In Orlová, every eighth person faces foreclosure.
In Frýdlant nad Ostravicí, 37 kilometers away, four times fewer.
Why?
We looked for the answer in data from the Index of Stronger Regions, a joint project of Česká spořitelna and Evropa v datech. 49 quality-of-life indicators for each of 206 cities in the Czech Republic. The story that emerged is surprising.
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Index of Stronger Regions
A joint project of Česká spořitelna and Evropa v datech that measures quality of life in every Czech region. Not with a single number, but with 49 indicators from foreclosures to internet speed. Because without stronger regions, there can be no prosperous Czechia.
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49 indicators
Every region in Czechia, 206 municipalities with extended powers (ORP), has a profile built from 49 indicators. From life expectancy to internet speed. From foreclosures to the number of libraries.
We wanted to read a story from the data. So we started looking for structure.
Seven project pillars
The project authors, Česká spořitelna and Evropa v datech, divided the 49 indicators into seven thematic pillars, from demographics through economics to leisure. Each indicator is scored 0–100.
But pillars are categories, not explanations. Foreclosures are in "Economics," yet they correlate more strongly with health than with income. We needed to find out what truly differentiates regions from one another.
Who is at the top?
If you asked people on the street, most would guess Prague. Or Brno. But at the top of the overall ranking stands Turnov, a town in the Bohemian Paradise with thirty thousand inhabitants.
Prague is in 9th place. It excels in infrastructure and economics, but lags behind smaller towns in healthcare and environmental quality.
And who is at the bottom?
At the very bottom is Horšovský Týn. But more important than the name at the end of the table is the pattern: weak regions are not just "the Ústí region." You find them in the Plzeň Region, in the Ore Mountains foothills, and in the Jeseníky area.
No ORP is hopeless. And none excels in everything. That is precisely why a single number is not enough.
What hides behind a single number?
4Using factor analysis, we identified four independent dimensions along which regions differ from each other. No two correlate. Each tells a different story.
Transit stops, shops, internet, gas stations, dentists
Foreclosures, crime, life expectancy, education, unemployment
Tourism, landscape, restaurants, museums, sports facilities
Average age, migration, pharmacies, funding per pupil
Four views that explain the differences
Of the 49 indicators, 42 systematically differentiate regions and compose into four views.
Accessibility and infrastructure: transit stops, shops, internet, dentists. Social resilience: foreclosures, crime, life expectancy, education. Place attractiveness: tourism, landscape, restaurants, museums. Demographics: aging, migration, pharmacies.
The remaining 7 indicators do not systematically differentiate regions. For example, pediatricians and social services: correlation with prosperity only 0.03–0.17. They are missing in both rich and poor regions. That is not a prosperity problem; it is a systemic problem.
Four independent axes
We plotted accessibility and infrastructure (transit stops, shops per km², internet) against place attractiveness: tourism, landscape, museums, ecological stability.
Correlation zero. Both dimensions are completely independent of each other. A town can have excellent infrastructure without tourists, or the reverse. The Krkonoše and Šumava mountains attract visitors but do not have gas stations on every corner. Two different models of success.
Social resilience is the key
And now the most important chart of the entire analysis. Overall prosperity on the X-axis, social resilience (foreclosures, crime, life expectancy, education) on the Y-axis.
Correlation 0.65, the strongest of all four views. Infrastructure has 0.53, attractiveness 0.45, demographics 0.30. Social resilience is what most differentiates a prosperous region from a lagging one. We will return to it, because it turns out it also influences how regions vote.
What the data also reveal
Some relationships between indicators confirm intuition. Others defy it. We examined all 1,176 pairwise correlations among the 49 indicators and selected four that are most surprising.
Education or affordable housing?
The strongest negative correlation in the entire dataset: r = −0.64. Regions with a high share of university graduates are systematically more expensive for housing.
