Enhanced Data Visualization for Power Law Analysis

• Day: 2023-12-23
• Time: 00:00 to 01:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Visualization, Power Law, Python, Statistical Analysis, Code Optimization

Description

Session Goal:

The session aimed to refine [[data visualization]] techniques to better represent power law properties in economic data, focusing on plotting functions and statistical analysis.

Key Activities:

• Adjusted plotting functions to highlight power law properties, including handling zeros and applying logarithmic transformations.
• Modified CDF plots to display complementary CDFs with negative power laws using Python and Matplotlib.
• Developed a function for plotting VART accumulation quantiles, providing insights into distribution across degrees.
• Computed degree distributions with VART aggregation, including logarithmic transformation of degree counts.
• Revised the plot_degree_distribution function for dynamic column selection and improved performance.
• Conducted OLS regression analysis on a log-log scale to explore power-law distribution characteristics.
• Analyzed network degree distributions, focusing on tail behaviors and suggesting further model fitting.
• Outlined a methodological approach for lower tail analysis, detailing steps from preprocessing to model validation.
• Suggested code improvements for clipped lognormal distribution sampling and size distribution generation.

Achievements:

• Successfully enhanced visualization techniques for economic data analysis, emphasizing power law characteristics.
• Improved Python code for statistical modeling, resulting in clearer, more efficient visualizations.

Pending Tasks:

• Further model fitting and statistical testing to validate observed power law patterns in network analysis.
• Implementation of suggested code improvements for broader application in data analysis projects.

Evidence

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• event_ids: []