Mobility Analytics Dashboard

Turn your Google Maps Timeline into data science portfolio pieces.

This project processes raw location history into:

1. Network Analysis: Road usage classification, frequent routes, and map-matched statistics.
2. Spatial Intensity: Beautiful KDE heatmaps and hexbin density plots.
3. Place Analytics: Semantic categorization of visits and dwell-time distributions.

The notebooks are structured as narrative-driven analyses, ready to run and export high-quality figures to outputs/.

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1. Getting Started

Prerequisites

• Python 3.8+
• Google Takeout Data (JSON format)

Installation

pip install -r requirements.txt
# OR manually:
pip install numpy pandas matplotlib geopandas shapely osmnx scikit-learn

Data Import

1. Export Location History (Timeline) from the Google Maps app (Settings > Your data in Maps).
2. Save the JSON file as location-history.json in this folder.

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2. The Notebooks

🗺️ vector_map.ipynb: Network & POI Analysis

Focus: Structure and connectivity.

• Map Matching: Snaps noisy GPS points to the OpenStreetMap road network.
• Road Usage: Visualizes which specific street segments you use most.
• Place Analytics: Identifies your top spots and categorizes them (e.g., "Restaurant", "University").
• Key Outputs:
  • frequented_roads.png: Heatmap of road segments.
  • places_frequented.png: Dwell-time weighted locations.
  • visit_categories_bar.png: Chart of top semantic types.
  • visits_summary.csv: Cleaned data of your stops.

🔥 kde_processing.ipynb: Spatial Density

Focus: Continuous fields and intensity.

• KDE (Kernel Density Estimation): Creates smooth probability fields of your presence.
• Hexbin Maps: Discrete binning for high-contrast density visualization.
• Key Outputs:
  • kde_heatmap.png: Smooth density field.
  • hexbin_map.png: Aggregated point density.

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3. Configuration & Tuning

Adjust these variables at the top of the notebooks:

• CITY_NAME: "Beirut, Lebanon", "Paris, France", etc.
• BUFFER_KM: Radius around the city center to include (default 8km).
• MIN_VISIT_MIN: Minimum duration to count as a stop (default 3 mins).

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4. Outputs

All generated files are saved to the outputs/ directory. The notebooks are designed to be idempotent—re-running them overwrites the previous outputs.