Data Visualization & Story Telling
Data visualization and story telling with tableau
4 January 2024


Whenever we encounter a new dataset, we often need a rough idea about the data itself. We can get this through metadata, basic statistics, and data visualization. On top of that, we often use data visualization for telling a "story" for our project. So that we can perform an action based on that story. If the story is good enough, the story will be remembered and understand easily.

In this article, we are going to discuss data visualization to depicts our dataset and infer or gain valuable insight. The example dataset that we are going to use is coming from Australian Government which can be found here.

Effective Story Telling

We can enhance our story telling by ensuring these aspects

  • Context
  • Appropriate visual
  • Focus attention


By understanding and giving context we are making sure that the person who reads this fully understands that the visualization that we made. A good example of this is labeling the graph that we make with a proper label. We describe the metric if it is a number or an explanation whenever it needs one.

Context also means that we understand as the maker of the visualization that this will be given to whom and what is the purpose of it. By having an idea of this, we are going to make the visualization that is appropriate and useful for the stakeholder. A good starting point for this is by asking who constitutes the primary audience for the visualization, what intrinsic value does the information hold for them, how will the intended audience interpret and glean insights from the visual representation?

Appropriate visual

An appropriate is also a crucial part of the story. We need to choose the correct type of graph to show things that we are interested in and infer the correct information from the graph. A good rule of thumb for this:

  • Comparing Categories

    • Bar
    • Horizontal Bar
    • Stacked
  • Distribution

    • Histogram
    • Box & Whisker
  • Time

    • Line
    • Area

Focus Attention

We need to focus our audience attention towards the things that matter. We can achieve this by emphasizing certain part of the graph. One easy and yet effective way is by giving a different and contrast color or by labeling it.

Another technique that we can use is Gestalt Principles. There are 6 laws or principles that can help guide us to focus our audience's attention which are proximity, similarity, enclosure, closure, continuity, and connectedness. By conscientiously integrating these principles into our design framework, we gain the ability to shape visual narratives that capture and guide the viewer's focus, elevating the overall impact and effectiveness of our communication.

  • Proximity: This principle posits that elements placed close to each other are perceived as a group or related entities. When designing, arranging related elements in close proximity can establish visual connections and convey a sense of unity or association.
  • Similarity: The principle of similarity suggests that elements with similar attributes, such as shape, color, size, or texture, are perceived as belonging together. Leveraging similarities in design can effectively convey relationships or groupings.
  • Enclosure: Enclosure involves creating a boundary around related elements, forming a perceptual unit. When elements are enclosed within a common boundary, they are perceived as a cohesive whole. This principle is particularly useful for organizing information and emphasizing relationships.
  • Closure: Closure refers to our tendency to perceive incomplete or open shapes as complete entities. By strategically leaving gaps or presenting fragmented information, designers can engage the viewer's mind, prompting them to mentally complete the missing parts and fostering a sense of wholeness.
  • Continuity: The principle of continuity emphasizes the human inclination to perceive continuous patterns or lines as connected and flowing smoothly. By aligning elements in a continuous manner, designers can guide the viewer's gaze and convey a sense of direction or connection.
  • Connectedness: Connectedness underscores the idea that elements that are visually connected are perceived as belonging together. Whether through lines, colors, or other visual cues, establishing connections between elements enhances their perceived relationship and coherence.

Telling a Story

There are a few ways we can convey our story so that the audience can understand fairly easily.

  • Change over time: This way of story telling is commonly used for time series data. We can notice the trend or if there is any reocurrences of any particular phenomenon.
  • Drill down: As the name suggests, we drill down our story to a more specific area.
  • Zoom out: It is the opposite of drill down, we start off with a specific and work our way through the bigger view of the story.
  • Contrast: We compare and contrast with one aspect to another

Case Study

As I mentioned previously in introduction part, we are going to use these knowledge in a real world data and I am going to use Tableau to demonstrate it. The first thing that we need to do is finding context. In this case, there are a few notes/metadata that they have included in this dataset. It describes how, when, and the criteria of the data. These information gives us a little bit of context on the data itself and a precautions when we use this data.


There are a few key features from this dataset that we can use to infer a few valueable information such as

  • Crash severity: How severe the crash was and total casualties involved in that crash.
  • Crash vehicle: What and how many vehicle were involved in that crash.
  • Location & time: Latitude and longitude where the crash happened, when it happened, name of the suburb, and postcode.
  • Condition: Atmospheric condition, crash road surface, lighting condition, speed limit, and description of a crash.

With these information, I am going to explore a few questions within this dataset:

  • How is the crash severity trend?
  • Where crashes have been happening?
  • At what time usually crashes happen?
  • What is the common vehicle that is causing crashes?


Based on those questions, I have created some plots to try to answer those questions. Here we can see that a scatter plot with superimposed map of Brisbane and surrounding suburb, a line graph illustrating severity trend, and another a bar chart of vehicle trend. I chose these three graphs to clearly show how the trend looks like. As we can see that crashes have been happening heavily in Brisbane CBD with most of them are medically treated. The majority of victims undergo either hospitalization or medical treatment, with a noteworthy alteration in the trends for both categories observed since 2017.


Furthermore, I explored on characteristic of the crashes, I am trying to identify what is the common "scenario" that a crash would likely to happen. Here on the second dashboard we can see that what is the most common speed limit, suburb, time, lighting condition, and crash road feature. We can see that from that graph, mostly crashes occur on 60 km/h speed limit with equally distributed victim severity. From lighting and crash roadway feature perspective, the data is highly skewed towards daylight lighting condition and no roadway feature. Time on crash happened is quite evenly distributed among active time with some spikes at 8am, 3pm, 4pm, and 5pm.


Based on those explorations, we can make a few points:

  • Most crash victims are generally hospitalized or medically treated.
  • Crashes have happened a lot in Brisbane CBD and surrounding suburb.
  • The most common condition for a crash to happen is in a daylight lighting condition, without roadway feature, and around certain period of time.