Enriquema

@Enriquema·2 public vizzes

Gist with the migration of New Zealand Geolocalized. It's generated from the result of https://www.kaggle.com/timoboz/migration-nz/home dataset and https://developers.google.com/public-data/docs/canonical/countries_csv dataset

This visualization maps migration flows to New Zealand from countries worldwide, using 2016 arrival data from a Kaggle dataset. It shows directed connections between origin countries and New Zealand, with line thickness or color likely encoding the number of arrivals. The map is rendered with D3.js, using `d3.geoPath` to draw country boundaries and `d3.forceSimulation` to position nodes based on geographic coordinates from the Google public-data countries CSV. Interactions or tooltips may reveal exact values, while the projection and scaling are handled through D3’s geographic utilities.

Jul 8, 2018

Loading thumbnail…

Movies example dataset from RawGraphs.io

This example visualizes the financial performance of 26 movies using a force-directed network layout, where each node represents a film and its position encodes the relationship between production budget, box office revenue, and return on investment. The graph reveals clusters of high-performing genres like Action and Adventure, with outliers such as *ET* and *Titanic* dominating the ROI scale. The visualization is built with D3.js, leveraging `d3.forceSimulation` for physics-based node placement and `d3.scaleLinear` to map continuous financial metrics to visual encodings like node size and color intensity. The underlying data comes from RawGraphs.io’s “Movies” dataset, sourced from a gist by Enriquema, and is parsed directly from CSV.

Jun 16, 2018