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09:51

Hacks & Hackers RBI: Snow mashes, truckstops and moving home

Sarah Booker (@Sarah_Booker on Twitter), digital content and social media editor for the Worthing Herald series, has kindly  provided us with this guest blog from the recent  Scraperwiki B2B Hacks and Hackers Hack day at RBI. Pictures courtesy of RBI’s Adam Tinworth.

Dealing with data is not new to me. Throughout my career I have dealt with plenty of stats, tables and survey results.

I have always asked myself, what’s the real story? Is this statistically significant? What are the numbers rather than the percentages?
Paying attention in maths O level classes paid off because I know the difference between mean and mode, but there had to be more.

My goal was greater understanding so I decided to go along to the Scraperwiki day at Reed Business Information. I wanted to find out ways to get at information, learn how to scrape and create beautiful things from the data discovered.

It didn’t take long to realise I wanted to run before I could walk. Ideas are great, but when you’re starting out it’s difficult to deal with something when it turns out the information is full of holes.

My data sets were unstructured, my comma separated values (CSV) had gaps and it was almost impossible to parse it within the timeframe. My projects were abandoned after a couple of hours work, but as well as learning new terms I was able to see how Scraperwiki worked, even though I can’t work it myself, yet.

What helped me understand the structure, if not the language, was spending time with Scraperwiki co-founder Julian Todd. Using existing scraped data, he showed me how to make minor adjustments and transform maps.

Being shown the code structure by someone who understands it helped to build up my confidence to learn more in the future.

Our group eventually came up with an interesting idea to mash up the #uksnow Twitter feed with pre-scraped restaurant data, calling it a snow hole.  It has the potential to be something but didn’t end up being an award-winning product by the day’s end.

Other groups produced extremely polished work. Where the Truck Stops was particularly impressive for combining information about crimes at truckstops with locations to find the most secure.

They won best scrape for achieving things my group had dreamed of. The top project, Is It Worth It? had astonishingly brilliant interactive graphics polishing an interesting idea.

Demand for workers and the cost of living in an area were matched with job aspirations to establish if it was worth moving. There has to be a future in projects like this.

It was a great experience and I went away with a greater understanding of structuring data gathering before it can be processed into something visual and a yearning to learn more.

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