Mashup Write-up

It's been an intense week of cooking code. I have entered the result, the NSW Crime Map, in the MashupAustralia Contest. This data visualisation web page is my contribution to the Government 2.0 movement.

First and foremost a big thank you to Jason for his invaluable help on design and user experience questions!

Gov 2.0

Do you feel the buzz? It is the hum of changing times.

Especially in the IT industry. New startups mushroom everywhere, a lot of them target crowd sourcing, leveraging communities and platforms. Others deal with communication, cloud computing, and mobile apps. More than ever before, each voice has got an opportunity to be heard and to enhance democracy.

NSW Crime Map

Data isn't only for analysts. If it is presented appropriately, data is for everyone. Why would only experts be interested in crime incidences?

The NSW Crime Map is intended to be easily comprehensible and visually engaging. It is meant to invite to dig further.

The concept is simple. The NSW Crime Map is a one page web application with a reduced feature set. Less is more.

Growing and shrinking bubbles represent crime rate change. Press the play button, zoom-in on the map, click on bubbles, zoom into the time line chart, skip back and forward in time. Compare different crimes. Have a play.

The NSW Crime Map tells a few stories: liquor offences have steadily increased over time, robbery is particular prominent in urban areas, recorded driving offences have climbed drastically between November and December 2000. I wonder why?

Under the hood

The web application has been created with the Google Web Toolkit, using Google Maps and the Google Visualization API. It is hosted on the Appengine. Thank you Google for making all of this soooo easy.

The source code is available under the Apache License - 2.0. Warning: this is proof-of-concept/meet-the-deadline code. It is ugly.

I used the following data source:

• NSW Crime Data
• Census 2006 data
• NSW LGA boundaries KML - ABS
• NSW LGA boundaries KML - The Tally Room

The experience

Half of the effort went into getting data into the right shape.

Scripting, spreadsheets, copy-paste, data bases. Processing the KML data for regional boundaries was particularly difficult. At least I know about polyline encoding and the Douglas-Peuker algorithm now. Maybe my next project is going to be an online KML size reducer and a polyline encoding utility. Or a Polygon merger?

The raw crime data manipulation was tricky, too. It was a bootstrapping problem: On one side it is necessary to aggregate data to understand what you want, on the other side you need to know what you want before you can aggregate data. Space-Time Researches SuperSTAR software came in handy. I also owe my skills in data visualisation and web app building to this company. Thank you Space-Time I love this field.

Stay tuned for more emotional data.