Lonely Planet Data API: Extract Structured JSON in 2026

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Get structured JSON data from Lonely Planet pages by defining a schema for fields like property_name, price_per_night, and rating, then POSTing to AlterLab's Extract API. The service handles anti-bot measures, returns validated typed JSON, and costs as little as $0.001 per extraction. No HTML parsing or selector maintenance required.

Why use Lonely Planet data?

Travel companies use Lonely Planet data to enrich recommendation engines with verified attraction details. Market analysts aggregate pricing and availability trends across destinations for competitive benchmarking. AI teams train location-aware models on structured travel descriptions, ratings, and amenity lists to improve contextual understanding.

What data can you extract?

Lonely Planet's public pages contain structured travel information suitable for extraction:

• property_name: Hotel, tour, or attraction name (string)
• price_per_night: Current rate displayed on the page (string, preserves currency formatting)
• rating: Aggregate score from user reviews (string, e.g., "4.5")
• location: City, region, or neighborhood context (string)
• availability: Real-time status like "Available" or "Sold out" (string)

These fields represent only what appears visibly on public pages—no login, payment, or private data access is involved.

The extraction approach

Direct HTTP requests to lonelyplanet.com return HTML that requires fragile CSS selectors prone to breaking with site updates. Parsing introduces maintenance overhead and risks missing dynamically loaded content. A data API approach shifts the complexity: AlterLab handles rendering, anti-bot evasion, and schema validation so you receive ready-to-use JSON. This transforms extraction from a parsing task into a simple API call with guaranteed output structure.

Quick start with AlterLab Extract API

Begin by installing the AlterLab SDK (Getting started guide). Define your target schema, then call the extract endpoint. Below is a Python example extracting core travel fields from a sample Lonely Planet page:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "property_name": {
      "type": "string",
      "description": "The property name field"
    },
    "price_per_night": {
      "type": "string",
      "description": "The price per night field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    },
    "location": {
      "type": "string",
      "description": "The location field"
    },
    "availability": {
      "type": "string",
      "description": "The availability field"
    }
  }
}

result = client.extract(
    url="https://lonelyplanet.com/example-hotel-page",
    schema=schema,
)
print(result.data)

Output:

{
  "property_name": "Boutique Hotel Luna",
  "price_per_night": "$189",
  "rating": "4.7",
  "location": "Barcelona, Spain",
  "availability": "Available"
}

For quick testing, use cURL directly:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://lonelyplanet.com/example-hotel-page",
    "schema": {
      "properties": {
        "property_name": {"type": "string"},
        "price_per_night": {"type": "string"},
        "rating": {"type": "string"}
      }
    }
  }'

Define your schema

The schema parameter uses JSON Schema Draft 07 to specify expected output types and descriptions. AlterLab validates extracted data against this schema, coercing values to match declared types (e.g., converting "4.7★" to "4.7" for a string rating field). This eliminates post-processing—your pipeline receives immediately usable typed JSON. Include only fields you need; omitting unused properties reduces cost and complexity. For nested objects or arrays, extend the schema with additional properties or items definitions.

Handle pagination and scale

For bulk extraction across multiple Lonely Planet pages:

1. Batch processing: Group URLs in async jobs using AlterLab's job endpoint (see Extract API docs)
2. Rate limiting: Stay within concurrency limits based on your plan; the API returns 429 if exceeded
3. Cost optimization: Use the /v1/estimate endpoint to preview expenses before extraction—critical for budgeting large pipelines
4. Error handling: Implement retry logic for 5xx errors; 4xx indicates schema or URL issues requiring fixes

Visit /pricing for volume discounts. Typical extraction costs range from $0.001–$0.01 per page depending on complexity and LLM usage for field interpretation.

Key takeaways

• Structured JSON extraction via schema validation is more reliable than HTML parsing for travel data
• AlterLab's Extract API handles compliance, rendering, and anti-bot measures so you focus on data usage
• Start with a minimal schema for core fields (property_name, price_per_night, rating) then expand as needed
• Always verify public data accessibility and respect rate limits when building pipelines

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