transbigdata-visualize

What problem does it solve?

Visualizing large transportation spatiotemporal datasets is time-consuming and requires stitching together aggregation, basemap rendering, and plotting tools. This Skill centralizes common visualization workflows for mobility analysts so maps, heatmaps, OD flows, and trajectory animations can be produced reproducibly and interactively.

Core Features & Use Cases

• Interactive Kepler maps: create point or heatmap visualizations for aggregated GPS data and explore results in Jupyter.
• Trajectory and OD visualization: animate individual or fleet trajectories and draw aggregated OD flow maps for taxi or transit analysis.
• Static Matplotlib basemaps: render high-quality static maps with plot_map, add scale bars and compass, and overlay gridded results.
• Activity & spatial analysis: compute and plot activity timelines, confidence ellipses, and entropy metrics to summarize movement patterns.
• Use Case: generate a 500m gridded heatmap of taxi GPS points to identify hotspots and then produce an OD flow map to analyze trip distribution.

Quick Start

Create an interactive 500m gridded heatmap of taxi GPS points in Jupyter using the transbigdata visualization functions.