The Problem
Existing travel apps surface popular, commercially-driven, tourist-reviewed content. The local perspective — the thing travelers actually want — is structurally invisible.
The Opportunity
Residents know their cities in ways no algorithm does. The design challenge is building the trust, verification, and discovery systems that make local knowledge accessible without losing its authenticity.
My Role
Solo UX/UI — research synthesis, information architecture, interaction design, visual design, and prototype testing.
How Might We
Help tourists experience a city the way its residents live it — not the way its tourism board photographs it?
Interviewed two distinct groups: frequent travelers (5) and urban locals in cities they'd received visitors (5). Both perspectives were essential — the supply side and the demand side of local knowledge.
Audited TripAdvisor, Google Maps, Airbnb Experiences, Yelp, and Foursquare — analyzing trust signals, recommendation provenance, and where the "local voice" was either absent or manufactured.
Observed three travelers navigating Chicago using only existing apps for a day. Documented decision points, moments of frustration, and what they asked locals directly when the app failed them.
The gap between what travelers want and what apps provide is structural, not cosmetic.
"I always feel like I'm missing the real city. The tourist spots are fine but they feel staged — like a theme park version of the place."
Insight 01 · The Staged Experience Problem
"TripAdvisor is just tourists reviewing experiences made for tourists. It's circular. I want to know where the locals actually eat."
Insight 02 · Tourist Reviewing Tourist
"When I ask a local — even a stranger on the street — I always get a better recommendation in 30 seconds than 20 minutes of app research."
Insight 03 · The Local as Living Algorithm
"I'd trust a recommendation from someone who actually lives there — if I could verify they actually live there. That's the thing apps haven't solved."
Insight 04 · Trust Requires Verification
Mapping the emotional arc of a typical city visit revealed exactly where friction and disappointment concentrate.
Opens TripAdvisor, Google Maps, Reddit. Overwhelmed by the volume of "Top 10" lists. Frustrated that they all recommend the same places.
Arrives with a list of "must-sees." Follows the app. First stop is crowded, overpriced, and nothing like the photos. Mood dips.
Wanders off-plan. Stumbles on a local market, a café the app doesn't mention, a neighborhood that feels real. Best moment of the trip.
Tries to get from the market to a restaurant. App routes them through a tourist corridor. Gets lost. Misses dinner reservation.
Tells friends about the trip. The stories are all from the unplanned moments — the market, the café, the stranger who gave directions. Not the tourist sites.
Every major platform aggregates opinions — but none distinguish between resident knowledge and tourist opinion. That's the gap.
| Platform | Content Source | Strengths | Critical Gap |
|---|---|---|---|
| TripAdvisor | Tourist-generated reviews | ScaleCoverage | Reviews are from visitors, not residents. Commercial bias. Ranking manipulation well documented. |
| Google Maps | Algorithm + mixed reviews | NavigationReal-time data | Optimized for popularity and SEO. Local voice is statistically drowned out. |
| Airbnb Experiences | Host-curated activities | Local hostsCuration | Monetized "local" — hosts are incentivized to perform authenticity, not express it. Tourism-first, not resident-first. |
| Foursquare / Swarm | Check-in data + tips | Location contextResident mix | No verification of local vs visitor. Tips are minimal, unstructured, and rarely updated. |
| Atay ↗ | Verified residents only | Authentic sourceVerified localsNon-commercial | Challenge: building the local contributor network and maintaining trust without commercial incentives. |
29 · Product Manager · New York, NY · Frequent traveler (6+ trips/year)
Sofia travels for curiosity, not leisure. She does extensive research before every trip but consistently finds that the best moments are the ones her research didn't surface. She's tried every travel app and describes most of them as "tourist traps wrapped in good UX." She's started asking hotel staff and strangers for recommendations instead.
Travel Style
Independent, cultural immersion
App Behavior
Heavy pre-trip, light during
Trust Signal
Peer recommendations, local voice
Pain Points
37 · Teacher · Chicago, IL · 14-year resident
James is proud of his city and quietly frustrated that visitors only ever see the same three neighborhoods. He recommends places to visiting friends all the time and loves the expression on their faces when they discover something unexpected. He'd contribute to a platform like Atay if it didn't feel like unpaid marketing work for a corporation.
