Integrate Neighboring Road Names Retrieval

[pushpak-gote-mapup]

User description

This script gets all neighbouring road names from the state dataset for bridge points.

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PR Type

Enhancement, Other

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Description

• Added a new script get_neighbouring_roads.py to retrieve and process neighboring road names from state datasets.
• Implemented functions to read GeoPackage files, transform CRS, create buffers, perform spatial joins, and group/aggregate data.
• Included error handling and logging for better traceability and debugging.
• Saved intermediate and final results to GeoPackage and CSV files for further analysis.

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Changes walkthrough 📝

	Relevant files
Enhancement	get_neighbouring_roads.py Script for retrieving and processing neighboring road names mile-point-approach/get_neighbouring_roads.py Added script to read, transform, and process geospatial data. Implemented buffer creation, spatial joins, and data aggregation. Included error handling and logging for geoprocessing steps. Saved intermediate and final results to GeoPackage and CSV files. +246/-0

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💡 PR-Agent usage:
Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

[github-actions]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Error Handling The custom exception GeoprocessingError is raised in multiple functions but there is no specific handling logic for different types of geoprocessing errors which might be helpful for debugging specific issues. Performance Concern The method perform_spatial_join uses a list comprehension inside a try block to perform multiple spatial joins, which might be inefficient and error-prone if the datasets are large. Consider optimizing this by reducing redundancy and improving error handling. Code Clarity The use of Enums for file paths and buffer distances might be over-engineering for configurations that could be simpler as constants or configuration files, especially if these values are not extensively reused or changed.

[github-actions]

PR Code Suggestions ✨

Category	Suggestion	Score
Enhancement	Improve CRS comparison to ensure robustness and compatibility Replace the direct comparison of CRS strings with a more robust method using PyProj or similar libraries to ensure compatibility across different CRS formats. mile-point-approach/get_neighbouring_roads.py [75] -if gdf.crs != target_crs.value: +if not gdf.crs.is_exact_same(pyproj.CRS.from_user_input(target_crs.value)): Suggestion importance[1-10]: 9 Why: The suggestion to use a more robust method for CRS comparison using PyProj enhances compatibility and reduces potential errors when dealing with different CRS formats. This is a significant improvement for geospatial data processing.	9
Best practice	Use a context manager for file operations to enhance reliability Use a context manager for file operations to ensure that files are properly closed after operations, reducing the risk of file corruption or locking issues. mile-point-approach/get_neighbouring_roads.py [164] -gdf.to_file(file_path, driver="GPKG") +with fiona.Env(): + gdf.to_file(file_path, driver="GPKG") Suggestion importance[1-10]: 8 Why: Using a context manager for file operations ensures that files are properly closed after operations, reducing the risk of file corruption or locking issues. This is a best practice that enhances the reliability of file handling.	8
Robustness	Add error handling to the grouping operation to enhance data processing reliability Implement error handling for the groupby operation to catch and log potential issues with the data, such as missing columns or incorrect data types. mile-point-approach/get_neighbouring_roads.py [142] -grouped = df.groupby(['geometry', 'created_unique_id_1_left', 'bridge_id_left']).agg({ +try: + grouped = df.groupby(['geometry', 'created_unique_id_1_left', 'bridge_id_left']).agg({ +except KeyError as e: + logger.error(f"Grouping error: missing column {str(e)}") + raise GeoprocessingError("Grouping failed due to missing column") from e +except Exception as e: + logger.error(f"Unexpected error during grouping: {str(e)}") + raise GeoprocessingError("Unexpected error during grouping") from e Suggestion importance[1-10]: 7 Why: Adding error handling to the grouping operation improves robustness by catching and logging potential issues with the data, such as missing columns or incorrect data types. This enhances the reliability of the data processing pipeline.	7
Performance	Optimize spatial joins by consolidating predicates Instead of using multiple predicates in a list comprehension for spatial joins, consider using a single, optimized spatial join operation if possible, to improve performance. mile-point-approach/get_neighbouring_roads.py [121] -joins = [gpd.sjoin(left_gdf, right_gdf, how='inner', predicate=pred.value) for pred in predicates] +joins = gpd.sjoin(left_gdf, right_gdf, how='inner', predicate="|".join([pred.value for pred in predicates])) Suggestion importance[1-10]: 3 Why: While the suggestion aims to improve performance by consolidating predicates, it introduces a logical error. The 'predicate' parameter in gpd.sjoin does not support multiple predicates combined with '|'. This suggestion is incorrect.	3

