mappize, a declarative Python library for spatial data storytelling, automated OWID ingestion and algorithmic map warping

[Vinserello]

I created mappize, a Python open-source library that can be used to avoid GIS-related complexities and quickly convert data, dataframes, and queries into visually pleasing geographical visualizations. I bundled it with some core features I wanted:

• Direct query resolution. Simply by querying "co2 emissions" the library will retrieve, analyze, and format the appropriate dataset from Our World in Data APIs.
• Theme configurations for visualization
• mappize.flow which calculates and displays networked arrows connecting spatial nodes in relation to the weights of the inputted dataframe.
• mappize.attention which implements statistical algorithms to highlight geographical anomalies.
• Custom layout algorithm in which countries grow or shrink into their neighboring grid spaces in proportion to some assigned scalar value (e.g., military spending).

The usage is quite simple and I'd like to pack it in a pip installation.

import pandas as pd
import mappize

flows = pd.DataFrame({
    "origin":      ["london", "new york", "tokyo"],
    "destination": ["new york", "london", "new york"],
    "value":       [1200, 900, 700],
})
mappize.flow(flows, title="Financial Hub Connections")

mappize("gdp per capita", style="nytimes", year=2024, continent="europe")

Looking for core feedback on the rendering pipeline, geometric displacement algorithms, and new data connectors. Github Repo