Beyond the Whiteboard: Building a Structured ANZSCO Dataset with BFS

In computer science lectures, we often treat algorithms as abstract concepts on a whiteboard. However, real-world data is messy and unstructured. While building a dataset for the Australian and New Zealand Standard Classification of Occupations (ANZSCO), I had to implement a fundamental graph traversal strategy—the **Breadth-First Search (BFS)** algorithm—to transform a complex web hierarchy into a linear, analyzable dataset.

The ANZSCO is not a flat list; it is a tree graph defined by the Australian Bureau of Statistics (ABS). To visit every node without losing track of parent-child relationships, we must respect its five hierarchical levels:

• **Major Group (1-digit):** The broadest level (e.g., Professionals).
• **Sub-Major Group (2-digit):** Subdivision (e.g., Health Professionals).
• **Minor Group (3-digit):** Further refinement (e.g., Medical Practitioners).
• **Unit Group (4-digit):** Specific groupings (e.g., General Practitioners).
• **Occupation (6-digit):** The atomic unit and 'Leaf' of our tree graph.

When facing a tree traversal problem, BFS is superior for web scraping when depth is unknown. BFS explores the graph layer by layer, ensuring we capture all Major Groups before moving to Sub-Major groups. It operates like ripples in a pond—expanding outward uniformly to maintain data quality level-by-level.

The engine driving BFS is the Queue data structure. By maintaining a 'frontier' of URLs to visit, the scraper remains systematic and disciplined.

# The Core BFS Logic for Hierarchy Traversal
queue = [root_url] # ABS Index Page
visited = []

while queue:
    # Dequeue: First-In-First-Out (FIFO)
    current_url = queue.pop(0) 
    
    # Process & Extract Data
    soup = fetch_and_parse(current_url)
    
    # Enqueue Children: Find sub-links (next level)
    child_links = soup.find_all('a', class_='hierarchy-link')
    for link in child_links:
        if link not in visited:
            queue.append(link) # Add to back of the queue
            visited.append(link)

The result of this algorithmic approach is a structured dataset where every occupation carries its full lineage. This modularity allows for robust debugging and avoids the stack depth limits often encountered in recursive DFS scrapers.

• **Robustness:** Uses iterative while-loops instead of recursion.
• **Data Integrity:** Row-level lineage (Major > Sub-Major > Minor > Unit > Occupation).
• **Separation of Concerns:** Production-ready scripts (scrape_occ_abs.py) vs. Experimental notebooks (linkless.ipynb).

This project bridges the gap between academic theory and practical utility. As a computer science student, I realized that algorithms are tools that dictate how we interact with the digital world. The next step is scaling this architecture with concurrency using `asyncio` to speed up the traversal process.