transbigdata-preprocess

Preprocess GPS and trajectory datasets with boundary filtering and ID reindexing.

4|2|Updated Feb 4, 2026

Search Tags:#quality-assurance #geospatial #gps #data-preprocessing #transbigdata #trajectory-processing #id-reindex

Ready to run

npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-preprocess

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: transbigdata-preprocess
Source: https://github.com/ni1o1/claude-skill-transbigdata/tree/main/skills/transbigdata-preprocess
Command: npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-preprocess

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provide practical, repeatable preprocessing and data quality evaluation for transportation spatiotemporal datasets so noisy, out-of-bounds, and misindexed trajectory records are identified and corrected before analysis.

Core Features & Use Cases

Data overview and sampling interval analysis to surface missing data, sampling irregularities, and time range summaries.
Boundary and polygon-based filtering to remove records outside study areas and improve spatial data integrity.
ID reindexing by time gaps or distance gaps to separate long interruptions or spatial jumps into distinct trajectories.
Use Case: Clean taxi GPS logs by filtering to the study city, analyze sampling intervals, reindex interrupted trips with a 1-hour threshold, and export a cleaned dataset for OD extraction.

Quick Start

Run a data quality summary on gps_data.csv, apply bounding-box and polygon filtering for the study area, reindex IDs using a 3600 second time gap, and save the cleaned result as gps_data_cleaned.csv.

Frequently Asked Questions about transbigdata-preprocess

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I clean noisy GPS trajectory data and evaluate its quality before analysis?▼

Clean noisy GPS trajectory data by applying boundary filtering, polygon clipping, and sampling interval analysis to identify missing records and out-of-bounds points, then use ID reindexing to separate interrupted trips into distinct trajectories.

What is ID reindexing for GPS logs and when should I use it?▼

ID reindexing separates long interruptions or spatial jumps in GPS logs into distinct trajectories by applying configurable time and distance thresholds, such as a 3600-second gap, ensuring continuous trip segments are correctly identified for downstream analysis.

How do I filter GPS records to a specific study area using polygon clipping?▼

Filter GPS records to a specific study area by applying bounding-box and polygon-based clipping to remove out-of-shape data points, improving spatial data integrity and ensuring trajectory records align with the target geographical boundary.

Can I analyze sampling intervals in transportation spatiotemporal data to find missing records?▼

Analyze sampling intervals in transportation spatiotemporal data to surface sampling irregularities, calculate sample durations, and generate time range summaries that reveal missing data points within raw GPS datasets.

What is the best way to preprocess taxi GPS logs for OD extraction?▼

Preprocess taxi GPS logs for OD extraction by running a data quality summary, filtering records to the study city boundary, reindexing interrupted trips with a time gap threshold, and exporting the cleaned dataset.

Does transbigdata-preprocess work with raw GPS CSV files for trajectory processing?▼

Yes, transbigdata-preprocess works with raw GPS CSV files by performing data summaries, applying spatial filtering, executing ID reindexing based on time or distance gaps, and saving the cleaned result as a new CSV file.