Using visualization as a thinking tool to discover patterns, relationships, and uncertainties in science data investigations.
Throughout this module, we continue working with our running example of investigating electrical fire incidents, focusing on how visualization helps reveal patterns and relationships that inform our understanding of contributing factors.
Once data has been cleaned and structured, the next step is to explore it visually. Exploration is not about proving a conclusion, it is about discovering patterns, relationships, anomalies, and uncertainties that help refine understanding of a real-world problem.
In investigations of electrical fires, visualization helps reveal:
This phase is closely connected to modeling and interpretation. What you see here may lead you to revise your research question, request additional data, or rethink earlier assumptions.
Throughout this phase, LLMs function as idea generators and explanation partners, not authorities.
Any chart, interpretation, or narrative produced with LLM assistance must be:
The following seven-step workflow guides the process of exploring data visually and communicating findings responsibly.
Before creating any chart, you may restate my investigative question and decide what kind of insight you are looking for.
Electrical fire examples:
Each question implies a different visualization goal:
Prompting technique: Zero-shot prompting
“Given this research question about electrical fire causes, what type of insight should a visualization aim to show: trend, comparison, distribution, or relationship?”
Once the goal is clear, choose a chart that matches it and decide how variables map to:
Examples:
Prompting technique: Few-shot prompting
“Here is my data on fire safety analysis.
Based on these, suggest an appropriate chart for comparing electrical fire incidents across wire materials.”
I treat the output as a suggestion, not a final decision.
At this stage, the goal is exploration, not polish.
Generate an initial chart and ask:
Prompting technique: Prompt chaining
Prompt 1:
“Create a simple bar chart comparing electrical fire incidents by wire material.”
Prompt 2:
“Explain what patterns or differences are most noticeable in this chart.”
Interpretation involves describing what the data actually displays, not explaining why it happens.
Separate:
Example interpretation:
Observed: “Buildings with wiring older than 30 years appear more frequently in the dataset.”
Speculation: “Older insulation materials may degrade over time, increasing fire risk.”
Prompting technique: Chain-of-thought prompting
“Step by step, describe the main patterns in this electrical fire visualization.
Clearly separate observed data patterns from possible explanations. Use the attached data as context”
Before communicating results, check whether the visualization could mislead.
Key questions:
Prompting technique: Self-consistency
“Review this visualization for potential sources of misinterpretation.
Then review it again from the perspective of a skeptical reader.”
This helps surface issues you might overlook.
Once the visualization is accurate and interpretable, translate it into communication products:
Each figure includes:
Data visualization is rarely final on the first attempt.
You should:
This ensures transparency and reproducibility.
Exploration and visualization are not just about presentation, they are thinking tools. In investigating electrical fire data, visualizations help surface relationships, challenge assumptions, and guide next steps in analysis.
LLMs can accelerate this process by suggesting options and explanations, but the responsibility for accuracy, interpretation, and ethical use always remains with the investigator.
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