Motivation
Pride of place, local advocacy
Barrier
Time investment, trust in platform
Trust Signal
Non-commercial, community-driven
Concerns as Contributor
The IA problem was dual-sided: the app needed two distinct entry points — one for travelers exploring, one for locals contributing — while keeping the experience unified.
↑ Simplified site map — full IA available in Figma prototype
Interviews with travelers and locals. Shadow sessions. Identified the dual-persona problem and the trust gap as primary design constraints.
Defined three principles: Local First, Trust is Earned (not Claimed), and Discovery over Direction.
Sketched 4 concept directions. Validated the "Local Badge" verification model and the "Local Day" itinerary builder through concept testing.
Built flows in FigJam, then two full hi-fi iterations in Figma. First iteration tested the map; second tested the contributor flow.
3 rounds of moderated testing — split between traveler and local contributor perspectives. Key fix: simplified the Local Badge verification from 7 steps to 3.
Lo-Fi Wireframe
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01 · Lo-Fidelity
Structural sketches focused on information hierarchy and navigation model. Validated the tab structure and map-primary layout.
Mid-Fi Prototype
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02 · Mid-Fidelity
Introduced the Local Badge and category filter. First round of usability testing revealed the filter toggle needed stronger visual differentiation.
Hi-Fi Design
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03 · Hi-Fidelity
Full visual design with verified local iconography, neighborhood-based map clusters, and the Local Day itinerary builder.
Map with Local Filter
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Decision 01
Early versions used a dropdown filter to switch between "Local Recommendations" and "All Reviews." Testing showed users didn't use it — it felt like an advanced setting, not a core choice.
Redesigned as a persistent toggle embedded in the map header, visually switching the entire map layer — color scheme, pin types, and category density — to signal that this is a fundamentally different view of the same city.
Design Rationale
The toggle needed to feel like choosing a different guide, not adjusting a search filter. The visual mode shift communicates: you're now seeing this city through different eyes.
Decision 02
The original verification flow had 7 steps — address upload, ID check, selfie, manual review. Drop-off in testing was 80% before step 4. The trust signal that made the product valuable was killing the contributor pipeline.
Simplified to 3 steps: location history check (passive, opt-in), one local landmark question, and a community endorsement request. Verification communicates "enough confidence to trust" rather than "airtight identity proof."
Design Rationale
Trust in a platform like Atay is probabilistic, not absolute. The verification UX needed to reflect that — rigorous enough to be meaningful, light enough to not deter honest contributors.
Local Verification Flow
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Local Day Builder
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Decision 03
Research showed travelers don't just want individual recommendations — they want a narrative: a coherent day that flows geographically and thematically. Point-of-interest apps don't do this.
The Local Day feature lets verified locals build full-day itineraries (morning → afternoon → evening) tied to a neighborhood, a theme, or a mood. Travelers get a curated experience, locals get a canvas for storytelling about their city.
Design Rationale
Individual spots are data. A Local Day is a story. The product becomes more valuable when it shifts from information retrieval to experience curation — and more human when locals are the authors.
Testing was split across two user types: travelers and local contributors. Three rounds, 5 participants each. Core tasks: find a local recommendation in an unfamiliar neighborhood, and add a recommendation as a local.
Issue 01 · Round 1 · Travelers
4 of 5 users never switched to the Local view. They didn't notice the toggle in the map header, and one described it as "a settings thing, not a main feature."
Fix Applied
Promoted the toggle to the primary navigation bar with distinct icon states. Added an onboarding tooltip on first launch: "See this city through local eyes."
Issue 02 · Round 1 · Locals
All 5 local participants expressed discomfort with the original 7-step verification. Two dropped out. Common phrase: "This feels like I'm applying for something."
Fix Applied
Redesigned verification as a 3-step lightweight flow. Reframed the language: from "Verify Your Identity" to "Earn Your Local Badge." Completion rate improved to 4/5.
Issue 03 · Round 2 · Travelers
Users exploring multiple local spots couldn't tell if they were walkable or required transit. Discovery stalled because the spatial relationship between places wasn't communicated.
Fix Applied
Added walk-time and transit-time indicators directly on place cards in list view. Introduced the "Local Day" route builder in Round 3 as a full solution — a curated, geo-logical sequence of local spots.
Four core screens representing the complete traveler journey: Explore landing, Local map view, Place detail with Local Badge, and Local Day itinerary.
Explore
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01 · Explore
Let Discover
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02 · Local View
Place Detail
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03 · Place Detail
Map / Route
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04 · Local Day