BLRMap
A Bengaluru focused iteration of OpenStreetMap, with a lot of cool features in store!
StreetMap Bengaluru - Time-Aware Community Map for Bengaluru
Live Demo
Video Demo
Overview
StreetMap Bengaluru is a time-aware, community-driven mapping platform designed to help users discover places in Bengaluru based on intent, location, and time of day.
Unlike traditional maps, it combines structured data with user-generated insights to create a dynamic and evolving city exploration experience.
Problem Statement
Existing map platforms are:
Static and not time-aware
Focused on listings rather than intent
Weak in local discovery and hidden gems
Limited in community-driven insights
Users struggle to find relevant places based on real-world context like time, mood, and locality.
Solution
StreetMap Bengaluru introduces a 3D discovery model:
WHAT → Category (cafes, parks, metro, etc.)
WHERE → Locality + filters
WHEN → Time-based modes (morning, noon, evening, night)
Additionally, it integrates community contributions and personalized recommendations.
Core Features
Map Interface
Interactive map using Leaflet
Dynamic markers with clustering-ready structure
Hover → quick insights
Click → detailed place panel
Navigation System
Navbar (WHAT)
Normal Map
Cafe, Restaurant, Park, Metro, BMTC, etc.
Footer (WHEN)
Morning
Noon
Evening
Night
Auto-detected based on current time
Sidebar (WHERE)
Locality-based refinement
Geo-aware zooming
Nearby fallback for low-data areas
Recommendation Engine
Personalized place suggestions
Context-aware (time + filters + locality)
Click → focuses map and opens place details
UI redesigned to feel like a discovery engine, not an assistant
Add Place System
Minimal required fields with validation
Custom category support
Inline error handling (no silent failures)
Designed for fast contribution
Community Contributions
Multiple users can:
Add reviews (tips)
Upload photos
Add menu images
Ownership tracking per contribution
Users can delete their own content
Reviews System
Multiple reviews per place
Lightweight tip-based system (not heavy reviews)
Dynamic insights shown on map
Supports real-world experiences instead of static descriptions
Search System
Global place search in navbar
Direct access to existing place cards
Locality search with:
Enter-to-select
Auto zoom
Fallback suggestions
Filters (Refinement-first Design)
Filters repositioned as refinement tools (not discovery)
Works alongside recommendation engine
Supports:
Category
Locality
Tags
Time
UX Improvements
Map auto-focus on:
filter selection
recommendation click
category change
Coverage-aware empty states:
“Not many places documented here yet”
CTA: Add place / Discover
Technical Stack
Frontend
Next.js
React
Tailwind CSS
React Leaflet
Backend
Next.js API Routes
Database
MongoDB Atlas (Mongoose)
Maps
OpenStreetMap
Data Model (Simplified)
{
"name": "CTR Malleshwaram",
"category": "cafe",
"location": {
"type": "Point",
"coordinates": [77.5706, 12.9916]
},
"area": "malleshwaram",
"tags": ["morning", "breakfast"],
"description": "Iconic dosa spot",
"tips": ["Go before 8 AM", "Very crowded after 9"],
"reviews": [],
"photos": [],
"menuImages": []
}API Endpoints
GET /api/places
POST /api/places
POST /api/reviews
DELETE /api/reviews
Filters Supported
/api/places?category=cafe
/api/places?mode=morning
/api/places?area=indiranagar
/api/places?openNow=true
Development Progress (Incremental Work)
Phase 1
Map rendering
Basic markers
Static dataset
Phase 2
MongoDB integration
API routes
Dynamic data loading
Phase 3
Navbar category filtering
Sidebar filters
Footer time modes
Phase 4
Add place functionality
Validation and UX fixes
Phase 5
Recommendation engine
Map interaction improvements
Locality refinement system
Phase 6
Community contributions
Reviews, photos, menu support
Delete functionality
Unique Value
Time-aware mapping system
Recommendation-first discovery model
Community-driven insights instead of static listings
Geo-aware refinement and fallback logic
Dynamic map interaction (focus + navigation)
Future Scope
Social Layer
Full discussion threads per place
Media-rich contributions (photos/videos)
User profiles and contribution history
Recommendation Engine
Mood-based suggestions (work, relax, hangout)
Personalized ranking using user behavior
Hidden gem discovery
Map Intelligence
Heatmaps for popular areas
Real-time crowd/activity signals
Advanced geospatial queries
Contributors
Nandita — Backend, APIs, Frontend, Map UI, UX Design
Amrita — Backend, Database, Recommendation System
Conclusion
StreetMap Bengaluru transforms maps from static tools into dynamic, context-aware, and community-driven platforms.
It reflects how people actually explore cities - based on time, intent, and shared experiences.