate 'containedInPlace' mappings.