How SAP Lumira finalized my Dream Destination Countries
Deliberated a lot about the Data Geek Challenge and perhaps what I should start with. Done some visualization on few samples CSV files and was thinking to get some amount of data to create my first visualization. However, I failed to get any concrete data with rows in few thousands.
Hence, I started with recent analysis I have done to zero on bunch of best country to work and live in with higher disposable time at hand for pursuing your hobbies. The data worked up on here is taken from http://www.oecdbetterlifeindex.org/. Though the actual data has lots of parameters on which the study was carved in. For sheer my understanding and analysis, I have taken just a few parameters which were relevant to me.
Important parameters of Better Life Index on which I created the visualizations are:
Household net adjusted disposable income (USD /Yr)
It’s the maximum amount that a household can afford to consume without having to reduce its assets or to increase its liabilities. It’s obtained adding people’s gross income and then subtracting the taxes on income and wealth, the social security contributions paid by households as well as the depreciation of capital goods consumed by households.
Employment rate (%)
It is the number of employed persons aged 15 to 64 over the population of the same age
Time devoted to leisure and personal care (Hrs/day)
This indicator measures the amount of hours per day on average full-time employed people spend on leisure and on personal care activities. Leisure includes a wide range of indoor and outdoor activities such as walking and hiking, sports, entertainment and cultural activities, socializing with friends and family, volunteering, taking a nap, playing games, watching television, using computers, recreational gardening, etc.
Expected years in education (Yrs)
This indicator is the average duration of education in which a 5 year old child can expect to enrol during his/her lifetime until the age of 39.
Life expectancy at birth (Yrs)
Life expectancy measures how long on average people could expect to live based on the age-specific death rates currently prevailing.
- Started with small amount of date taken below.
- To choose the best destination for working and pursuing my hobbies, I wanted to know the countries where I can get some handsome disposable income which will take care of my living and leisure activities. I choose the basic column chart with countries as dimensions and Net Disposable Income as measure. As clearly visible Australia, Germany, US, Switzerland, Luxembourg and Norway scored very high here.
- However, only net disposable income isn’t going to help me if I won’t get any disposable time. So, just added another measure and visualized what I will get with column chart measured against 2 parameters. The options started making sense with amount of time I would get in US lagging behind and others leading.
- Well, with economic cycles running at breath-taking speed, I though it is imperative that I stay in employment and don’t get run over by the cycle. So, took help of line chart with couple of Y-Axis and that made options even clearer by highlighting Australia, Switzerland, Germany and Norway.
- Lastly, I wanted to stay as close to home as possible with some concerns for health Services. Geographic charts with Life Expectancy measure helped me big way in that.
- Finally, to underline my conclusion, I surrendered to Bubble Chart with Net Income, Time for Leisure Activities and Employment rate in X, Y and Bubble width measures respectively. That clearly pointed me towards Switzerland and Germany.
Its exciting to see how the colorful visualization helps you in unfolding the underlying data story. Eager to grab more data and come up with even more colorful elaboration and conclusion. 🙂