Both indicators belong to the same factor (social resilience) but pull against each other. Educated regions attract people, push prices up, and displace those who cannot afford them. A dilemma with no easy solution.
Two worlds of Czech infrastructure
Libraries and gas stations belong to one factor (infrastructure). But the correlation is r = −0.60.
Libraries are a sign of a compact town with a center, walking distances, and civic amenities. Gas stations indicate dispersed settlement dependent on cars. Both patterns can score high in infrastructure, just in entirely different ways.
More money per pupil ≠ more graduates
Correlation r = −0.49. Regions that invest the most in elementary schools have the fewest university graduates.
A paradox? Not quite. Small schools in depopulating regions cost more per pupil (fixed costs, few children). University graduates concentrate in cities with large, efficient schools. Funding per elementary school pupil does not measure education quality — it measures the cost of remoteness.
Where people live longer, more people start businesses
Life expectancy (F2) correlates with the number of entrepreneurs (F3) at r = +0.45. Yet they belong to different factors.
Both indicators measure something like the vitality of a place. Regions with active entrepreneurship also have a healthier population. It is not direct causation, but a shared root: a functioning community where people want to live and work.
Three types of regions
When we let an algorithm divide 206 regions into groups by similarity, the result is not chaos. Three clear types emerge. Their distribution across the map tells a story about Czech geography that you know from personal experience — you just have not seen it in numbers before.
Prosperity map
● Prosperous regions: from Prague to small towns like Zlín or Jilemnice. What unites them is above-average social resilience and infrastructure. Not necessarily large cities. Rather those where schools, doctors, and families hold together.
● Cities under social strain: Most, Teplice, Karviná. They have roads, hospitals, and trains. But foreclosures, crime, and low life expectancy keep them in a different league.
● Inner periphery: far from centers, weak infrastructure, but surprisingly good place attractiveness. Quiet countryside, tourism, and an aging population.
Every fifth Czech lives in a region under social strain. And every fifth in the inner periphery.
Ústí Region: zero green dots
The only region where not a single ORP falls in the prosperous category. 10 of 16 ORP are under social strain, the remaining 6 are inner periphery. The entire region is red and blue. Not even Ústí nad Labem, a regional capital with 120,000 inhabitants, made it into the green category.
Vysočina: the mirror image
The opposite extreme: no city under social strain. 10 of 15 ORP are prosperous. Low foreclosures, stable families, functioning schools. But as we will see shortly, this is a region that is surprisingly losing momentum. Statically healthy, dynamically eroding.
The proximity paradox
62% of neighboring ORP belong to different types. Towns between which you drive fifteen minutes differ in life expectancy, foreclosures, or access to a doctor as dramatically as if they were in different countries. Four examples.
Olomouc × Vítkov
33 km apart. Same region, different world. Olomouc is fourth in the overall ranking, Vítkov at 186th out of 206.
Olomouc is a university city with full infrastructure (I = 84) and strong social resilience (S = 84). Vítkov has infrastructure at 5 out of 100. Social resilience at 20. Young people leave, services disappear. Yet the regional authority is within sight.
Hradec Králové × Nový Bydžov
19 km. The closest extreme in Czechia. A regional capital at 31st place and a region at 167th place are separated by just 19 kilometers.
Nový Bydžov has neither infrastructure, nor tourism, nor demographic dynamism. Yet it lies in the middle of the Hradec Králové Region. Prosperity does not spread like a wave. It stops at the ORP boundary.
České Budějovice × Český Krumlov
A UNESCO jewel with social problems. Krumlov has the highest attractiveness in all of Czechia (100 out of 100). But social resilience of only 40.
Tourism brings revenue but not stable employment or affordable housing. Seasonal work offers no security. A tourism monoculture erodes the social fabric of permanent residents.
Prague × Český Brod
28 km from the center. At 172nd place. Prague is ninth in Czechia: excellent infrastructure, strong social resilience, tourist attractiveness. But beyond the city boundary, a different world begins.
