Hiker 🥾

A hiking trail explorer for Israel with an interactive map and filtering system.

Overview

Hiker is a web-based application that aggregates hiking trails from multiple sources and presents them on an interactive map. Users can filter trails by difficulty, length, and tags to find their perfect hike.

What's Inside

🖼️ Frontend - Vibe-coded vanilla JavaScript + HTML/CSS

• Interactive Leaflet map
• Hebrew-language interface
• Real-time filtering by distance and tags
• Responsive design with collapsible sidebar
• Trail details popup

🐍 Backend - Hand-written Python scrapers

• Parallel scraping from multiple hiking data sources (Parks.org.il, Tiuli, kkl
• Data aggregation and normalization
• Exports to JSON for use by the frontend

Features

• 🗺️ Interactive Map - Browse trails on a Leaflet map with markers
• 🔍 Smart Filtering - Filter by trail length and tags (e.g., dog-friendly, water, scenic)
• 📊 Sorting - Sort trails by name, length, difficulty, or geography
• 📱 Mobile Responsive - Works on desktop and mobile devices
• 🌐 No Backend Required - Static site deployed via GitHub Pages (data served as JSON)

Getting Started

Running the Data Scraper

cd backend
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
python main.py

This generates hikes.json with the latest trail data.

Running Locally

The frontend is a static site. Serve it locally:

# Using Python
cd docs
python -m http.server 8000

# Or use any other static server
# Visit http://localhost:8000

Data Sources

• Parks - Israeli national parks and nature reserves
• Tuuli - Community hiking database
• KKL - Jewish National Fund trails

Transparency Notes

Frontend Code is intentionally informal and flexible ("vibe-coded"):

• Prioritizes functionality and rapid iteration
• Not heavily structured or commented

Backend Code is hand-written without external frameworks:

• Custom scrapers tailored to each source's HTML structure
• No database layer, so the site can be hosted static
• Direct BeautifulSoup parsing

Both approaches work well for this project's scale and serve as reference implementations.

Development

To add a new data source:

1. Create a new scraper file in backend/ (e.g., new_source.py)
2. Implement get_all_hikes() function returning list[HikeInfo]
3. Import and add to parallel threads in main.py
4. Run main.py to generate updated hikes.json

Deployment

The frontend is hosted on GitHub Pages via the docs/ folder. Push to trigger automatic deployment.

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Happy Hiking ⛰️🏕️🥾