Australia Tax Stats 2011-12 | Visualisation & Insights

This post visualises Australian Tax data for Individuals during the income year 2011-12. This web application allows the user to explore tax filing data to derive a number of insights. The data is available from the Australia Government website here. 

Watch the video for a look at this application. 

Specific Insights
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1) Size of the workforce has not changed much: If the Wikipedia page on Australian economy is correct, then the workforce has not changed from 2011-12. The workforce count on the data confirms this.

2) Workforce made up of Equal numbers from both genders: Tax data shows that almost the same number of men and women figure in the filings.

3) State-wise gender gap in registered salary: Data shows that women, when compared to men, although roughly the same number in the workforce as men have their cumulative salary field at half the $ number in WA. This gap varies for the states. Since the number of women is almost the same, this wage gap triggers cause-analysis such as:

1. Women are paid less in the same job compared to men.
2. If (1) is not true then, women are not in the same pay band/scale or jobs as men are in. This would explain why the salary is less even though women make up the same head count.

4) Taxable Income/Salaries for under 18 age and above 70: The data shows that there is healthy income (but < 1 Billion) for these two age groups.

5) Comparison of states: On all accounts the ranking is NSW, Victoria, Queensland, WA, SA, TAS and other.

There are other data points like child support by filing individual, refundable, exemptions etc which can be analysed.

Data visualisation: Plotting USGS earthquakes

This post makes use of USGS's live feed of earthquakes. The feed is plotted on a Leaflet map to visualize the data. Leaflet is a javascript library for interactive maps (http://leafletjs.com/). The advantage is that it consumes Geojson well.  

Feeds are categorised into all quakes and significant ones. The app's hourly feed is updated continuously for new data. When new data is available the map is updated. For each point of interest a leaflet layer group is utilised. This holds a circular marker layer and a place holder. A popup layer is also added which displays a text with the title from geojson. This title includes magnitude and location of the quake. All layer groups are added to the map's layer group. This allows individual layers associated with a point to be update or removed when data is updated.

Although all earth quakes are not significant, from the data is clear that earthquakes are more common in plate boundaries and fault lines (not surprising but nice to visualise). Switching to a weekly or monthly view makes vulnerable locations apparent.

Significant quakes are also seen in daily, weekly and monthly basis. Adding filters to query on Magnitude or changing the circle marker circumference based on magnitude would be good. However, significant earthquakes are far less in number and are shown separately in the app. Also to be noted is that, leaflet slowed down when a lot of layers were used for each point to support individual point updates. It also eases out when the map is just cleared and updates are added.

Tools:

Python 3.5.3
Django 1.10
Postgresql
Bootstrap 3.3.7
Leaflet 1.2.0
Javascript, JQuery
Geojson