Scraping The Greater Cleveland Food Bank A Comprehensive Guide

by Sebastian Müller 63 views

Hey guys! Today, we're diving into a super important project – building a scraper for the Greater Cleveland Food Bank. This is a crucial step in making sure people have access to the resources they need. We'll go through everything from the initial checks to the nitty-gritty code, so let's get started!

Food Bank Information

Before we jump into the code, let's get familiar with the Greater Cleveland Food Bank:

Service Area

The Greater Cleveland Food Bank serves a wide area, including these counties:

  • ASHLAND, OH
  • ASHTABULA, OH
  • CUYAHOGA, OH
  • GEAUGA, OH
  • LAKE, OH
  • RICHLAND, OH

First Things First: Check for Vivery!

Alright, listen up! This is super important. Before we start writing any custom code, we need to check if the food bank is already using Vivery. Vivery is a platform that provides food bank search functionality, and we might already have a scraper for it. So, before you go any further:

  1. Visit the Find Food URL (https://www.greaterclevelandfoodbank.org/get-help/map).
  2. Look for these Vivery indicators:
    • Embedded iframes from pantrynet.org, vivery.com, or similar domains.
    • "Powered by Vivery" or "Powered by PantryNet" branding.
    • A map interface with pins showing food locations.
    • A search interface with filters for food types, days, etc.
    • URLs containing patterns like pantry-finder, food-finder, or pantrynet.

If you spot Vivery:

  • Close this issue and add a comment saying: "Covered by vivery_api_scraper.py"
  • Add the food bank name to the Vivery users list. This helps us keep track of which food banks we've already covered.

Why is this step crucial? Because we don't want to waste time building something that already exists! Plus, using a unified scraper like the vivery_api_scraper.py ensures consistency and efficiency. This is all about working smarter, not harder, guys!

Implementation Guide: Let's Get Scraping!

Okay, if the Greater Cleveland Food Bank isn't using Vivery, then it's time to roll up our sleeves and build a custom scraper. Don't worry, I'll guide you through it step by step.

1. Create the Scraper File

First up, we need to create a new Python file for our scraper. Name it app/scraper/www.greaterclevelandfoodbank.org_scraper.py. This naming convention helps us keep things organized and easily identifiable.

2. Basic Structure: The Foundation

Now, let's lay the foundation for our scraper. Open the file you just created and paste in this code:

from app.scraper.utils import ScraperJob, get_scraper_headers

class GreaterClevelandFoodBankScraper(ScraperJob):
    def __init__(self):
        super().__init__(scraper_id="www.greaterclevelandfoodbank.org")

    async def scrape(self) -> str:
        # Your implementation here
        pass

Let's break this down:

  • We're importing ScraperJob and get_scraper_headers from our utils module. ScraperJob is a base class that provides a lot of the boilerplate functionality we need for a scraper, like managing the scraping lifecycle and handling errors. get_scraper_headers is a handy function that gives us standard HTTP headers to use in our requests.
  • We're creating a class called GreaterClevelandFoodBankScraper that inherits from ScraperJob. This is where all our scraping logic will go.
  • The __init__ method is the constructor for our class. We're calling the super().__init__ method to initialize the ScraperJob base class, passing in a scraper_id that uniquely identifies our scraper.
  • The scrape method is where the magic happens. This is the method that will actually fetch the data from the website and extract the information we need. Right now, it's just a placeholder – we'll fill it in soon.

3. Key Implementation Steps: The Heart of the Scraper

This is where things get interesting! We need to figure out how to extract the food resource data from the Greater Cleveland Food Bank's website. Here's a breakdown of the steps we'll take:

  1. Analyze the food finder page: Go to the Find Food URL (https://www.greaterclevelandfoodbank.org/get-help/map) and take a good look around. How is the data presented? Are there lists, maps, or search forms? This will give us clues about how to extract the information.

  2. Determine the data source type: This is crucial! We need to figure out where the data is coming from. Here are the most common possibilities:

    • Static HTML with listings: The data is embedded directly in the HTML of the page. This is the simplest case – we can use libraries like BeautifulSoup to parse the HTML and extract the data.
    • JavaScript-rendered content: The data is loaded dynamically using JavaScript. This is a bit more complex – we might need to use a headless browser like Selenium to render the JavaScript and get the data.
    • API endpoints: The data is fetched from an API. This is often the cleanest and most reliable way to get data – we can make direct API requests and get the data in a structured format like JSON.
    • Map-based interface with data endpoints: The data is displayed on a map, and the map data is fetched from an API. This is similar to the API endpoints case, but we need to figure out how the map interacts with the API.
    • PDF downloads: The data is in a PDF file. This is the trickiest case – we'll need to use a PDF parsing library to extract the data.

