On Saturday, 18 April 2026, the 1926 Census of Ireland was released to the public for the first time. The records became freely searchable through the National Archives of Ireland exactly 100 years after census night, opening the first census taken after the establishment of the Irish Free State.
Public interest was immediate. The National Archives had prepared hundreds of thousands of household and enumerator returns for online access, and the Irish Times reported 20 million website hits, 1 million individual visits, and 1 million downloads after the records went live.
Search made the national collection accessible. I wanted to rebuild part of it around one place.
Ardmore 1926 is a local-history GIS site for the Ardmore and Grange area of County Waterford. It takes the census returns for 46 townlands and turns them into a browsable map, household index, townland explorer, names view, and statistics dashboard.
From National Archive to Local Place
The 1926 Census records people at household level. It gives names, ages, relationships to the head of household, religion, Irish-language ability, birthplace, marital status, and links back to the scanned census forms.
For family history, that level of detail is immediately useful. You search for a person, open a household return, and see where they were on census night.
Local history benefits from a wider view. A single return tells one household story; a townland-level dataset begins to show how a community fitted together.
The Ardmore site is built around that wider view:
- households by townland
- Irish-language patterns
- surname and first-name frequency
- local-born share
- household structure
- differences between Ardmore village and surrounding rural townlands
The public National Archives search remains the source. Ardmore 1926 adds a local interpretation layer designed around browsing a place as well as finding a record.
What You Can Explore
The site follows the ways people usually explore a place.
The overview page starts with the townlands. Each card gives a quick summary and, where available, the Irish placename and meaning from Logainm. From there, you can jump into a townland page and see its households, age profile, Irish-language figures, and basic demographic breakdown.
For direct record browsing, the household page is the main route in. You can search by name, filter by townland, or filter by household type. Each household expands to show its members, their relationships, ages, language records, birthplaces, and links back to the National Archives record.
Charts and tables pull the area together on the statistics page: age structure, Irish-language share by townland, household sizes, household types, surnames, religion, and other summary views.
The names page handles a more human kind of pattern spotting. It shows common male first names, female first names, surnames, and migrant origins. Some results are expected; others are small details that make the place feel less abstract.
Figure 1: Surname frequencies and recorded migrant origins from the Ardmore 1926 names page.
The GIS centre of the site is the map. It shows the 46 census townlands as polygons, with surrounding townlands included as a quieter context layer so the Ardmore area does not float in empty space. The default layer maps the share of people recorded as speaking Irish, with options for population density, mean age, local-born share, and average household size.
The Dataset Behind the Site
The published Ardmore site has no live census-processing step. Everything has already been cleaned, filtered, grouped, and exported into static files.
The current Ardmore export contains:
- 1,146 people
- 277 households
- 46 census townlands
- 46 mapped census townland polygons
- 107 adjacent townland polygons for map context
Static files keep the site fast and simple. The browser fetches prebuilt JSON and GeoJSON, then renders the pages, charts, filters, and map. Runtime infrastructure stays minimal: no backend API, database server, or live data processing.
The processing work happened earlier in a separate census workflow. I started from a Waterford county extract from the National Archives API, cleaned it, filtered it to the Ardmore area, and exported the site data.
The census data still needed checking, even where the source supplied updated_* fields. Those fields were extremely useful, but they were not automatically analysis-ready.
Cleaning Without Pretending
I wanted the data to stay visibly imperfect where the evidence was imperfect.
Relationship-to-head values are a good example. They are essential for understanding households, and the raw values were messy. Some were straightforward: head, wife, son, daughter, servant, boarder. Others included spelling variation, Irish-language terms, OCR noise, and values too damaged to classify safely.
For the site, I grouped relationships into broad categories such as Head, Spouse, Child, Grandchild, Servant, Boarder, Visitor, Other family, and Unclassifiable. Those categories support household browsing and broad household-type summaries while avoiding claims about every individual kinship link.
Birthplace needed the same kind of restraint. Many Waterford variants could be normalised confidently, but not every fragment or OCR-damaged value could be repaired. Where the evidence was weak, the value stayed unknown.