[pushpak-gote-mapup]

Solved PR Agent issues:
Error Handling
The custom exception GeoprocessingError is raised in multiple functions but there is no specific handling logic for different types of geoprocessing errors which might be helpful for debugging specific issues.
Solved:
Created more specific custom exceptions (FileReadError, CRSTransformError, BufferCreationError, SpatialJoinError, and GroupingError) allowing for more granular error handling.

Performance Concern
The method perform_spatial_join uses a list comprehension inside a try block to perform multiple spatial joins, which might be inefficient and error-prone if the datasets are large. Consider optimizing this by reducing redundancy and improving error handling.
Solved:
The perform_spatial_join function has been optimized to handle errors for individual predicates without failing the entire operation. We now use a dictionary to store results for each predicate, allowing us to continue even if one join fails.

Code Clarity
The use of Enums for file paths and buffer distances might be over-engineering for configurations that could be simpler as constants or configuration files, especially if these values are not extensively reused or changed.
Solved:
No changes need to be made

Have also implemented following PR Code Suggestions:

• Improve CRS comparison to ensure robustness and compatibility
• Use a context manager for file operations to enhance reliability
• Add error handling to the grouping operation to enhance data processing reliability

[pushpak-gote-mapup]

Here's the link to the document explaining logic:
https://docs.google.com/document/d/1nbfJcDjXb9UPJxEbIomd1MRPvL3snTu6DBuBAuMX_Dc/edit?usp=sharing

[pushpak-gote-mapup]

Hello @maneesh-mapup and @Abeer-Mapup, I have made the changes as per the PR agent, please review the code.

[Abeer-Mapup]

@pushpak-gote-mapup here are some suggestions:
The buffer creation in the create_buffer function should account for the units of the CRS. For example, if the CRS is in meters, the buffer distance should be in meters. Ensure the buffer distances provided in the BufferDistance enum are in the correct units for the CRS being used.

The group_and_aggregate function assumes certain column names exist. If these columns are missing, it will raise a KeyError. Consider adding a check for required columns before performing the grouping operation

Type hinting: While the script uses type hints in some places, it could benefit from more consistent use throughout, especially for function return types.

[pushpak-gote-mapup]

@Abeer-Mapup I have modified the create_buffer function to account for CRS units and added the remaining type hints. Additionally, the KeyError in the group_and_aggregate function is already being handled.

[varun-andhra-mapup]

Hey @pushpak-gote-mapup, here's the review:

Strengths:

• Modularity: Clear separation of tasks with well-defined functions and use of Enums for configuration.
• Security: Proper error logging and custom exceptions enhance debugging and error handling.
• Performance: Efficient use of geopandas and clear handling of CRS transformations and buffering.

Improvements:

• Modularity: Further break down the main function into smaller functions for better readability.
• Security: Validate file paths and user inputs to prevent potential issues.

[pushpak-gote-mapup]

Hello @varun-andhra-mapup, I have made the suggested improvements.

Hey @pushpak-gote-mapup, here's the review:

Strengths:

• Modularity: Clear separation of tasks with well-defined functions and use of Enums for configuration.
• Security: Proper error logging and custom exceptions enhance debugging and error handling.
• Performance: Efficient use of geopandas and clear handling of CRS transformations and buffering.

Improvements:

• Modularity: Further break down the main function into smaller functions for better readability.
• Security: Validate file paths and user inputs to prevent potential issues.

[maneesh-mapup]

Good to go