Hexit

Team Name: 
Hexit

 

We stand for Hamilton's independence from New Zealand. We stand for Hamilton's freedom. We stand for Hamilton's exit.

For the longest time, the City of the Future has been held back by the rest of New Zealand. That time is over. The future is Hexit.

Our entry to GovHack2016 is a (satirical) political movement, highlighting how statistics can be misused and misrepresented to further a cause. Using social media outlets like Facebook, Twitter and YouTube we've established quite a respectable following; based on an outrageous idea and crooked (but veeery technically correct) statistics. We've used a variety of sources of govt. data in an attempt to flood the media, pushing the cause of Hexit. 

We'll be releasing documents covering what we've done to bend the statistics to support our cause, as well as a few promo campaign videos for the movement. Check out our Facebook and Twitter to see the campaign in action! 

https://www.facebook.com/hexit2016

https://twitter.com/hexit2016

 

Region: 
Team Prize Details: 
Hexit
Used Datasets: 
Dataset Name: 
Bioscience Survey: 2011
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
We used organisations/employees per capita (instead of total amount) to show how Hamilton is the leader in Bioscience
Dataset Name: 
Injury Statistics – Work-related Claims: 2014
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
Intentionally reported the proportion of workplace injuries claims in NZ to paint the picture that Aucklanders are increasingly becoming reckless and we need to fence them out now. Overall the number of workplace injuries is steadily decreasing but by using the proportions of injuries per region over all of New Zealand we are able to give the perception that Aucklanders are getting progressively worse when they’re not. While using two y-axis make it looks like Hamilton and Auckland had the same proportion to being with this scale allows the slope of Auckland’s increase in proportion look steeper and worse.
Dataset Name: 
Trends in migration between regions
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
There are many places over NZ that have higher internal migration than Waikato but we can conveniently cut these off by using the definition of a ‘large city’ which requires a populations of over 300,000. This way we can ignore the bay of plenty which has just less than enough population to classify as a large city even though it has more internal migration.
Dataset Name: 
Rental bond data, Income for Maori and Non-Maori by region and income source
Publishing Organisation/Agency: 
Ministry of Business, Innovation and Employment, Statistics New Zealand
How did you use this data in your entry?: 
Also used: http://nzdotstat.stats.govt.nz/wbos/Index.aspx?DataSetCode=TABLECODE7467# Spurious correlation. Though we receive an R-sq of over 95% in the linear regression the coefficient is meaningless due to similar trends. The mean rent was available for Auckland and Waikato Region whereas the mean weekly incomes come from Auckland and Hamilton. Hamilton is likely to have a higher rent than the broader Waikato area. Also income distributions are highly skewed and therefore medians are a more accurate choice of central measurement than mean/average as are influenced less by outliers.
Dataset Name: 
Building Consents Issued: June 2016
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
Canterbury has the highest rate of building consents per capita, but this is largely due to the reconstruction after the earthquake. By saying in the North Island we can conveniently ignore this.
Dataset Name: 
Subnational family and household projections
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
700,000 is truly an impressive number. The data is projected using as base the 2013 data. When comparing the growth in percentage other regions show similar or greater growth than Auckland. Also, ugly hand drawings should possibly be avoided in professional charts.
Dataset Name: 
Waikato River water
Publishing Organisation/Agency: 
Watercare (.co.nz)
How did you use this data in your entry?: 
Waikato river did supply (almost) 30%... but only during the 2013 drought. And this was less than 1% of the river’s volume.
Dataset Name: 
Crime Statistics for calendar year ending 31 December 2014
Publishing Organisation/Agency: 
NZ Police
How did you use this data in your entry?: 
Auckland had 2014 per 10000 population 8.9 robberies and 160.7 burglaries (total), the Waikato district much less, only 4.5 but 158.9 burglaries (total). We had cherry picked the crimes where the Waikato does well. Dwelling assaults the Waikato is double to Auckland.
Dataset Name: 
Agricultural Production Statistics: June 2007 (Final)
Publishing Organisation/Agency: 
Statistics New Zealand
How did you use this data in your entry?: 
It should have also been mentioned that the data is from 2007. Furthermore, we left the data from the South Island out where the Canterbury region had a higher number for their Hay, Silage and Balage harvest than the Waikato.
Event Location: 
Hamilton