Visualizing of Halt Times of Passenger Trains at Stations of Mumbai
(Complete process and exploration is up as a post on Medium)
(Complete process and exploration is up as a post on Medium)
Visualizing data involves telling stories with data. Visualizations engage the viewer far more than a wall of text and drive interesting insights. This visualization process was done during an academic assignment. The data provided was that of train information of the Indian Railways. The information included the train numbers, train names, source, destination, distances and number of stops. The task was to identify an interesting aspect from the data and visualize it.
So the next thing I did was ask myself and the data some defining questions, like
1) What story can I tell with this data?
2) What interesting insights do I want the viewer to gain out of the visualization or what insights should the
visualization depict?
3) What needs to be highlighted to gain attention? What should be the focus?
4) What would be of interest in this data?
5) Is there a possibility of overlapping this data set with another to create an interesting story with the mash-up?
1) What story can I tell with this data?
2) What interesting insights do I want the viewer to gain out of the visualization or what insights should the
visualization depict?
3) What needs to be highlighted to gain attention? What should be the focus?
4) What would be of interest in this data?
5) Is there a possibility of overlapping this data set with another to create an interesting story with the mash-up?
Indian Railway Train data (source: data.gov.in)
With these questions in mind, after some pondering and exploring previously visualized railway data, I got the idea to visualize how long the trains halt at the stations and if that is related in any way to the importance of that station or to the rush of the station, i.e how busy it is. I had a hunch from personal observations that trains halt longer at important stations and busier junctions, so the data could narrate if this hunch was correct and if so, how much is the variability in the halt timings at the stations related to the overall halt time of the train.
Due to constraint of time, I picked a subset of this data, i.e I decided to take up all the trains that halt at stations within Mumbai.
Due to constraint of time, I picked a subset of this data, i.e I decided to take up all the trains that halt at stations within Mumbai.
Calculating the required data
A concept sketch of how the visualization can be done, inspired from the Marey diagram
Purpose of the visualization was:
1) Comparison
2) Scalability
3) Easy consumption of data (Meaning it should provide insights at-a-glance)
4) Tool for execution
2) Scalability
3) Easy consumption of data (Meaning it should provide insights at-a-glance)
4) Tool for execution
An exploration as a Gantt Chart. The X-axis shows time, the Y-axis shows the trains. The colours denote the stations and the halt times are represented by the size of the bars. A Gantt Chart is a type of bar chart, developed by Henry Gantt in the 1910s, that is generally used to illustrate time intervals. (Chart created using RAW Graphs)
The final interactive visualization as a treemap using Datamatic. A treemap is a space filling visualization of data hierarchies and proportion between elements. The different hierarchical levels create visual clusters through the subdivision into rectangles proportionally to each element’s value. Treemaps are useful to represent the different proportion of nested hierarchical data structures.
The colours denote the stations and the size of the boxes denote the halt time. One can see the exact halt time upon hovering over the boxes.
