Pulse Atlas

1Why absolute scores#

Every indicator in the atlas is scored on a fixed scale from 1 to 5. The scale is absolute: each indicator has a fixed lower and upper reference value — goalposts — and a region's score is where its value falls between them. A region is scored against the goalposts, not against other regions.

This is the property the whole atlas depends on: an unchanged value means an unchanged score. If a region's unemployment rate is the same next year, its unemployment score is the same, whatever happened elsewhere in Europe.

Rank and percentile scoring behaves differently: a region can improve on every measure and still fall, because other regions improved faster. Where a region stands is still worth knowing, so the atlas shows each region's position within its country and within the EU. That position is reported on its own; it is never folded into the score.

The mechanics, in full: goalposts are set from robust percentiles (p2/p98) of a multi-year, EU-wide baseline — never the raw minimum and maximum, which a single outlier can distort, and never an indicator's definitional bounds. A share cannot leave 0–100, but real European values fill only a slice of that range, so definitional goalposts would compress every region into the top bands. A value is placed linearly between its goalposts; values beyond a goalpost are clipped to it; indicators where higher is worse (unemployment, road deaths) are inverted; the result is a continuous score from 1 to 5. The five color bands on the map are that score cut into equal steps — a presentation of the score, not a second scale. Goalposts are frozen and versioned: changing one bumps the method version (see sources).

Beside every score the atlas shows the raw value and its reference year, so you can check the score against the source.

2What the atlas measures#

The atlas measures 10 Eurostat indicators for 244 European regions and publishes a composite for the 241 that clear the coverage gate (see missing data). Regions are NUTS-2 — the EU's standard statistical regions, the level between country and province: Andalucía, Île-de-France, Lombardia.

Most of the panel is scored, across 5 dimensions of well-being:

• Income — household net disposable income per inhabitant (nama_10r_2hhinc).
• Employment — employment rate, ages 20–64 (lfst_r_lfe2emprt); unemployment rate, ages 15–74 (lfst_r_lfu3rt).
• Education — tertiary attainment, ages 25–64 (edat_lfse_04); young people not in employment, education, or training, ages 15–24 (edat_lfse_22).
• Health — life expectancy at birth (demo_r_mlifexp); road deaths per million inhabitants (tran_r_acci).
• Inclusion — people at risk of poverty or social exclusion (ilc_peps11n).

The rest are context lenses — scored and shown, but kept out of the composite: GDP per inhabitant (nama_10r_2gdp), because regional output is not the same thing as residents' living standards, and households with internet access (isoc_r_iacc_h), because several countries report the underlying survey only above the regional level.

Every indicator's table code, the exact filters used, its unit, its direction (whether higher is better or worse), and its reference year are recorded in the published data files; the atlas shows the table code and the reference year beside every value.

3How scores combine#

Aggregation happens in two steps, and the two steps use different math on purpose.

Within a dimension, indicator scores are averaged arithmetically. The indicators inside a dimension are close substitutes, and a plain average reads the way you'd expect.

Across dimensions, the overall composite is the geometric mean of the dimension scores. The geometric mean limits substitution: a strong score in one dimension cannot fully compensate for a weak score in another. That is deliberate for a well-being measure — a serious shortfall in one dimension should stay visible in the composite, not be offset by strength elsewhere.

The dimensions carry equal weight. Equal weighting is a choice, not a neutral default: the atlas takes no stance on whether health matters more than income, or education more than employment. If you would weight the dimensions differently, you can — every raw value and every per-dimension score is published.

The composite is deliberately a secondary lens. The atlas leads with the multi-dimensional profile, and no surface reduces a region to one number.

4Missing data#

The atlas never imputes a missing value — no modeling a plausible number from neighbors, no carrying an old value forward, no filling from a national average. An imputed value can't be checked against a source, so the atlas leaves the gap visible instead.

One sourcing note that can look like an exception: Cyprus, Estonia, Latvia, Luxembourg, and Malta are each a single region. Where a regional value is missing for them, the national series is used instead: it describes the identical territory, not a model of it, and the atlas labels the value with its sourcing basis and a lowered confidence flag. The internet-access context lens — which never enters the composite — may likewise carry a labeled value from a region's parent statistical area where the survey stops above the regional level (see gaps).

Instead, missing data is handled by withholding. A region's composite is published only when the region has data in every scored dimension and for at least 60% of the scored panel overall. The 60% line is an editorial convention — no statistical authority blesses a particular threshold — so it is declared here and held constant across method versions.