Český Brod has the lowest demographic score in the entire country. Young families leave for better amenities, services disappear, the average age rises. And Prague itself is not much better demographically: 25 points, the weakest in the entire top ten. The metropolis's success does not spill over its boundary.
Who is rising, who is falling?
A static picture is not enough. This chart shows two things at once: where a region stands today (X-axis) and which direction it is moving (Y-axis). Four quadrants emerge, and the most dangerous is the bottom right: regions that are still prosperous but losing momentum.
In the top left, you find hope: weaker regions on the rise. In the top right, leaders keeping pace.
Vysočina is surprisingly declining
Remember the region without a single red dot on the map? Vysočina, statistically the healthiest region in Czechia, is falling. Three of the fifteen fastest-declining ORP in the entire country are from here: Pelhřimov, Havlíčkův Brod, and Nové Město na Moravě. Telč is close behind.
An aging population, young people leaving for work, gradual closure of services. Once the doctor leaves and the school closes, social resilience begins to erode. Vysočina is further along in this process than its statistically good results suggest at first glance.
Bouncing off the bottom, stagnation at the top
Turnov, ranked first overall, has slightly negative momentum. Maintaining the top is harder than reaching it. Conversely, Podbořany, one of the weakest ORP in the country, is growing at the fifth fastest rate. Is this a bounce off the bottom, or just statistical noise?
The answer will come with more years of data. But the pattern is clear: convergence exists, it is just slow.
How prosperity votes
One more look at the same data. We linked 206 regions with 2025 parliamentary election results: from 14,711 polling stations through 6,389 municipalities to the ORP level. What we found confirms the key finding of the entire analysis: a region's social resilience correlates with voter turnout more strongly than income, education, or city size.
0.88Correlation of social resilience with voter turnout: r = 0.88. The strongest relationship in the entire dataset.
Where there is social resilience, people go to vote
Turnout in the 2025 parliamentary elections reached a national average of 68.9%. But the spread is enormous: from 54.7% in Aš to 76.6% in Šlapanice. A difference of 22 percentage points.
What decides? Not city size: infrastructure correlates with turnout at just 0.05. Social resilience decides: foreclosures, crime, life expectancy, education. Correlation r = 0.88, the strongest relationship in the entire dataset.
One party, one dividing line
Correlation of ANO's vote share with overall prosperity: −0.68. With SPOLU the exact opposite: +0.65. The lower the quality of life in a region, the stronger ANO.
In 141 of 206 ORP, ANO has over 35%. It drops below 25% in only four: Prague and its immediate surroundings.
The color of the dots shows social resilience (I2). Red dots — high foreclosures, crime, and low life expectancy — are almost always at the top. Prosperity and social resilience predict election outcomes better than any poll.
The periphery mobilized
Between 2021 and 2025, turnout increased in all 206 ORP without exception. On average by 4 percentage points. But the lower the social resilience, the greater the increase (r = −0.58).
Cities under social strain: +4.8 percentage points. Prosperous regions: +3.2 pp. The periphery mobilized more strongly than the centers.
ANO gained in all 206 ORP, on average by 8.8 percentage points. In Orlová +16.3 pp, in Prague +2.4 pp. SPOLU lost across the board as well: −4.4 pp. In regions where SPD weakened significantly, ANO gained the most.
The periphery is not just one
When we add voting behavior to the original prosperity data, the "inner periphery" splits into two distinct types.
Active countryside: regions like Blovice, Pacov, or Moravské Budějovice. Higher turnout, weaker ANO. Functioning community life, even if economically weaker.
Passive periphery: Vejprty, Vítkov, Králíky. Low turnout, strongest ANO, the greatest increase between 2021 and 2025. Regions where social resignation is reflected in voting behavior.
One type of periphery needs protection of what works. The other needs a restart. Policies that lump them together work for neither.
Find your region
Enter an ORP name and see how it scores across all four views.
