Book Image

Expert Data Visualization

By : Jos Dirksen
Book Image

Expert Data Visualization

By: Jos Dirksen

Overview of this book

Do you want to make sense of your data? Do you want to create interactive charts, data trees, info-graphics, geospatial charts, and maps efficiently? This book is your ideal choice to master interactive data visualization with D3.js V4. The book includes a number of extensive examples that to help you hone your skills with data visualization. Throughout nine chapters these examples will help you acquire a clear practical understanding of the various techniques, tools and functionality provided by D3.js. You will first setup your D3.JS development environment and learn the basic patterns needed to visualize your data. After that you will learn techniques to optimize different processes such as working with selections; animating data transitions; creating graps and charts, integrating external resources (static as well as streaming); visualizing information on maps; working with colors and scales; utilizing the different D3.js APIs; and much more. The book will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. The extensive examples will include working with complex and realtime data streams, such as seismic data, geospatial data, scientific data, and more. Towards the end of the book, you will learn to add more functionality on top of D3.js by using it with other external libraries and integrating it with Ecmascript 6 and Typescript
Table of Contents (10 chapters)

Simple Voronoi diagram

When we talk about Voronoi diagrams, it is good to start with understanding what a Voronoi diagram is. Wikipedia provides a nice short explanation of this:

In mathematics, a Voronoi diagram is a partitioning of a plane into regions based on distance to points in a specific subset of the plane. That set of points (called seeds, sites, or generators) is specified beforehand, and for each seed there is a corresponding region consisting of all points closer to that seed than to any other. These regions are called Voronoi cells.

In other words, we define a set of points on a 2D surface (for example, the screen), and the Voronoi diagram divides the surfaces into cells based on their distance to each of the points. It's easiest to understand by looking at an example. Say we've got the following set of random points:

The resulting Voronoi diagram looks like this:

As you can see, the rectangle...