NARA's enumeration district index covers roughly 50,000 districts across the 1790 to 1950 censuses. Researchers usually arrive from a county-and-year search and need to know which microfilm reel covers their district, what the population count was, and which towns the district included. That context lives only inside aggregator search results, not on its own URL.

SleekRank reads the NARA district index and emits one WordPress page per district per year. Each page carries district number, county, state, population, microfilm reel, boundary description, and a Place schema block, all driven by tag and selector mappings on a shared base. The URL pattern is /census/{slug}/, and new releases appear after the next cache refresh.

The operational win is that NARA already publishes the index. SleekRank reads it directly without re-importing into a CMS database. Researchers land on the district they want, the historical society keeps the traffic, and the 50,000-page corpus stays in sync with NARA on every refresh.

Enumeration district research is one of the highest-intent traffic categories in genealogy. Researchers search for the exact intersection of year, county, and district, and most queries are very specific. Aggregators currently own the category because they own the index workflow, not because their per-district pages match the precision of the query.

NARA itself publishes the canonical index, but the public-facing experience funnels everything through a search box rather than a per-district URL. SleekRank closes that gap by turning the same NARA file into a per-district landing page corpus on a historical society's own domain. Each district becomes its own URL that ranks for county, state, year, and district-number searches.

Internal links across districts and counties build a structural network that strengthens the society's authority. Place and Dataset schema make every district eligible for entity panels and enhanced results. The corpus stays current with NARA on every cache refresh, while contributors continue improving boundary narratives through the underlying spreadsheet.

Aggregators win on breadth; the per-district page wins on precision and on citation, which is exactly what census researchers need from a public archive.