Key takeaways
Social resilience is the key — and it determines politics too
Foreclosures, crime, life expectancy, and family stability explain most of the differences in prosperity (r = 0.65) and voter turnout (r = 0.88). Infrastructure alone is not enough. Social resilience also predicts party preferences: the lower it is, the stronger the support for parties promising change: ANO, SPD, Motorists, Enough! (r = −0.73).
Different problems require different solutions
Weakened urban ORP like Ostrava or Most have infrastructure; they need social work, debt counseling, and employment programs. The active countryside needs protection of what works: doctors, schools, transport connections. The passive periphery needs a restart, but a different kind than industrial cities. And tourism monocultures like Český Krumlov need diversification, not more tourism subsidies.
Vysočina is a warning
The statically healthiest region is dynamically eroding the fastest. Once the doctor leaves and the school closes, statistics hold up for a few more years, but the process is already irreversible. Vysočina shows that prevention is cheaper than rescue.
The gap is widening
Between the 2021 and 2025 elections, the periphery mobilized more strongly than the centers, but in favor of ANO. Poorer regions came to vote in greater numbers and voted more decisively for ANO. Wealthier regions strengthened SPOLU. Regional inequalities are translating into political geography ever more strongly.
A single number is not enough
A ranking of 1–206 is an orientation map, not a diagnosis. For decision-making, you need a region's profile across four views, its trajectory over time, and the awareness that swapping two indicators changes the top 10 by a third. Data shows the direction. Answers must be found by people on the ground.
Methodology
Data
The Index of Stronger Regions is based on 49 quality-of-life indicators for 206 municipalities with extended powers (ORP) + Prague. Source data come from publicly available databases of the Czech Statistical Office (CSO), the Ministry of Labour and Social Affairs, the Ministry of Health, the Czech Office for Surveying, Mapping and Cadastre, the Czech Telecommunication Office, and other government sources. Most indicators reflect the state in 2023–2025; the exact year for each indicator is documented in the source dataset.
Election data: Results of the 2025 and 2021 parliamentary elections from CSO / volby.cz (14,711 polling stations, 6,389 municipalities, aggregated to ORP level).
Four-factor model
The original index uses 7 expert-defined pillars. This analysis adds an alternative view: exploratory factor analysis (EFA) on rank transformations of 49 indicators. Extraction method: Principal Axis Factoring (PAF), rotation: Varimax.
Parallel analysis recommends 5 factors; we chose 4 as a compromise between parsimony and reliability (the 5th factor has α = 0.55). Resulting factors:
- Infrastructure and accessibility (29.6% of variance, α = 0.85): gas stations, shops, transport, internet, fiber optics
- Social resilience (31.0%, α = 0.88): foreclosures, crime, life expectancy, education, unemployment
- Place attractiveness (23.5%, α = 0.84): tourism, accommodation, restaurants, sports facilities, landscape
- Demographics and care (15.9%, α = 0.69): average age, migration, doctors, pharmacies
Total explained variance: 90.9% (adj. R² = 0.91). KMO = 0.80, Bartlett's test: p < 0.001. Bootstrap stability of factor loadings verified on 1,000 replications (41/42 assigned indicators stable).
Clustering
K-means clustering (k = 3) on 4 standardized factor scores. Three types of regions: Prosperous regions, Inner periphery, Cities under social strain. Choice of k supported by silhouette analysis.
Correlations and limitations
Reported correlations are Pearson's, verified by Spearman's coefficient and OLS with controls. The analysis is descriptive: correlations at the regional level do not imply causation and cannot be transferred to individual voter behavior (ecological fallacy).
Factor scores are weighted averages of rank positions and reflect the relative standing of ORP, not absolute indicator values.
Author and source
Analysis: David Navrátil · davidnavratil.com
Source project: Index of Stronger Regions (49 indicators, a project by Evropa v datech and Česká spořitelna)
When citing, please reference the source and link to this page.