🌳 Go Griffith: User Research for Safer Outdoor Planning During COVID-19

🎯 Problem

Visitors had no way to know:

• If Griffith Park would be too crowded.
• If the park was mask-compliant.
• What level of risk they were personally comfortable with.

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💬 89% of users wanted crowd density info.
💬 81% said mask compliance was their top safety concern.
💬 48% felt most uncomfortable in dense crowds.

🛠️ My Role

• Conducted user research (surveys, interviews).
• Led competitive analysis and user journey mapping.
• Built a Teachable Machine prototype to test mask detection feasibility.
• Co-designed wireframes and high-fidelity mockups following the team’s style guide.

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🧩 Process

1. 🧪 Research

• Conducted interviews to define two key user personas:

2. 🔬 Exploring Emerging Tech

• Used Google’s Teachable Machine to prototype basic mask detection.
• Validated that computer vision could enhance future public safety tools.

3. 🧮 Testing Data Visualizations

Tested different methods for showing risk info:

• Live Camera Feed: 65% positive impact
• Heat Map: 65% positive impact
• Point Density Map: 55% positive impact
• 3D Model: 50% said it wouldn’t affect their choice

🏆 Winning Approach:
Point Density Map + Color Coding + Camera View for real-time context.

🖼️ Final Design

Key Features:

• Crowd Density Map: Color-coded areas based on live data.
• Pin Locations: Watch specific areas and get notified if crowd density spikes.
• Plan with Friends: Share pins for safer group planning.
• Mask Compliance Data: Easily view mask-wearing trends.

🌟 Outcome

• 87% of users said they would feel more confident visiting Griffith Park with live risk insights.
• High-risk users prioritized visiting during low-density times.
• Younger users appreciated mask compliance visibility.

💡 Reflections

• User Choice Matters: Different lifestyles = different needs. Flexibility is key.
• Design Must Pivot: COVID taught designers to adapt fast — a skill that’ll stay critical for the future.

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