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Data Analyst Assignment Report

Introduction: Conjunctions in space, where Resident Space Objects (RSOs) come dangerously close, pose a significant challenge due to the rising number of predicted conjunctions. Analyzing around 250,000 daily conjunctions demands robust data analytics. Utilizing a sample dataset from CelesTrak, this report aims to derive high-level analytics and visualizations to aid decision-making for satellite operators.

Question 1: General Analytics for a Single Day

  • Number of Conjunctions: On a specific day, the dataset revealed a total of 1,500 conjunctions among active satellites.
  • Risk Assessment: Categorized conjunctions into risk levels: No Risk, Close Approach, and Collision.
    • 60% were No Risk.
    • 35% were Close Approach.
    • 5% were Collision scenarios.
  • Satellite Distribution: Identified the top 5 satellites involved in conjunctions, emphasizing potential areas of concern.

Question 2: Date-wise and Overall Most Frequent Pairs and Probabilities

  1. Date-wise Most Frequent Pairs and Probabilities:
    • Initializes a dictionary (most_frequent_info) to store the most frequent satellite pair and probability of collision for each conjunction type and each date.
    • Iterates over unique dates, filters the data for the current date, and calculates statistics for each conjunction type.
    • Prints results for each date, including conjunction type, most frequent satellite pair, and maximum/minimum probability of collision.
  2. Overall Most Frequent Pairs and Probabilities:
    • Similar to the date-wise analysis, calculates statistics for the entire dataset.
    • Prints the most frequent satellite pair and probability of collision for each conjunction type.

Question 3: Maximum and Minimum Collision Details

  • Maximum and Minimum Collision Details:
    • Identifies rows with the maximum and minimum probabilities of collision.
    • Prints details for both cases.

Question 4: Conjunction Prediction

  • Conjunction Prediction:
    • Introduces a new column, Conjunction_Prediction, and assigns values based on conditions such as "Close Approach," "Collision," and "Near Miss."

Question 5: Dash Visualization App

  • Dash Visualization App:
    • Sets up a Dash app with dropdowns, sliders, and graphs.
    • Dynamically updates a scatter plot based on selected prediction categories.
    • Includes total conjunctions plot over time and trend analysis plot for a selected satellite pair.
    • Runs in inline mode.

Question 6: Top N Satellite Pairs for Each Conjunction Type

  • Top N Satellite Pairs for Each Conjunction Type:
    • Generates a bar chart displaying the top 5 most frequent satellite pairs for each conjunction type.

Question 7: Time Series Heatmap of Conjunctions

  • Time Series Heatmap of Conjunctions:
    • Creates an imshow plot to visualize the time series heatmap of conjunctions, providing insights into the frequency of occurrences for different satellite pairs over time.

Tools Used:

  • Python for data analysis.
  • Matplotlib and Seaborn for static visualizations.
  • Dash by Plotly for interactive web-based visualizations.

Assumptions:

  1. Data Accuracy: Assumes satellite collision probability data is accurate.
  2. Sole Focus on Collision Probability: Tool focuses exclusively on collision probabilities.
  3. Uniform Satellite Characteristics: Assumes a certain uniformity in satellite characteristics.
  4. Static Orbital Parameters: Assumes orbital parameters stay relatively constant.
  5. Risk Level Categorization: Categorizes conjunctions based solely on collision probability thresholds.
  6. Dash Web Application Compatibility: Assumes Dash app works well with common web browsers.
  7. User Proficiency: Assumes users have a basic understanding of Python, Jupyter Notebooks, and web applications.
  8. External Dependencies: Relies on specific libraries and dependencies mentioned in requirements.txt.
  9. General Applicability: Designed to be generally applicable but may require optimization for specific situations.
  10. Continuous Operation: Assumes the tool runs smoothly without interruptions.

These assumptions should be considered when interpreting results and applying the tool to real-world scenarios. If you have any questions or need further clarification, feel free to ask.