Walks of Life

Walks of Life
Team Name: 
Project 205

 

Summary

The Walks of Life Project is a multiple choice quiz game to challenge and intrigue high school students with some surprising and interesting facts arranged by significant period of life derived from open data sets. The aim is to be informative about content and the range of open data available.

 

The problem: When you know what you are looking for, open data tells a vivid and complex story about what life is like in Australia. For men and women, people who are: young, old, rich, poor, live in rural areas or cities and so much more. However, for those unfamiliar with a content area or how to access and interpret data, the huge volume of open data can be paralysing or daunting.

 

“Walks of life” is a cross-platform compatible webapp that allows for small, digestible pieces of open data to become stories, to help young people to start learning about what data is available and what it might mean for them. “Walks of Life” takes you through the lifespan of Sam, our virtual man or woman and challenges you to think about statistics from different life stages.

 

As Sam progresses through his or her life, the user is quizzed on various questions, derived from open data sources, mainly from the Australian Institute of Health and Welfare. A correct answer progresses you to the next life stage. If the answer is incorrect, a life is lost, with 3 lives given before Sam dies, the game is over and you must begin again from birth. Once the game has ended, the user is presented with the most common cause of death and some interesting statistics about the age at which Sam died.

 

The questions in the quiz are based on descriptive statistics from the data. As the quiz progresses the questions are based on certain age ranges starting at 0-4 years to 75+ years. The questions are not aimed at being a comprehensive educational resource but rather to aid in framing and revealing the current possibility space presented by open data. For example: “What time of the day saw the most road deaths in 2010?” reveals that we have road death data down to the time of day. The question “For 15 to 19 year olds, what was the leading cause of death in 1909?” shows how far back the data goes in some datasets, in this case the GRIM books.

 

We used many different data sets to come up with multiple choice questions and fun facts on a range of issues by investigating and interpreting data and using analytics and pivot tables to uncover interesting statistics. These included:

 

  • General Record of Incidence of Mortality (GRIM) books

  • Premature mortality in Australia

  • Higher Education Attrition rate data 2005-2013

  • Australian Road Deaths Database

  • Australian Cancer Incidence and Mortality Combined Counts

  • Household Income and Income Distribution, Australia, 2009-10

  • Youth Justice Detention Data

  • National Drugs Strategy Household Survey

 

The question database is an open ledger with a simple question format, updates to this are automatically available to be pulled and used as a question when the webapp is used. This makes walks of life flexible and a good place to showcase new data sets, new data variables or other innovations.

 

Release notes detailing these updates and changes is a concise and easy to use format for those familiar with the content area or data. Walks of Life’s value lies in delivering existing and emerging data and data capabilities to early high school students in a way that gives new data a narrative and makes it relatable to them. While the Project 205 team created questions from our own analysis, data and content experts can write their own questions to the ledger.

 

From a data owner or custodian's perspective, if you have ever wanted to get younger students engaged or involved in your data or research, Walks of Life gives you the capability to challenge or inspire students, rather than trying to force students into data a well designed, relatable question can lodge in your mind and foster intrinsic motivation.

 

Currently questions are grouped and displayed by age, the next goal is adding indigenous status and simple location data, e.g. state. This will allow for students to navigate to lifestages of interest and be presented with targeted questions that are of significance to that particular life stage, further linking datasets to a relatable core system.

 

Used Datasets: 
Dataset Name: 
General Record of Incidence of Mortality (GRIM) books
Publishing Organisation/Agency: 
Australian Institute of Health and Welfare
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
Filtered to find leading cause of death for males and females for each age group for a particular year. Performed analytics to extract interesting statistics from the data which were used to create multiple choice questions.
Dataset Name: 
Premature mortality in Australia 1997–2012
Publishing Organisation/Agency: 
Australian Institute of Health and Welfare
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
Used supplementary table to extract striking values and contrasting differences on premature and/or avoidable deaths. This was used to create questions to prompt the user into considering how their choices and behaviour might effect health outcomes.
Dataset Name: 
Higher Education Attrition rate data 2005-2013
Publishing Organisation/Agency: 
Department of Education and Training
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
This dataset was already constructed in a very usable format. The attrition rates per age group were already calculated and were simply extracted. This highlights the fact that some of the open datasets are ready to be used and don't need any analytics for public use.
Dataset Name: 
Australian Road Deaths Database June 2016
Publishing Organisation/Agency: 
Bureau of Infrastructure, Transport and Regional Economics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
Analysed data to create interesting fact based questions for the year 2010. The data was analysed using SPSS to extract descriptive statistics. Means were analysed and the greatest differences and most interesting contrasts were selected. These were which time of day and what speed zones saw the most deaths, how many total road deaths there were and whether men or women are more likely to die on Australian roads.
Dataset Name: 
Australian Cancer Incidence and Mortality Combined Counts
Publishing Organisation/Agency: 
Australian Institute of Health and Welfare
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
Extracted the four most common cancer types for the 30-34 age group. Calculated survival rate of these cancers using the incident and mortality counts, this was used to form a question on cancer survival rates.
Dataset Name: 
Household Income and Income Distribution, Australia, 2009-10
Publishing Organisation/Agency: 
Australian Bureau of Statistics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
This dataset was already summarised in easy to access tables. This made it quite difficult to filter and extract means based on different grouping variables. It highlighted the differences in open data sources and it would have been more beneficial to have had access to the complete data source. Tables were examined for contrasts between groups with the most interesting averages to be selected for inclusion in the questions.
Dataset Name: 
Youth Justice Detention Data
Publishing Organisation/Agency: 
Australian Institute of Health and Welfare
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
This dataset was limited in scope. Data had been condensed to quarterly nightly populations of only 2 age groups. The data was broken down by gender and indigenous status. Means were calculated through Excel to extract the distribution of men and women and also Indigenous and non-indigenous nightly populations. Conclusions were drawn and a question formed to educate the user about rates of youth population by gender in Australia.
Dataset Name: 
National Drugs Strategy Household Survey
Publishing Organisation/Agency: 
Australian Institute of Health and Welfare
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
This dataset was interesting as it described community perceptions about drug use. This enabled innovative questions that allowed the user to map their perceptions of drugs against other community members. Average responses were calculated through SPSS analysis and descriptive statistics. Questions were formed to describe the most common responses in the community.
Event Location: 
Canberra