SIEM REAP, Data visualization is a key tool to add insight to stories and inform readers, an expert said as he unveiled free training for Cambodia’s journalists is in the pipeline.
Using data in stories is rare among Cambodian journalists and Open Development Cambodia (ODC) senior data researcher Vong Piseth said that data visualization helps readers analyze data tables by extracting important information, and summarize data to provide meaningful information that is eye catchy.
He added that data visualization plays a role in providing insight along with context. When dealing with complex information that is unsuitable for narrative, visualization is a solution.
Data visualization is also often used to show major findings, he said at Cambodia’s ICT Camp 2022 in Siem Reap.
“When we produce charts, we produce insight. The goal is not about charts, it's about insight,” Piseth said. “Data visualization allows us to see the patterns, relationships, and trends. From these visual presentations, we can explore what is interesting. It helps us digest to gain deep understanding. That is called insight, the actionable information for telling a story and supporting decision-making.”
Piseth said if a media institution seeks to pursue a data-driven story, it must decide whether to recruit a new team or train existing journalists about data. “I recommend getting journalists on a training course,” he said, adding ODC plans to host a session but has not yet confirmed the details.
Piseth noted that data visualization is the process of transforming data into visual features that display patterns, trends, and relationships. However, it is not solely a visual explanation of procedures, concepts, or workflows.
The process starts with analyzing a dataset and looking at the attributes before designing an appropriate chart that represents this data, he advised.
Data journalists also need to know which categories of the chart the data they are examining falls into. This includes comparison, distribution, relationship, and composition. Knowing what attributes are available can make reaching the final goal more achievable. Attributes are the content of characteristics of individual data records.
Qualitative charts are ordered, for example schools (primary, secondary, undergraduate, master, PhD) while examples of unordered data can be found in transportation (cars, motorbikes, buses, bicycles).
Piseth said quantitative charts are discrete – for example, population, enrollment, age – and continuous – for example, elevation, rainfall, tree cover loss. He also introduced some commonly used charts that are used to visually represent data. These include Pie charts, columns, line charts, tables, pictograms, mapping, and hierarchy charts.
A Pie chart is good for comparing datasets, column charts are ideal for comparing magnitude, line charts tend to be used to observe trends, and tables are commonly used to explore data in an interactive way.
Pictograms are good for pictorial comparisons and compositions, while mapping is for spatial comparison and distribution, and hierarchy charts are good for sub-category comparisons.
Piseth introduced some tools for data visualization, which includes Flourish, R Programming, Datawrapper, Python, Google Sheet, and Microsoft Excel.