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Case Study · 01

Local Radar

One search. Every local information.

Role
Full Stack Developer
Duration
2024 – 2025
Team
2 Members
Status
Live
Live Demo
Source
Overview01

A single lens for every place.

Local Radar is a location-based discovery platform designed to eliminate the need for users to visit multiple websites when searching for information about a specific location.

Instead of searching separately for jobs, restaurants, schools, fuel stations, public transport, local news or nearby services, users simply enter a location and receive all relevant information from a single platform.

The goal was to build a unified discovery experience that saves time and makes local information accessible through one search.

The Problem02

Finding information about a place is surprisingly fragmented.

Someone moving to a new city or visiting an unfamiliar location typically has to search across multiple platforms — one for jobs, another for restaurants, another for news, transport, fuel, schools, hotels and so on.

Each platform provides only one category of information, forcing users to switch between several websites before making informed decisions. This fragmented experience was the core problem we wanted to solve.

The Vision03

One location. Every answer.

A platform where a single location search can answer almost every local information need. Whether student, working professional, tourist, business owner or senior citizen — everyone should be able to discover a place from one interface.

Long term, Local Radar becomes a community-driven platform where users contribute and update local information, making the platform richer and more useful over time.

Solution04

Aggregate the open web into one clean interface.

Local Radar aggregates publicly available information from multiple trusted online sources and presents it through one clean interface. Instead of maintaining large amounts of manually entered data, the platform continuously collects publicly available information using automated web scraping pipelines and transforms it into a unified experience.

  • Jobs
  • Restaurants
  • Hotels
  • Schools
  • Colleges
  • Fuel Stations
  • Local News
  • Public Transport
  • Hospitals
  • Shopping
  • Tourist Attractions
  • Nearby Services
How It Works05

A data aggregation architecture.

  1. 01

    User searches for a location.

  2. 02

    Backend identifies the requested category.

  3. 03

    Dedicated scraping services collect publicly available information.

  4. 04

    Data is cleaned, normalized, and validated.

  5. 05

    Frequently requested information is cached with Redis.

  6. 06

    Frontend renders results in a consistent, readable format.

Technology Stack06

Built for speed, clarity and scale.

Frontend

  • React.js
  • Tailwind CSS
  • GSAP
  • Framer Motion

Backend

  • FastAPI
  • Python

Database

  • MongoDB

Cache

  • Redis

Data Collection

  • Playwright
  • BeautifulSoup
  • Requests
Key Features07
  • 01Unified location search
  • 02Real-time public data aggregation
  • 03Modern responsive interface
  • 04Fast API architecture
  • 05Redis caching layer
  • 06Location-based discovery
  • 07Multiple information categories
  • 08Scalable backend services
Challenges08

The hard parts, honestly.

Challenge · 01

Aggregating different data sources

Every website has a different layout, response structure and update frequency. Creating a common data model required careful normalization and transformation.

Challenge · 02

Maintaining data freshness

Because the platform depends on publicly available information, keeping results fresh required efficient scraping workflows and caching strategies.

Challenge · 03

Performance

Collecting data from multiple sources increases response time. Redis caching significantly reduced repeated requests and improved perceived speed.

Challenge · 04

Scalability

The architecture was designed so new categories can be added without touching the overall application structure, making growth easier to maintain.

Impact09

Many searches, one experience.

Local Radar transforms multiple independent searches into one unified experience. Instead of opening several websites, users access essential local information through a single platform, reducing search time and improving decision-making.

The modular architecture also enables the platform to expand into additional categories without major redesign.

Future Roadmap10

From discovery to local ecosystem.

  • Community-generated content
  • Business owner listings
  • User reviews and ratings
  • Personalized recommendations
  • AI-powered location assistant
  • Saved locations
  • Travel planning
  • Event discovery
  • Local marketplace
  • Public service information
  • User contribution system
What I Learned11
  • 01Designing scalable backend architectures
  • 02Building location-based products
  • 03Working with real-time data pipelines
  • 04Data normalization across sources
  • 05API design
  • 06Performance optimization with Redis
  • 07Responsive React experiences
  • 08End-to-end full-stack delivery

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