    To figure out the data source type, use your browser's developer tools (usually by pressing F12) and go to the "Network" tab. This will show you all the network requests the page is making. Look for API calls (requests that return JSON data) or other clues about where the data is coming from.

  3. Extract food resource data: Once we know the data source type, we can start extracting the data. We need to get the following information for each food resource:

    • Organization/pantry name: The name of the food pantry or organization.
    • Complete address: The full street address.
    • Phone number (if available): A contact phone number.
    • Hours of operation: The days and times the food resource is open.
    • Services offered (food pantry, meal site, etc.): What services does the food resource provide?
    • Eligibility requirements: Any requirements people need to meet to access the food resource.
    • Additional notes or special instructions: Any other important information, like what to bring or how to register.

  4. Use provided utilities: We've got some handy utilities to make your life easier:

    • GeocoderUtils: This is for converting addresses to coordinates (latitude and longitude). We need coordinates so we can display the food resources on a map.
    • get_scraper_headers(): Use this to get standard HTTP headers for your requests. This helps make your scraper look like a normal web browser.
    • Grid search (if needed): If the food bank uses a map-based interface and you need to search a large area, you can use self.utils.get_state_grid_points("OH") to generate a grid of points to search.

  5. Submit data to processing queue: Once you've extracted the data, you need to submit it to our processing queue. This is how we get the data into our system. Here's how you do it:

    for location in locations:
    json_data = json.dumps(location)
    self.submit_to_queue(json_data)

    We're looping through a list of locations (each location is a dictionary containing the data for one food resource), converting each location to a JSON string using json.dumps, and then submitting it to the queue using self.submit_to_queue.

4. Testing: Making Sure It Works

Testing is crucial! We need to make sure our scraper is working correctly before we deploy it. Here's how you can test your scraper:

# Run the scraper
python -m app.scraper www.greaterclevelandfoodbank.org

# Run in test mode
python -m app.scraper.test_scrapers www.greaterclevelandfoodbank.org
  • The first command runs the scraper and submits the data to the queue. You can then check the queue to see if the data is there and if it looks correct.
  • The second command runs the scraper in test mode. This will print the scraped data to the console instead of submitting it to the queue, which is useful for debugging.

Essential Documentation: Your Resources

We've got some great documentation to help you out. Check these out:

Scraper Development

  • Implementation Guide: docs/scrapers.md - This is a comprehensive guide with lots of examples. Read this carefully!
  • Base Classes: app/scraper/utils.py - This file contains the ScraperJob, GeocoderUtils, and ScraperUtils classes. These are your friends – get to know them!
  • Example Scrapers: We've got several example scrapers that you can use as a starting point:
    • app/scraper/nyc_efap_programs_scraper.py - This scraper shows how to scrape data from an HTML table.
    • app/scraper/food_helpline_org_scraper.py - This scraper shows how to do a ZIP code search.
    • app/scraper/vivery_api_scraper.py - This scraper shows how to integrate with a Vivery API.

Utilities Available

  • ScraperJob: The base class for all our scrapers. It provides scraper lifecycle management, error handling, and other useful features.
  • GeocoderUtils: A utility class for converting addresses to lat/lon coordinates.
  • get_scraper_headers(): A function that returns standard HTTP headers for your requests.
  • Grid Search: A technique for searching map-based interfaces by dividing the search area into a grid of points.

Data Format: What We Need

Scraped data should be formatted as JSON. This is super important for consistency and for our system to process the data correctly. Here are the fields we need (when available):

{
    "name": "Food Pantry Name",
    "address": "123 Main St, City, State ZIP",
    "phone": "555-123-4567",
    "hours": "Mon-Fri 9am-5pm",
    "services": ["food pantry", "hot meals"],
    "eligibility": "Must live in county",
    "notes": "Bring ID and proof of address",
    "latitude": 40.7128,
    "longitude": -74.0060
}

Make sure your scraper outputs data in this format!

Additional Notes: Pro Tips!

  • Some food banks may have multiple locations or programs. Make sure your scraper gets them all!
  • Check if the food bank has a separate mobile food schedule. This is often listed in a different place than the regular food pantry schedule.
  • Look for seasonal or temporary distribution sites. These are especially important during holidays or emergencies.
  • Consider accessibility information if available. This can include things like wheelchair accessibility, language services, and dietary accommodations.

Let's Do This!

Alright guys, that's the roadmap for implementing a scraper for the Greater Cleveland Food Bank. It might seem like a lot, but take it one step at a time, and don't be afraid to ask for help. Remember, this is a really important project that will help people in need. Let's get to work!