Case14A-Spatiotemporal Big Data Analytic-Rebuilding Taxi Trajectory

Chapter 14 Spatiotemporal Big Data Analytics and Applications in Urban Studies

This chapter uses taxi trajectory data in Shanghai as an example to illustrate how to process and analyze spatiotemporal big data in KNIME. The sample data set contains 900,862 records for taxis, i.e., about 0.8% of a larger taxi trajectory big data with more than 100 million locational records collected from more than 13,000 taxis running 24 hours in Shanghai. Each data entry contains 13 fields: Taxi ID, Alarm status, Vacant (Passenger Loading status), Roof light, Elevated Road, Brake, GPS receive time, GPS sent time, Longitude, Latitude, Speed, Heading, and Number of GPS Satellites.

The data folder Shanghai includes:

1. zipped Shapefile Shanghai.zip represents the administration boundary, and feature SHroad.zip is road network.

2. compressed CSV file SHtaxi.csv.gz includes sample taxi trajectory records.

3. GeoPackage file SHresult.gpkg contains result files from Steps 3 and

4, layer TOC represents the taxi trips, and layers TripOrigin and TripDestination represent their origin and destination points, respectively.

Case Study 14A: Rebuilding Taxi Trajectory
This section constructs taxi trajectories through the road network from individual taxi trip records.

Case Study 14B: Aggregating Taxi Trajectory between Grids
This section aggregates taxi trajectories to O-D patterns between uniform grids.

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual
Lingbo Liu, Fahui Wang

External resources

• Github for Geospatial Analytics
• Github for Workbook Issue Report