A university project involved recording data about our university travel activity over a week. Such data included modes of travel, distance from the university and departure/arrival times. Data from all students in the class was combined into a spreadsheet. The task was to analyse this aggregate data to find patterns, relationships or some other aspect in order to tell a story or convey a message to a particular target audience through a visualisation.
From the aggregate student data, I found that the great majority of students travelled with a private vehicle (78%), and on average (including waiting time) each trip took almost an hour—simply too long. For this reason, my piece was to be targeted at the state transport department in advocating for increased frequency of public transport services to facilitate commuting university students.
Central to the piece are a map illustrating where students live and a graph which shows what times they leave home and the university. The map intends to assist the department in identifying where to designate new services, while the graph suggests when to implement more frequent connections.
The piece deals with varying forms of data; for example, there is proportionate data (days travelled by different students), data about physical relationships (distances of students’ home suburbs to the university), data recorded over time (departure times), and select data “bites” (such as the longest journey). By using a restrained colour palette and clear typography, all of these data are represented in a coherent and digestible manner.