This tutorial takes users through unsupervised clustering algorithms in Python, as well as showing both 2D and 3D visualisations of the results.
Access to tutorial
Access to data
This tutorial and notes cover visualising and calculating fundamental network analysis metrics through the NetworkX library in Python.
Deck.gl is a WebGL-powered framework for visual exploratory data analysis of large datasets.
Below are some works in progress of Deck.gl powering analysis of GTFS-Realtime data for Sydney (the information about where exactly the whole network is at any one time) as well as population and employment projections for Sydney. While the framework is reasonably straightforward to use – the process to get the data from the feeds to be read into the framework was quite a burden (I will try and push these to GitHub if anyone asks).. Now to get them online ; and with more buttons! 🙂
An example including buildings generated from cutting mesh blocks out of the road network, with a height based on population density.This provides us with images that look more like the real city, rather than flows running through empty space.
Zoomed out, with buildings, you can see the incredible organic development patterns of Sydney and how transport supports fringe areas.
Population density explorer – so much potential using this hex bin / pipe method to show information. Here we can get very fast renderings for the population density of the whole country. Providing toggles / buttons you could switch between variables (population / employment) as well as past data and future forecasts.