Data Visualization
This project explores the transformation of raw data into comprehensible and explorable information, emphasizing the relationship between data, visualization, and interaction. The project starts by building a database of 100 items, organizing it cohesively, and creating both static and interactive visualizations to navigate the data.
I started by brainstorming ideas for the 100 data entries in which I decided on 100 instances of picking up my phone. Each entry was categorized by metadata, using Richard Saul Wurman’s L.A.T.C.H. principles (Location, Alphabet, Time, Category, Hierarchy). This framework allowed me to organize and sort the data through seven unique properties:
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Date
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Time
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Reason for Pickup
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App(s) Used
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Location
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Mood Before
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Mood After
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Weekend / Weekday
Drafts
I began sketching the visualization by exploring different ways to represent the data meaningfully, focusing on how the seven metadata properties could interact visually to reveal patterns and connections at a glance.
Final Sketch
My final design sketch brought together key elements of the data, integrating visual hierarchies and interactions to emphasize usability and clarity. Next, prototyped the design in Figma, focusing on creating an intuitive interface that allows users to seamlessly explore and interpret the data.
Design
The design process prioritized scalability, ensuring the interface could accommodate not only the original 100 entries but also a significantly expanded dataset, such as 200 or 500 entries, without losing clarity or usability. The final visualization sketch emphasized integrating both information design and data visualization to enable users to filter, sort, and explore the dataset interactively. Patterns such as how mood correlates with specific apps or how usage trends vary between weekdays and weekends were made discoverable through intentional visual hierarchies and interaction points.