---
id: ch5-guide-attractiveness
type: spine
title: "How to read the innovation potential grid on the Czech-Bavarian border"
readingTime: 2
standalone: true
core: false
teaser: "Start from a question, pick a scale and a segment, then read the 1 km cells instead of the district average: a worked route through the attractiveness tool."
parent: null
diagram: fig-guide-attractiveness
recallQ: "How should a regional user work with the Czech-Bavarian innovation potential tool, and what should they not expect from it yet?"
recallA: "Start from a concrete question, choose the scale and segment (for example agrotourism), read the Attractiveness Index cell by cell on the 1×1 km grid, then use the municipal summaries and sector reports in plans and impact assessments. Segment analyses are still being populated and cross-border indicators are not fully harmonised, and the tool shows present potential, not futures."
highlights:
  - "Led by Plan4all and the Deggendorf Institute of Technology for south-western Bohemia and eastern Bavaria"
  - "Inputs: statistical indicators, universities and research institutes, transport networks, socio-economic data"
  - "Outputs: Attractiveness Index, thematic maps, municipal summaries, agri-food sector reports"
  - "Beta: segment analyses still being populated"
status: accepted
concept: rural-attractiveness
lang: en
lens: czech-bavaria
depth: standard
formalism: none
visuality: balanced
lengthBand: standard
genre: worked-example
state: edited
carriers: prose|diagram
---

**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](#c/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](#c/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).

**Sources:**
- D4.2 PoliRuralPlus Platform Beta, §3.3.1 Jackdaw tool ecosystem; §3.10 Rural Attractiveness / area-potential analysis; §6.2 Table 7. [PDF](https://www.poliruralplus.eu/docs/421/D4.2_PoliRuralPlus_Platform_Beta.pdf)
- D4.1 PoliRuralPlus platform design, §6.10 Rural Attractiveness Analysis Application. [PDF](https://www.poliruralplus.eu/docs/162/D4.1_PoliRuralPlus_platform_design_v1.pdf)