Mapping Unemployment: Spatial Patterns and Clusters in Jakobstad with Finland's Fine-Grained Statistical Data

Finland’s unemployment landscape tells a story far more complex than simple national averages suggest. By analyzing Statistics Finland’s remarkably detailed 250-meter grid data, we can uncover spatial patterns of unemployment that reveal how economic opportunities and challenges cluster across the Finnish landscape.

As you’ll see, this is just a glimpse into this particularly interesting dataset. I’ll be using a sample dataset from the city of Jakobstad (Fin. Pietarsaari), using a dataset from 2017. While this analysis is localized, the methods I’ll demonstrate are easily scalable. We can apply them on a national level using the annually updated 250-meter grid dataset, which is separately available (purchasable) from Statistics Finland.

When it comes to Jakobstad, with a population of around 19,000, Jakobstad is a perfect case study. Its economic identity has long been shaped by its coastal location, transitioning from a historical center for shipbuilding and international trade to a diverse industrial hub. While traditionally known for its manufacturing, including a major paper mill and the world-renowned Nautor Swan yacht builders, the city’s economy is now also driven by a strong service sector.

The Power of Granular Data

Most unemployment analyses rely on municipal or regional statistics, but Statistics Finland’s RTTK (Ruututietokanta) grid database offers something unique: employment data at 250-meter resolution. This means we can examine unemployment patterns at the neighborhood level, revealing spatial dynamics invisible in broader statistical aggregates.

Our analysis covers 394 grid cells with 160 containing valid unemployment data. This granular approach allows us to detect local hotspots and coldspots that traditional analyses might miss.

Figure 1: Overview of unemployment patterns across Finnish 250m grid cells, showing spatial distribution, LISA clusters, and key statistics

Key Findings: Urban Areas Show Higher Unemployment

Perhaps the most striking finding challenges conventional assumptions about urban economic advantages. This analysis reveals that urban areas (defined as grid cells with ≥ 1,500 inhabitants/km²) have 57% higher unemployment rates than rural areas:

• Urban areas: 12.1% average unemployment (median 10.7%)
• Rural areas: 7.7% average unemployment (median 6.7%)

It’s important to note a key limitation: to protect individual privacy, Statistics Finland suppresses data for any grid square with a population too small to prevent identifying a person. This means the unemployment numbers for sparsely populated, rural areas are likely understated, which may lead to an overestimation of the figures.

Nevertheless, this pattern suggests that while cities concentrate economic opportunities, they also concentrate job seekers, potentially creating more competitive labor markets with higher visible unemployment rates.

Figure 2: Distribution of unemployment rates showing the contrast between urban and rural areas

Spatial Clustering: Unemployment is Not Random

Statistical analysis reveals that unemployment patterns are far from random across Jakobstad. Using Moran’s I spatial autocorrelation analysis, we found significant positive spatial clustering:

• Moran’s I: 0.248 (p < 0.001)
• Interpretation: Areas with similar unemployment rates cluster together

This clustering indicates that neighboring areas share similar employment conditions, suggesting spillover effects where economic opportunities and challenges spread across boundaries, as the maps below illustrate.

Interactive Exploration

The spatial patterns become even clearer through interactive exploration. Use the maps below to explore the data in detail:

Hotspots and Coldspots: The Geography of Employment

Local Indicators of Spatial Association (LISA) analysis identified distinct spatial clusters:

Unemployment Hotspots (19 cells):

• Average unemployment: 20.1%
• Character: High unemployment surrounded by other high-unemployment areas
• Implication: These represent persistent pockets of employment challenges

Employment Coldspots (12 cells):

• Average unemployment: 1.7%
• Character: Low unemployment surrounded by other low-unemployment areas
• Implication: These are employment success stories with regional stability

Spatial Outliers (9 cells):

• Mixed patterns of high unemployment surrounded by low (5 cells) and vice versa (4 cells)
• These transitional areas warrant closer investigation

Methodology: Rigorous Spatial Analysis

This analysis employs established spatial analysis techniques:

1. Data Processing: Cleaned Statistics Finland’s RTTK 250m grid data, calculating unemployment rates as unemployed/(unemployed + employed)

2. Urban-Rural Classification: Applied 1,500 inhabitants/km² threshold consistent with Finnish urban planning standards

3. Spatial Weights: Constructed 8-nearest neighbor weights to account for geographic relationships

4. Global Analysis: Computed Moran’s I to test for overall spatial clustering

5. Local Analysis: Applied LISA to identify specific hotspots and coldspots

Policy Implications: From Analysis to Action

These spatial patterns suggest several policy directions:

Targeted Interventions:

• Focus on 19 hotspot areas: These clusters of high unemployment represent priority areas for intervention
• Urban employment strategies: Address why dense urban areas show higher unemployment despite economic concentrations
• Regional approaches: Leverage spatial spillovers by developing strategic employment hubs

Infrastructure and Connectivity:

• Transportation networks: Ensure high-unemployment areas aren’t isolated from opportunities
• Regional integration: Consider labor market dynamics across administrative boundaries
• Early warning systems: Monitor spatial outliers as potential transitional areas

Data-Driven Monitoring:

• Regular spatial analysis: Track how unemployment clusters evolve over time
• Multi-scale approaches: Combine grid-level detail with regional strategy
• Evidence-based targeting: Use hotspot identification to guide resource allocation

Limitations and Future Directions

This analysis has several important limitations:

• Temporal snapshot: The biggest one, this sample dataset represents 2017 conditions; patterns have most certainly evolved substantially since then.
• Missing data: 22.8% of cells lack employment data, likely rural areas with very low population
• Privacy constraints: Small area statistics subject to data suppression for privacy protection
• Causation vs correlation: Spatial patterns suggest relationships but don’t establish causation

Future research could examine:

• Temporal changes: How do spatial unemployment patterns evolve over time?
• Correlation between unemployment and socioeconomic indicators: Analyze potential correlation and generate a correlation matrix
• Causal mechanisms: What drives the observed spatial clustering?
• Policy effectiveness: Do spatially-targeted interventions reduce clustering?

Conclusion: Space Matters for Employment Policy

This analysis demonstrates that unemployment in Finland exhibits clear spatial patterns rather than random distribution. The identification of significant clustering, combined with the counterintuitive finding of higher urban unemployment, suggests that employment policy must account for spatial dynamics.

By mapping unemployment at unprecedented resolution, we can move beyond one-size-fits-all approaches toward spatially-informed strategies that recognize how economic opportunities and challenges cluster across the landscape. The 19 identified hotspots provide a concrete starting point for targeted intervention, while the broader pattern of spatial clustering suggests that regional approaches may be more effective than purely local ones.

As Finland continues to navigate economic challenges and opportunities, fine-grained spatial analysis offers a powerful tool for understanding where help is most needed and how economic dynamics unfold across space.

Data Source and Acknowledgments

This analysis uses Statistics Finland’s RTTK (Ruututietokanta) 250-meter statistical grid database. This sample dataset was downloaded from an online data portal managed by Esri Finland Oy.

For questions about methodology, please don’t hesitate to contact me.