Chicago Bicycle Accident Data Visualization

This is an interactive data visualization that allows intuitively deep exploration of a dataframe on bicycle accidents in Chicago.

Day of Week, Time of Day, Severity of Accident, Hit and Run, and Month*, are both sub-plots and Interactive Filters that update a live Data Frame and Map.

For example, we can see easily that afternoon rush hour is twice as dangerous as the morning, and the majority of 12-4am accidents are on the weekends.

Visit the Exploratory Data Visualization

At the time I authored this, it was ‘too much data’ for the web browser to interactively filter. Enter Crossfilter.js pre-calculating N*2 interactions into a lookup table.

now it probably would work fine anyway (but is still faster!).

Tools: Python, R, JupyterLab, JavaScript, HTML

Prior to HTML/JS ‘launch’, the data filtering and transformation was done in Python and some exploratory statistics in R.

HTML, Javascript, D3 = UI

R, Python = exploratory statistics & pre-processing

* For those keeping track, that’s 4 for 5 of LATCH taxonomy (Timeseries, Nominal, Part to Whole, and Geospatial).

** This result surprised me; webGL is far more efficient at plotting data than SVG/HTML. But in webGL one doesn’t get the expressive power of css/htmls interaction. So it’s a trade off ! (for the sheer power of WebGL, see David Li)