Topical map — what's on PYC Hub and how it connects

PYC Hub is organised as a topical map, not a flat blog. Four things matter for how content sits next to each other:

1. The central entity is measured, not asserted. Everything here is about local digital visibility as a quantity: six pillars, published weights, scores anyone can recompute from the open data. The Local Digital Visibility Index is the centre of the map; the other clusters exist to explain, analyse and apply it.
2. Entity hubs. Each knowledge-base cluster is an entity — a topic with a hub page that lists the articles inside it. The hubs are the load-bearing structure; spokes link up to their hub, and the hub links back down.
3. The pillar is the bridge between clusters. Each of the six scoring pillars is simultaneously something the index measures, something the knowledge base explains, something the glossary gives you tools to analyse, and something PYC is paid to fix. That one hinge is what joins four otherwise separate bodies of content, and it is applied mechanically rather than by hand — see the mapping in src/lib/bridge.ts.
4. Predicate links, not generic anchors. Anchor text names the entity it points at — never “click here”, never “this article”. This is what lets search engines (and you) follow the entity graph.

The six knowledge-base clusters

• Getting Started — Foundations. Everything else assumes the ground covered here.
• General — Day-to-day practice: on-page, backlinks, content, local, the model of search itself.
• Advanced Solutions — Higher-leverage work for teams already shipping the fundamentals. International SEO, big-data analytics, deep keyword discovery.
• Integrations — Where SEO meets schema, paid search, video, content marketing, backlink intelligence.
• Troubleshooting — Diagnostic playbooks. Duplicate content, indexing failures, mobile breakage.
• Plugins — WordPress-specific tooling. Custom plugin development, security hardening, plugin-introduced bugs.

How the Glossary slots in

The Glossary catalogues twenty statistical and machine-learning methods that show up in modern SEO analysis — Bayesian inference, principal component analysis, ANOVA, k-nearest neighbours, ARIMA, regression, the optimisation family (AdaGrad, Adam, coordinate descent), and a few less obvious ones (Boltzmann machines, control-vector parameterisation, Dempster-Shafer theory).

Each glossary entry carries an Applied in the Knowledge Base block at the bottom, pointing to the knowledge-base articles that lean on it. The two clusters that bridge the most into the glossary:

• Advanced Solutions — the-power-of-big-data-in-seo and advanced-keyword-research-techniques together reference most of the glossary.
• Troubleshooting — resolving-indexing-and-crawling-issues, addressing-duplicate-content-issues use ANOVA, regression, Kolmogorov complexity, Dempster-Shafer for diagnostic reasoning.

The reverse map is in Methods referenced in this article, rendered on the relevant knowledge-base spokes.

One clarification the glossary itself now makes: these methods are not what computes the index. Scoring is plain arithmetic — ratios, weighted sums, a clamp — deliberately, so that any published figure can be recomputed from the open dataset without trusting a model. The glossary is the toolkit for interrogating results, and the maths pages say so.

The Blog and Local Indices clusters

• Blog — Ten posts on tactical topics: canonical tags, page speed, structured data, sitemaps, internal linking, social signals. The blog is the tactical layer; the knowledge base is the strategy layer.
• Local Indices — Recurring, data-driven league tables measuring how local businesses perform online: currently seven indices, 79 businesses, six pillars, refreshed quarterly with every change recorded in the changelog. The indices are not downstream of the rest — they are the centre. The knowledge base explains what each pillar measures, the glossary supplies the analysis toolkit, and the blog argues cases the index now tests.

Predicate summary

From	Predicate	To
Article	belongs to	Cluster hub
Cluster hub	contains	Sibling articles
Glossary entry	is applied in	KB Advanced Solutions / Troubleshooting articles
KB article	references method	Glossary entry
Index scorecard	is explained by	KB article for that pillar
KB article	is measured by	Index pillar, with the current cohort figure
Glossary entry	analyses	Index pillar data
Blog post	asserts a case tested by	Index pillar
Index pillar	is remediated by	A PYC service page
Case study	demonstrates	KB strategy + Glossary method on one client
Blog post	sits next to	Tactical sibling in the same cluster

This is the entity graph the site is built around. Every internal link names an entity and follows one of the predicates above.

The last predicate is the one that used to be missing. The hub published 159 pages of first-party measurement and linked to pyc.agency zero times, so the research absorbed authority instead of passing it to the practice it evidences. Every scorecard now names the two weakest pillars for that firm and links to the work that addresses them.