Some fields were deliberately kept out of the spotlight. The fertility and child-survival fields had enough corruption in the wider Waterford extract that a public local dashboard would need much narrower review before using them. I left those charts out of the main site.
Households also needed attention. The census used form names as household identifiers, but some larger households continued onto addendum pages. Those pages could look like separate headless households unless they were linked back to the parent form. The cleaning script merged addendum pages into parent households where the file naming pattern and image group made that defensible.
The aim was honest data: browsable, explainable, and always linked back to the original record.
Defining Ardmore
One of the trickiest GIS decisions was the boundary itself.
Census administration in 1926 relied on District Electoral Divisions and other formal geography. Local identity follows looser boundaries. A parish, village, hinterland, DED, and townland set can overlap without matching exactly.
For this site, Ardmore is defined as a local-history area. It includes the core Ardmore, Grange, and Glenwilliam area, with a small number of neighbouring townlands added where they clearly belong to the Ardmore hinterland. Some townlands from otherwise included administrative units are excluded where they sit farther from the local area being represented.
That makes the boundary an editorial and historical choice. There was no single official Ardmore census unit in 1926. The site tries to be transparent about that, because boundaries shape every map and every denominator.
Mapping Townlands
OpenStreetMap townland geometry provided the map boundaries, with verified relation IDs used for matching.
Townland names repeat, spellings vary, and a name like Newtown or Ballyquin can easily point to the wrong place if it is matched automatically. For the Ardmore map, I used manually verified OpenStreetMap relation IDs for the census townlands, then attached census counts and summary statistics to each polygon.
Surrounding townlands use a separate context layer. They are visible on the map so the area makes spatial sense, with different styling to keep them outside the Ardmore census dataset.
Irish placenames came through a separate Logainm workflow. The site uses Logainm names and glossary meanings where they could be matched confidently. In several cases, census spellings, OpenStreetMap names, and Logainm records did not line up neatly, so manual overrides and review were part of the work.
Here, GIS means more than drawing shapes. The map depends on data cleaning, placename reconciliation, local boundary decisions, and geometry checks agreeing with one another.
Building a Static Census Site
The public site keeps the architecture simple.
It is a Vite and TypeScript static app. Leaflet handles the map. Chart.js handles the charts. Each page fetches static files from public/data/: household records, statistics, townland polygons, and context polygons.
That architecture suits the project. The data changes only when the processing pipeline is rebuilt, so a live application server would add little value. Static hosting also keeps the site easy to serve and quick to load.
Most of the hard work has to happen before publication. Household classification, townland summaries, Irish-language percentages, birthplace normalisation, placename enrichment, and GeoJSON exports all need to be prepared ahead of time. The browser can then focus on browsing rather than rediscovering the dataset every time someone opens the page.
Why This Matters
A national dataset carries its force locally. People look for grandparents, streets, farms, neighbours, family names, and familiar townlands. The records connect a national historical moment to ordinary households.
For Ardmore, the census captures a coastal community in the early years of the Irish Free State. It shows who was living in the village, who was spread across the surrounding townlands, which households were extended, who was born locally, and where Irish was recorded in everyday use.
Putting that on a map changes the experience. You can read individual records, then move through the area as a place.
Beyond Ardmore
Ardmore is the first public expression of a larger workflow.
Since building it, I have been extending the same ideas across the full 26-county 1926 Census dataset in a separate project called census-26. That work now includes county-level raw downloads, a SQLite analytic mart, QA reports, cleaning rules, birthplace and migration readiness checks, Logainm-based geography reconciliation, and household-quality review.
The national workflow is more cautious than a public showcase needs to be. It keeps uncertainty visible through explicit eligibility fields, warning flags, review queues, and documented caveats. Current household work looks at where counties such as Cavan and Donegal can support household-composition analysis, where they need warnings, and where only smaller DED-level subsets are safe to use.
That is the direction I want future local-history projects to take: reproducible local datasets with clear denominators, visible uncertainty, useful maps, and links back to the source material.
The 1926 Census release gives Ireland a remarkable new public archive. Ardmore 1926 shows one way to bring that archive back down to the scale of a local community.