SKU: 24100969645

Nah&Frisch Supermarket Locations Dataset – Austria

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Description

Nah&Frisch Supermarket Locations Dataset – AustriaQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Nah&Frisch is an Austrian grocery cooperative that focuses on supplying small, independent local merchants, often in rural communities. It plays a vital role in maintaining local supply chains in villages across Austria. There are 207 Nah&Frisch Supermarkets as of 27 May 2026 in Austria. This dataset is

Nah&Frisch is an Austrian grocery cooperative that focuses on supplying small, independent local merchants, often in rural communities. It plays a vital role in maintaining local supply chains in villages across Austria.

There are 207 Nah&Frisch Supermarkets as of 27 May 2026 in Austria. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Nah&Frisch locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows.

Dataset Summary

  • Dataset Coverage: 207 Nah&Frisch supermarkets in Austria
  • Contents: Coordinates, addresses, postal codes, administrative divisions, contact details, and popularity scores
  • File Format: Fully geocoded CSV dataset (UTF-8)
  • Free Sample: Instantly accessible dataset to verify structure and data quality
  • Use Cases: Suitable for GIS, retail analytics, site selection, and AI/RAG workflows
  • Last Updated: 27 May 2026

Dataset Methodology:

This dataset is compiled from publicly available business listings, official company sources, and geospatial validation workflows. Automated quality checks and manual analyst reviews are applied to improve coordinate precision, address standardisation, duplicate detection, and overall analytical consistency.

It is periodically reviewed and updated to reflect known network changes, closures, relocations, and newly identified locations.

Dataset fields included in the CSV:

  • GUID
  • Title
  • Latitude
  • Longitude
  • Street No
  • Street
  • City
  • Admin_level_1
  • Admin_level_2
  • Gemeinde
  • Region
  • Population
  • Postal Code
  • Address
  • Wheelchair
  • Popularity Score
  • Phone
  • Website
  • Opening hours

Data Quality Scorecard

  • Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates)
  • Contact Details (Phone)82%
  • Web Address84%
  • Opening Hours92%
  • Popularity Score100%

Data Preview: Sample geospatial records from the Nah&Frisch dataset in Austria

ID Location Title Latitude Longitude Postal Code Full Address
2b1d6e8... Nah&Frisch Kramerstub`n Goldegg (Goldegg) 47.329060 13.079542 5622 100 Weng, Goldegg, 5622, Sankt Johann...
681cd8d... Nah&Frisch Genewein Kraubath (Kraubath an der Mur) 47.307252 14.935217 8714 8 Kirchplatz, Kraubath an der Mur, 87...
da92b9a... Nah & Frisch (Edlitz) 47.597757 16.140592 2842 20 Markt, Edlitz, 2842, Neunkirchen, ...
3d31e7a... Nah & Frisch (Wallern im Burgenland) 47.727850 16.933742 7151 53 Bahnstraße, Wallern im Burgenland,...
be221b2... Nah&Frisch Moyses Oggau HYBRIDMARKT (Oggau am Neusiedler See) 47.832197 16.666790 7063 5 Hauptstraße, Oggau am Neusiedler Se...

Note: Only a subset of the full dataset fields are displayed here. Download the free sample (option above) to view all fields and verify the data structure.

Why download from Geolocet?

  • Instant download - full dataset available immediately after purchase, no waiting, no manual fulfilment
  • Free sample first - verify structure, fields, and coordinate precision before you commit
  • Analysis-ready CSV - clean, standardised, and compatible with Excel, Python, QGIS, Power BI, and PostgreSQL out of the box
  • Regularly updated - last updated 27 May 2026

✅ Data looks right? Add to cart ↑ - or download the free sample first.

Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Nah&Frisch locations in Austria is found in Niederösterreich (92 sites, equivalent to 5.33 Nah&Frisch supermarkets per 100,000 residents). This is followed by Steiermark (41 sites; 3.23 per 100,000) and Oberösterreich (37 sites; 2.41 per 100,000). From a market-penetration perspective, Burgenland has the highest brand density at 7.0 locations per 100,000 people (population: 300,000), making it the most saturated region for Nah&Frisch in Austria. By contrast, Wien records only 0.1 locations per 100,000 residents (population: 2,025,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Learn more about the brand network in our report: View Report

Also available for Austria

Brand bundle

Top 14 Grocery Brands in Austria - €300

All major chains in one standardised dataset. Best for competitive benchmarking, network analysis, and market sizing across the leading brands.

View Top Brands dataset →

Full market coverage

All Grocery Locations in Austria - complete POI dataset

Includes everything in the brand bundle plus independent operators, smaller chains, and local businesses not covered by the top brands. Best for full market mapping, territory planning, and white-space analysis.

View full POI dataset →

Need the data in another format?

We can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at [email protected].

Who uses this data?

  • Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
  • Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
  • Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
  • Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
  • Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.
  • Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
  • Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
  • Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.

Frequently Asked Questions

Q: How recent is this dataset?

A: This dataset was last updated on 27 May 2026 and is periodically refreshed through automated collection and validation workflows.

Q: What coordinate reference system is used?

A: Coordinates are provided in the global WGS84 geographic coordinate system (EPSG:4326).

Q: Can I use this dataset in GIS software?

A: Yes. The dataset is suitable for GIS platforms including QGIS, ArcGIS, GeoPandas, CARTO, and other spatial analysis environments.

Q: How accurate are the coordinates?

A: Coordinates undergo automated validation and manual quality review processes to improve positional accuracy and analytical reliability.

Analyze this data with AI

Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:

  • "Analyze this Nah&Frisch dataset to identify underserved regions in Austria for potential market expansion."
  • "Identify regions in Austria where Nah&Frisch has a disproportionately strong or weak presence relative to population density."
  • "Identify municipalities in Austria with strong demographic potential but no nearby Nah&Frisch presence."

Disclaimer: All brand logos and trademarks displayed are the property of their respective owners and are used strictly for identification purposes. This product consists of geospatial location data only; no images, logos, or trademark rights are included in the downloadable files.

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WU.
West Palm Beach, US
★★★★★ 4
Good overview of the leading Agentic Framework. Will become outdated quickly.
Format: Paperback
3.5 Stars rounded up. Not a bad place to start if you need to get up to speed fast with Claude Code, understand its vast feature set, how it works under the hood, best practices, and the various agent primitives and how to get the most out of them. Agentic frameworks (Claude Code in particular) are quickly becoming table stakes for anyone working in tech, so it's best to start now. I appreciated the author's ability to flesh out areas where Anthropic's documentation is lacking in depth and nuance, and for some not already working with Claude in their own repos, the fact that he provides "toy" repos where one can experiment with the tools without fear of consequence. Where the book falls short is that most of the stuff in here is already covered pretty well already in Anthropic's docs, or even better so in their free "Skilljar" courses. What's more, some areas are given a bit of a shallow treatment, while others are a bit better done. So it's a bit inconsistent in that sense. Also, I can see how this book will quickly lose its currency in a few months at the pace things are going. Ultimately, for me, the price of this book was a bit rich for my liking given the criticisms above. Still, I feel like I got valuable info that rounded up what I already knew from working with this agentic framework. Recommended.
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Reviewed in the United States on May 28, 2026
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Brahmananda Reddy
Bozeman, US
★★★★★ 5
Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding
Format: Paperback
This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
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Reviewed in the United States on May 20, 2026
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UA
Alexandria, US
★★★★★ 5
A Good Reality Check on How AI Agents Actually Work in Enterprise Systems
Format: Paperback
Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.
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Reviewed in the United States on May 20, 2026
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Christopher West
San Leandro, US
★★★★★ 5
Great book! Practical and for developers that already use AI!
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Reviewed in the United States on April 11, 2026
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Paul Pollock
Bozeman, US
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