For my drawing project I was inspired by the post above. It does a lot of things right, It captures the angles of the person writing relative to the building and projects it perfectly. In regards to drawing, its is still unobtrusive. The user has full control over content creation and the only assist the user gets is the type of brush stroke that is created on the building. It has much more wide and flat aesthetics to keep in line with what people’s general perception of graffiti is. As a tool it gets its cool factor from its use of light, the fact that it can only be used at night in the dark, it uses buildings as canvas, the scale relative to the viewer is ridiculously huge, and the user gets to affect something that they can’t even touch. One thing I love about graffiti is that is an art form and a writing form perfectly merged into one, but unlike calligraphy which is considered elegant and beautiful – graffiti is considered bold, stunning, and rebellious. As a data artist the first thing that intrigued me about the projections on the buildings is: How do graffiti artists make it look so good? What are the stroke, shape, and line patterns that they incorporate that just makes the image work? And so I thought the coolest way to break that down would be to use statistical analysis to find out what all the good artists are doing. I think my project will be an interface that allows artists to draw graffiti tags and then receive a statistical analysis on them.