Schema.org now shows you how many sites are using each schema type

The folks over at Schema.org are now providing aggregate usage statistics for Schema.org terms across the public web. This will show you how many domains are using a specific schema/structured data element.

Schema.org announced, “we are pleased to share a new dataset providing aggregate usage statistics for Schema.org terms across the public web.” The dataset are updated monthly and they are aggregated at the domain level and presented in popularity range buckets. “This approach helps filter daily noise while highlighting meaningful adoption trends for researchers and toolmakers,” Schema.org wrote.

What it looks like. Here is a screenshot of two of these Schema.org pages,  author schema and event schema showing the usage statistics towards the top:

More about the data. Schema.org shared more information about the usage statistics over here, here is a summary:

• Schema.org term frequencies are measured within Google’s public web crawling infrastructure. The data is aggregated at the domain level (like example.com), not by individual pages. This means if you use the same term on 100 pages of your site, it still only counts as one domain using it.
• Instead of showing exact, raw numbers (which change daily and can be noisy), websites are grouped into range “buckets” (like “10K – 100K” domains). This keeps the data more stable and protects website privacy.
• HubThe raw files are available on the GitHub site at Google Public Stats dataset on GitHub. They are available in JSON and CSV with the same data and in a JSON summary format with aggregated bucket distributions. They are updated monthly.
• Term Type: The type of term. This is either Type (like “Person” or “Event”) or Property (like “price” or “telephone”).
• URI: The official URI of the term (for example: http://schema.org/Person).
• Domain Count Bucket: The range of unique domains using the term (for example: 100K - 1M domains).

Here is a screenshot of GitHub:

Why we care. Because we love data. Okay, outside of just loving data, knowing if a specific schema element is popular or not may be enough to convince your development team to implement that schema code on your website.