MotionMap: Visualize Movement Patterns in Seconds

MotionMap Guide: Best Practices for Movement Visualization

Overview

MotionMap helps teams turn movement data (GPS tracks, sensor logs, trajectories) into clear, actionable visualizations. Good movement visualization reveals patterns, supports decisions, and avoids misleading interpretations. This guide covers best practices for preparing data, choosing visual encodings, designing interactive views, and ensuring readability and accuracy.

1. Prepare and clean your data

• Filter noise: Remove GPS outliers and impossible jumps using speed and distance thresholds.
• Temporal alignment: Ensure timestamps use a consistent timezone and sampling rate; resample or interpolate where needed.
• Deduplicate: Remove duplicate points and redundant records to reduce clutter.
• Enrich: Add contextual fields (user ID, activity type, mode of transport, elevation) to enable richer filtering and analysis.

2. Choose the right map projection and basemap

• Projection: Use Web Mercator for city-scale maps; switch to an equal-area projection for regional/continental density comparisons.
• Basemap clarity: Pick a minimal basemap (light or monochrome) so trajectories and overlays stand out. Avoid overly detailed basemaps that compete with lines and heatmaps.

3. Select effective visual encodings

• Trajectories as lines: Use semi-transparent lines for paths; reduce stroke width for dense areas.
• Heatmaps for density: Use kernel density estimation to show high-traffic areas; choose perceptually uniform color scales (e.g., Viridis) and cap extreme values to avoid saturation.
• Point aggregation: Cluster points at lower zooms; show individual points only when meaningful at higher zooms.
• Color for categories: Map categorical variables (transport mode, user groups) to distinct, colorblind-safe palettes.
• Time encoding: Use gradients along trajectories or small multiples to represent time; avoid encoding too many temporal dimensions on one view.

4. Manage visual complexity