On the map, a region without data is a neutral hatched fill with its own legend entry — never a color that could be misread as a low score. In lists and charts, missing regions appear as explicitly unavailable; they are never silently dropped. Where a composite is withheld, the atlas says so and shows the coverage that fell short.

One residual gap to name: within a dimension, the score averages only the indicators that are present, so a dimension missing an indicator is itself a partial measure. The every-dimension requirement and the visible coverage count are the guardrails.

5Known gaps#

These are the panel's known weaknesses.

• Housing is omitted. There is no usable region-level housing series — Eurostat's overcrowding and cost-overburden tables are published at country level only. The atlas leaves housing out rather than approximate it with an income-based proxy, which would double-count income; the dimension will be added when a real regional series exists.
• Internet access has a survey ceiling. Regional internet statistics come from a survey that several countries — Germany and Greece among them — report only at national or macro-regional level. For those regions the atlas shows the parent area's value, labeled as such. That approximation is one reason internet access is a context lens rather than a scored dimension.
• Inclusion is thin. One indicator — people at risk of poverty or social exclusion — carries the dimension. It is the broadest umbrella measure available at the regional level; the atlas flags the dimension as thin instead of padding it with overlapping series.
• Vintages are mixed. Each value uses its region's latest populated year, so reference years differ across indicators — and can differ between regions on the same indicator. Reference years currently run from 2014 to 2025. The year is shown beside every value, so a mismatched comparison is always visible.

6Sources and licenses#

All statistics come from Eurostat's regional database, published under Creative Commons Attribution 4.0 (CC BY 4.0). Every indicator names its exact Eurostat table code (listed above); the exact filters are recorded in the published data files, so any number can be re-fetched from the source and checked.

The atlas's own derived data — scores, bands, coverage flags, the manifest — is published under the same license, CC BY 4.0: reuse it, with attribution.

Part of the indicator selection is aligned with an open JRC regional dashboard (publication JRC141991, CC BY 4.0): we took vetted Eurostat series from its annex, not its scoring or its structure.

One caveat: the map boundaries come from Eurostat's GISCO service and are © EuroGeographics. They are free to reuse for non-commercial purposes; commercial reuse of the boundary files requires a EuroGeographics license. This constrains the map geometry only — the statistics, the scores, and everything else on this page are CC BY 4.0.

The method is versioned. Changing the goalposts, the indicator panel, or the aggregation bumps the version; scores are then recomputed for the whole back-series under the new version, and scores from different versions are never mixed in one time series. A published version's scores never change under that version. Current: method v1.2.0, data snapshot 2026-06-23.

Pulse Atlas replaced an earlier municipal atlas of Belgium built by the same lab; that atlas is retired, and its municipal-level scores — computed by a different method — are no longer published.

7How to cite#

Cite the atlas with its method version and snapshot date; together they pin exactly what you saw:

Grove Lab. EU Pulse Atlas, method v1.2.0, data snapshot 2026-06-23. https://grove-lab.org/en/atlas (accessed 2026-09-10).

For a specific claim, link the view itself: explore URLs (/atlas/explore?…) carry the selected region and indicator, so the link you share opens on the numbers you cited.

The version pin keeps a citation reproducible: the same method version and snapshot always give the same scores. If the method changes, its version changes with it, so the scores you cited stay fixed under the version you cited.

8What this atlas cannot tell you#

The atlas cannot tell you everything about a region. These are its limits:

• It cannot tell you what matters most. The equal weighting across dimensions is a choice we made; a different, equally defensible weighting would rank regions differently.
• It cannot tell you why. The atlas describes measured outcomes; it does not explain them. A low score says nothing about its cause — whether history, geography, or policy.
• It compares slightly different years. Reference years differ across indicators, and can differ between regions on the same indicator. The year sits beside every value; check it before building an argument on a two-region comparison.
• It cannot see inside a region. A NUTS-2 region can span millions of people, a metropolis, and its rural hinterland. A regional average hides the variation inside it: a prosperous region can still contain struggling towns.
• Survey indicators carry noise. Several indicators come from sample surveys with sampling error. A small difference between two regions can be noise rather than signal — treat near-ties as ties.
• It only covers what Eurostat measures at the regional level. Housing is absent for lack of regional data; safety, the environment, and much of daily life are not in the panel — either no comparable regional series exists, or the thing is hard to measure.