Visualisation on a user's movements throughout a period of a month. Conducted in October 2019.

Based on common attainable data sets, the data can be translated into specific data pointers that relate to its sub-sets. For instance, all sub-sets utilizes the location data from the main data sets to provide for the user's Spending Habits, or Frequented Locations, per se.

Ultimately, data is communicated into statistics that implore the hacker to better understand the user's patterns and behaviours.

Screenshot 2019-11-18 at 6.00.38 PM.png
Webb-05.png

Spending Habits (Instances x Location)

By representing the location data towards the value, the visualisation depicts the spending patterns of the user, where a large group of transactions appear to the in frequents areas of the Orchard - Bugis area and the Bishan - Thomson area.

On the other hand, a number of purchases also occurred in the east, central area and slightly at the northern area. From this visualisation, it can be inferred that the user regularly transacts in the central and southern parts of Singapore

$625.6

Total Spent in a Month

x163

Times Spent via Card/Contactless Payment

Cost vs Category

With the data pool, there are 3 main categories (i.e. Lifestyle, Food & Transport). By placing the data set in a scatter chart, it is conclusive that the user consistently takes the same mode of transport every day, given the constant value given on the chart.

By limiting the data visualisation to display from $0 to $1 (see below), transport fares are clearly defined to be between $0 to $0.80 per trip, giving at least 2 trips per day.

$5.76

Average Cost of Transport per Day

$0.83

Most Common Cost in Data Pool

Screenshot 2019-11-19 at 11.04.23 PM.png

Find out more about how movement relates to understanding the user's Habits!