SpotIQ: Data-Driven Restaurant Location Intelligence

Introduction

Finding the right spot for a restaurant can make or break the business. Foot traffic, competition, audience demographics, and local spending habits all play a role — but manually analyzing these factors is time-consuming and error-prone.

SpotIQ is a web-based tool I built to simplify this process. It allows a restaurateur to quickly evaluate neighborhoods and find the best location for a specific cuisine and target audience.

Architecture Overview

SpotIQ is built as a modern full-stack application that processes real-world data and presents it through an intuitive interface:

Frontend Stack:

• Next.js + React for dynamic user interactions

• Tailwind CSS + shadcn/ui for clean, responsive design

• Leaflet for interactive map visualizations

Backend & Data:

• Next.js API routes for serverless processing

• OpenStreetMap via Overpass API for POI data

• NYC Open Data for pedestrian traffic patterns

• Custom scoring algorithms with normalization

The User Experience

The flow is intentionally simple:

1. Input: User selects area, cuisine type, and optional filters (target audience, budget level)

2. Processing: Backend aggregates multiple data sources and calculates scores

3. Results: Interactive map with area highlighting and actionable insights

No authentication required — users get immediate value without friction.

The Intelligence Layer: Scoring Algorithm

The core of SpotIQ is its scoring system that transforms raw location data into business intelligence:

Data Collection

Each neighborhood is defined by a bounding box, and I query for:

• Office buildings and business districts

• Transit hubs and subway stations

• Existing restaurants (direct and indirect competition)

• Real pedestrian traffic counts

Smart Normalization

Raw counts are normalized using caps to prevent outliers from skewing results:

normalized = Math.min(count / cap, 1);

Weighted Scoring

Different factors get different weights based on business impact:

• Positive factors: Transit access (+0.3), office density (+0.25), foot traffic (+0.3)

• Negative factors: Direct competition (-0.2)

Example Calculation

Offices: 0.45 × 0.25 = 0.1125
Transit: 0.20 × 0.3 = 0.06  
Competition: 0.2 × (-0.2) = -0.04
Foot Traffic: 0.85 × 0.3 = 0.255

Final Score: (0.1125 + 0.06 - 0.04 + 0.255) × 10 = 3.88/10

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Technical Deep Dives

Challenge 1: Client-Side Map Rendering

Leaflet requires browser APIs that don't exist during server-side rendering.

const MapPreview = dynamic(() => import("@/components/MapPreview"), { 
  ssr: false 
});

This ensures maps only render client-side while maintaining Next.js performance benefits.

Challenge 2: External API Integration

I encapsulated all external data fetching in dedicated library functions:

• Overpass API queries for POI data

• NYC Open Data API for pedestrian patterns

• Error handling and rate limiting built-in

Challenge 3: Real-Time Data Processing

The /api/calculateScore endpoint processes multiple data sources simultaneously, normalizes results, and returns both numerical scores and contextual insights — all in under 2 seconds.

Generating Actionable Insights

Beyond just numbers, SpotIQ provides context:

• High pedestrian traffic → "Strong potential for casual dining"

• Many offices nearby → "Excellent lunch and dinner opportunities"

• Heavy competition → "Consider niche differentiation strategies"

• Transit accessibility → "Easy customer access from multiple areas"

This transforms data into business strategy recommendations.

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Future Enhancements

• Additional Scoring Factors: Incorporate audience demographics (age, profession) and average spending per area to refine scores.

• Visual Insights: Heatmaps, top cuisines per area.

• Multi-City Support: Expand beyond NYC.

• User Accounts: Save past searches, preferred cuisines, and custom weighting.

• Advanced CI/CD: Vercel integration with automated tests and monitoring.

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Key Takeaways

Building SpotIQ taught me that effective business intelligence applications need three things:

1. Solid Data Foundation: Multiple reliable sources, proper normalization

2. Smart Processing: Algorithms that reflect real business dynamics

3. Clear Presentation: Complex analysis presented as simple, actionable insights

The combination of modern web technologies (Next.js, React, Tailwind) with thoughtful data science created a tool that turns hours of manual research into seconds of intelligent analysis.

SpotIQ demonstrates that powerful business intelligence doesn't require enterprise-grade complexity — sometimes the best solutions are elegantly simple tools that solve real problems with clean code and smart algorithms.

Project Links

• GitHub Repository: SpotIQ on GitHub

• Live Demo: https://spot-iq.vercel.app/