Previous Location which we provide by marking on map
Frequently visiting places
Current Location where you currenly live
More Results with score, reasonings, pros and cons
Results Recommendations

🌍 MoveWise — Our Project Story

✨ What Inspired Us

The idea for MoveWise came from a simple but widely experienced problem:
relocating to a new city.

When we move, we do not just change locations — we leave behind familiar places we trust, such as our gym, favorite café, or a reliable hospital. Most existing map-based applications focus on proximity, but they do not understand personal preference or past behavior.

We wanted to build something that answers a more human question:

“If we liked this place in our old city, where should we go in our new city?”

That question became the foundation of MoveWise.

🧠 What We Learned

Building MoveWise helped us grow across system design, geospatial reasoning, and AI integration.

Technical Learnings

Designing a location-aware backend using Node.js and Express
Working with Google Places and Distance Matrix APIs
Implementing reliable distance calculations using mathematical formulas
Integrating Gemini AI for structured comparison and ranking
Building APIs that are scalable, fault-tolerant, and frontend-friendly

Conceptual Learnings

Personalization creates more value than raw data
Distance represents user behavior, not just geography
AI becomes powerful when combined with deterministic logic
Clear backend design leads to a better user experience

🛠️ How We Built the Project

MoveWise was built as a multi-stage intelligent pipeline, where each step adds clarity and context.

1. Capturing User Context

We begin by understanding:

Where the user is moving from
Where the user is moving to
Which places they already trust

This allows us to build recommendations based on real preferences, not assumptions.

2. Preserving Travel Behavior with Distance Analysis

To preserve how far users are comfortable traveling, we calculate the distance between a trusted place and the previous city center.

When needed, we use the Haversine formula to compute the great-circle distance between two coordinates:

[ d = 2R \cdot \arcsin\left( \sqrt{ \sin^2\left(\frac{\Delta \phi}{2}\right) + \cos(\phi_1)\cos(\phi_2) \sin^2\left(\frac{\Delta \lambda}{2}\right) } \right) ]

Where: