Insolvency in QLD 2013-2014

Highest rate of insolvencies in QLD for 2013-2014 were from the Gold Coast region.
Team Name: 
Kitty and Keema

This being the first time either of us had attended GovHack, we came with big plans. Especially when there's so. much. data!

Initially we had an idea of telling a story using the data we had to tie everything to each other, kind of like a diary of our hacking experiences, combined with the end results of our hacks. By chance we stumbled onto the insolvency data and wondered how we could tie this with the geospatial data we'd looked over for another project. As we are both "Makers", we wanted to see in what ways we could translate the digital data into a physical representation. A picture is worth a thousand words, afterall.

The project evolved significantly several times in less time than the space of a day. From a story, to a conceptual artistic representation of the reported insolvencies, to a 3D printed "exploded" Q1 tower, to a 3D printed and sliced Q1 tower and a laser cut representation of the proportion of insolvencies of the Gold Coast and QLD.

Here is a brief summary of what we did:

1. Created the file: https://docs.google.com/spreadsheets/d/16cMjfXuUqV_VpCH4OoqbrUBcP9BdYpod...

2. Extracted the 2013/2014 insolvency data out for that year by postcode.

3. We then obtained geospatial data with relevance from the postcode to suburbs for further analysis.

4. Upon allocating the postcodes to suburbs we again filtered and sorted in descending order, founding that a large number of insolvencies were from the Gold Coast region.

5. We accessed more Geospatial data to find the post codes only from the Gold Coast.

6. We then did a figured out the ratio of the insolvlencies with postcodes from the Gold Coast City Council area in comparison to the rest of QLD.

7. These ratios were then used to create a physical 3D printed representation of the ratios... one being the Q1 Tower, a physical representation of the Gold Goast, which represents the Total number of Insolvencies. We then spilt the building into the 3 different types of insolvency types: # of Bankruptcies, # of Debt Agreement Debtor and # of Personal Insolvency Agreement Debtors for the Gold Coast.

8. To further illustrate the ratios visually, we decided to laser cut the shape of QLD to display the state percentages in proportion to the Gold Coast statistics.

We certainly had bitten off more than we could chew for our first GovHack, with constant revisions to our project based on what data we could obtain and possibly link together, which didn't always lead down the road we originally envisioned.

We were astounded at the percentage of insolvencies in the Gold Coast region alone, particularly the number of Personal Insolvency Agreements, especially when taking into account the accepted socioeconomic stereotype of the area. If only we had time to extract all the data nationally, that would definitely have been equally interesting to see translated into a visual representation.

 

Other tools used:

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The primary tool used to convert the ERSI shapefile data is Mapshaper:

https://github.com/mbloch/mapshaper

 

The primary tool used to convert the dataset data to GeoJSON files (for further processing with Python scripts) is ogr2ogr, which is part of GDAL:

http://www.gdal.org/

Region: 
Team Prize Details: 
TAFE Queensland East Coast
Used Datasets: 
Dataset Name: 
SVG-Koort Queensland
Publishing Organisation/Agency: 
By Slomox [Public domain], via Wikimedia Commons
How did you use this data in your entry?: 
We needed an SVG for the laser cutter to help create our visual representation of the data.
Dataset Name: 
Personal insolvency by postcode
Publishing Organisation/Agency: 
Australian Financial Security Authority
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
1. Created the file: https://docs.google.com/spreadsheets/d/16cMjfXuUqV_VpCH4OoqbrUBcP9BdYpod5I2OBVV1EF4/edit#gid=1943582839 2. Extracted the 2013/2014 insolvency data out for that year by postcode 3. We then obtained geospatial data with relevance from the postcode to suburbs for further analysis. 4. Upon allocating the suburbs from the suburbs we then once filter and sorted in descending order found that a large number of insolvencies were from the Gold Coast region. 5. We accessed more Geospatial data to find the post codes from only the Gold Coast 6. We then did a comparison of the insolvlencies with postcodes from the Gold Coast City council in comparison to the rest of QLD. 7. These ratios were then used to create a physical 3D printed representation of the ratios... one being the Q1 tower, which represents the Total number of Insolvencies, then spilt into the 3 different types of insolvency types: # of Bankruptcies, # of Debt Agreement Debtor and # of Personal Insolvency Agreement Debtors for the Gold Coast. 8. Further we decided to laser cut the shape of QLD to represent as well Splitting it into the 3 different types of insolvency.
Dataset Name: 
270.0.55.003 - Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2011
Publishing Organisation/Agency: 
Australian Bureau of Statistics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
used to aid in the breaking up of data into localities
Dataset Name: 
Local Boundaries - QLD
Publishing Organisation/Agency: 
Department of Natural Resources and Mining
Jurisdiction of Data: 
Queensland Government
How did you use this data in your entry?: 
used to aid in the breaking up of data into localities. More specifically the data URL below: http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={8F24D271-EE3B-491C-915C-E7DD617F95DC}
Dataset Name: 
Local Government Areas ASGS Non ABS Structures Ed 2011 Digital Boundaries in ESRI Shapefile Format
Publishing Organisation/Agency: 
Australian Bureau of Statistics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
information from the .shp files were utilised in order to identify some of the boundaries and of the Gold Coast and obtain regional data for further input analysis with regards to locality.
Dataset Name: 
NRP, Industry, LGA, 2010-2014
Publishing Organisation/Agency: 
Australian Bureau of Statistics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
information from the files were utilised in order to identify some of the boundaries and of the Gold Coast and obtain regional data for further input analysis with regards to locality.
Event Location: 
Brisbane Maker Node