Rural attractiveness mapping

Rural attractiveness: from a regional index to a 1 km grid

main tellingexample world: czech-bavariadepth: standard3 min markdown

PoliRural+ measures rural attractiveness on a map, and its tools have gone through three stages.

1. The Rural Attractiveness Explorer (inherited from PoliRural)

The Rural Attractiveness Explorer (RAE) produces a Rural Attractiveness Index (RAI). The index combines indicators such as social capital, economic opportunities and environmental assets, and the tool supports comparisons between regions. It was integrated into the PoliRural Digital Innovation Hub (D4.1 §2.1.1). The existing application offers data visualisation, filtering of input data, map viewer functions and work with user scenarios (D4.1 §6.10).

2. One app, many meanings of “attractive”

D4.1 notes that each pilot understands attractiveness differently, from innovation potential to tourism. The application is therefore adapted through dialogue with each pilot rather than with a single fixed formula (§6.10).

3. Innovation potential on a 1×1 km grid (Beta)

In the Czech-Bavarian border pilot, Plan4all and the Deggendorf Institute of Technology lead the Data-driven Innovation Potential Assessment tool. It maps south-western Bohemia and eastern Bavaria (D4.2 §3.10). Its main design choices:

• A regular 1×1 km grid replaces municipalities or districts, which avoids the bias built into aggregated administrative units.
• Inputs: statistical indicators, the locations of universities and research institutes, transport networks and socio-economic metadata.
• Outputs: a consolidated Attractiveness Index, high-resolution thematic maps, municipal summaries and sector reports, for example on the agri-food economy.
• Scales: from the local grid up to Central Europe, with segment analyses such as agrotourism or identifying high-tech hubs.

The tool is meant to pinpoint locations for food innovation and rural entrepreneurship, to assess digital skills and AI competence, and to support AI-powered help with grant applications. It also gives Czech and Bavarian actors a shared evidence base for coordinating investment.

Status: the tool is implemented as the analytical engine. The segment analyses are still being populated, the cross-border statistical indicators still need to be harmonised, and the AI advisory services for subsidies are to be expanded. Scaling the method to other Central European regions is a stated future direction (D4.2 §3.10). D4.2 also lists rural attractiveness analysis among the reproducible demos to prepare with model builders (§11.1).

The tool shows where potential lies today; it does not simulate futures. PoliRural+ handles scenario thinking through foresight and system dynamics.

How-to: Reading the innovation potential grid.

How to read the innovation potential grid on the Czech-Bavarian border

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When to use it: you have to decide where on the Czech-Bavarian border to put a food-innovation, rural-entrepreneurship or digital-skills effort, and you want evidence finer than a district.

What you need

• Access through the pilot. The pack gives no public address. The Data-driven Innovation Potential Assessment tool is led by Plan4all and the Deggendorf Institute of Technology within the Czech-Bavarian pilot (D4.2 §3.10); ask them.
• A concrete question: which kind of potential, for whom, and at which scale.

Steps

1. Name the question. D4.2 lists three uses: pinpointing locations for food innovation and rural entrepreneurship, evaluating digital skills and AI competence, and supporting AI-powered grant assistance.
2. Pick the scale. The visualisation modules go from the detailed local grid up to the cross-border context of Central Europe.
3. Pick a segment, such as agrotourism or identifying high-tech hubs, where it is available.
4. Read the grid. Each 1×1 km cell carries the Attractiveness Index. Compare cells, not municipal averages: the grid exists to avoid the bias of aggregated administrative units.
5. Check the layers behind a hotspot: statistical indicators, locations of universities and research institutes, transport networks and socio-economic data.
6. Pull the summaries. Use the municipal-level potential summaries and the sector reports, for example on the agri-food economy.
7. Use the evidence in strategic investment plans, territorial impact assessments or regional development roadmaps, and as a shared base when Czech and Bavarian actors coordinate investments.

What you get

A consolidated Attractiveness Index, high-resolution thematic maps, municipal summaries and sector reports (D4.2 §3.10). D4.2 reports that these grids supported comparison and coordination across south-western Bohemia and eastern Bavaria (Table 7).

Outside this region

The older Rural Attractiveness Explorer offers data visualisation, filtering of input data, map viewer functions (zoom, print, layers) and user scenarios, and it is adapted through dialogue with each pilot because each defines attractiveness differently (D4.1 §6.10). For any drawn area, JackDaw also counts Smart POI categories for attractiveness and innovation potential scores (D4.2 §3.3.1).

Watch out

• Segment analyses are still being populated, and data layers are still being refined.
• Cross-border statistical indicators still need harmonising; a cell’s score across the border is less comparable than within one country.
• The AI subsidy advice is to be expanded, not finished.
• The tool shows where potential lies today; it does not simulate futures (D4.2 §3.10).