Our Atlas product already features more than 200 data layers, and we continue to add new ones based on user requests. Our backlog currently includes over 80 layers. In this article, we explain the process of adding a new data layer, using the example of Automated External Defibrillators (AED).

A layer request: an inspiring example from Poland

One of Kontur’s offices is located in Poland, where we closely collaborate with the Polish OSM community—one of the most active and well-developed communities in the world. In Poland, the Public Access Defibrillation (PAD) program is being actively implemented, aimed at installing AED devices in public places and training people in first aid.

The number of AEDs in Polish cities is steadily increasing. Defibrillators are installed in high-risk locations where sudden cardiac arrest (SCA) is more likely to occur: shopping centers, railway stations, airports, public buildings, parks, squares, and stadiums. These locations are marked with special signs, and their availability can be checked using the RATOWNIK app. By enabling geolocation, users can view nearby AED locations and get directions. You can also explore AEDs worldwide on the map here.

The PAD initiative is of immense importance. In Europe, sudden cardiac arrest is one of the leading causes of death, and timely defibrillation within the first few minutes increases survival chances up to 75%. Polish OSM volunteers have done tremendous work mapping AED locations, making Poland’s AED map one of the most detailed in the world. AED data is not just numbers on a map—it’s vital information that can save lives.

How we add data layers

The Atlas product includes a variety of statistical data and analytical tools that help our clients solve their specific challenges. Each dataset is represented as a distinct data layer on the map—for example, the AED (defibrillator) data layer.

Adding a data layer to Atlas is a process that involves several steps. A key role in this process is played by our work with OpenStreetMap (OSM).

1. Data Collection
We extract data from OpenStreetMap, in this case—information about AED locations. Geocint enables automatic data downloading and updates at a specified frequency, ensuring the data remains current and reliable.

2. Data Cleaning and Validation
Data from OpenStreetMap (OSM) can sometimes be incomplete or contain errors. Using Geocint, we validate the quality of the data and make the necessary corrections to ensure the layer is accurate and useful for users.

3. Transformation
The extracted and validated data then undergoes an aggregation process. We aggregate the data using the H3 hexagonal grid.

The unified H3 grid to which we align our datasets makes it easy to combine various indicators with powerful analytics tools like Multi-Criteria Decision Analysis (MCDA). This enables effective and efficient solutions for tasks such as selecting optimal locations and optimizing placements.

4. Analysis and integration
After processing, the data is integrated into the Atlas. We visualize it, add labels, configure display parameters, and conduct testing to ensure that the layer works correctly and the data quality is at the appropriate level. A well-established data integration system allows us to promptly respond to customer requests and adapt the system to their needs.

How we display data on the map

When volunteers add information about the locations of defibrillators, this data is represented as points on the map.

Such maps are useful for people who want to locate defibrillators.
Atlas transforms these points into statistics (numbers) across various dimensions:

• Defibrillators to Aria (n/km^2) 
• Defibrillators to buildings (n/n) 
• Defibrillators to populated area (n/km^2)
• Defibrillators to population (n/ppl)
• Defibrillators to Total road length (n/km)

This data is intended for organizations that plan the placement of defibrillators or want to incorporate defibrillator usage statistics into their operations. Depending on the research being conducted, different layers can be used either individually or in multi-criteria analysis alongside other layers.

Using the defibrillator data layer as an example, you can see how we organize the process of adding new data. Our approach allows us to efficiently integrate data from OpenStreetMap and other sources, ensuring its relevance and usefulness for users.

We add data to make Atlas even better

Atlas currently contains over 200 data layers, and we plan to add at least 80 more. Thanks to Geocint, our open-source data processing tool, we have automated the integration process and can maintain up-to-date data, including real-time information provided via APIs.

If you need specific data layers on the map, we are ready to help! We take user requests into account and adapt to your tasks—whether it’s logistics, infrastructure planning, or risk analysis.

Let’s make data analysis more accurate and convenient. If you have an idea or need to add new layers to Atlas, contact us, and we will discuss how to make it happen!

P.S. We are truly grateful to the Polish OSM community for their contribution and the inspiration that helps us create solutions to make the world a better place.