Muniq

Muniq turns Munich into a personalized map. Set what matters – green, quiet, mobility, rent – and instantly see which neighborhoods fit your lifestyle best, backed by real data and clear insights.

Comment

🏙️ Muniq — Find Your Place in Munich, Powered by Data

🎯 Inspiration

Finding a home in Munich is stressful, and most housing platforms only show basic details like price and square meters. They don’t answer deeper questions:

  • How quiet is this area?
  • How green is it?
  • Is it family-friendly or student-heavy?
  • How good is public transport here?
  • What’s the air quality like?

Munich already offers high-quality open data, but it’s scattered and hard to use.
Muniq makes this data intuitive, visual, and personal.

📱 What Muniq Does

🌈 Interactive Heatmap

Districts are color-coded from green → red based on how well they match the user’s priorities.

🔧 Drag & Drop Priorities

Users can reorder what matters most:

  • Quietness
  • Green space
  • Public transport
  • Bike infrastructure
  • Rent affordability
  • Air quality
  • Family-friendliness

The heatmap updates instantly.

🔍 District Insights

Each district includes:

  • Fit score
  • Environmental metrics
  • Mobility metrics
  • Rent & density trends
  • A concise description
  • Optional overlays (noise, bike lanes, PT stops)

🧭 Cross-Platform App

Built using Compose Multiplatform, Muniq runs on:

  • Android
  • iOS
  • Web (WASM)

with native Google Maps on each platform.

🛠️ How We Built It

📱 Frontend — Kotlin Multiplatform

Key technologies:

  • Kotlin Multiplatform (shared logic + models)
  • Compose Multiplatform (shared UI)
  • Google Maps SDK (wrapped with expect/actual)
  • Koin for dependency injection
  • Ktor for networking
  • Material 3 design system
  • Localization (EN/DE/RU)
  • Light/Dark mode

UI features:

  • Safe-area aware floating buttons
  • Animated sidebar
  • Priority editor with drag-and-drop
  • Bottom sheet (peek → half → full)
  • Clean, minimal design

🔧 Backend — Kotlin + Spring Boot + Firestore

Backend code lives in muniq/backend/.

Technologies used:

  • Kotlin 2 + Spring Boot 3
  • Spring Web (REST)
  • Kotlinx Serialization
  • Firestore (Firebase Admin SDK)
  • Docker
  • Google Cloud Run

Backend Layout

  • Model: GreenRating.kt
  • Repository: GreenRatingRepository.kt
  • Service: GreenRatingService.kt
  • Controller: GreenRatingController.kt

API Endpoints

GET /api/green-ratings GET /api/green-ratings/{id}

Why Firestore?

  • Easy to work with district-level documents
  • Automatically scalable
  • Fast to iterate on

🧮 Data Pipeline

Data scripts live in muniq/scripts/.

Sources

We use official Munich datasets:

  • Noise
  • Air quality
  • Bike lanes
  • Public transport
  • Green spaces
  • Rent
  • Childcare
  • Population density

Steps

  1. Raw datasets → district metrics
  2. Normalize all metrics to a 0–100 scale
  3. Merge into district vectors
  4. Upload to Firestore

☁️ Deployment — Google Cloud Platform

  • Cloud Run for hosting (autoscaling + HTTPS)
  • Artifact Registry for container images
  • Firestore for data
  • Secret Manager for config
  • GitHub Actions for CI/CD

🧗 Challenges

  • Integrating Google Maps on iOS using a Compose wrapper
  • Handling drag-and-drop in a draggable bottom sheet
  • Normalizing datasets with completely different scales
  • Managing shared fonts/resources across platforms
  • Consistent safe-area handling (notches, bars, islands)

🏆 Accomplishments

  • Fully working cross-platform app
  • Real-time map scoring
  • Clean backend + data pipeline
  • WASM version running in browser
  • Architecture ready for other cities
  • Polished UI matching our design spec

📚 What We Learned

  • Compose Multiplatform is production-ready
  • Native map interoperability works well
  • Open data becomes powerful when visualized correctly
  • Firestore is a great fit for flexible geographic data
  • Shared Kotlin code accelerates development significantly

🚀 What’s Next

Apartment Listings

Connect district results with real housing listings.

More Cities

Architecture scales naturally to:

  • Berlin
  • Vienna
  • Zurich
  • Hamburg

Community "Vibe" Scores

Crowdsourced insights about nightlife, coffee, walkability, safety.

AI-Powered Recommendations

Automatically suggest districts based on user behavior.

🎉 Final Thoughts

Muniq transforms open city data into a simple, personal tool for discovering where in Munich you’d feel most at home.

It’s not just "Where can I rent?"
It’s "Which part of the city fits me?"

Built With

Share this project:

Updates