Not long ago, some of us needed to move to another country, and we spent a lot of time choosing where to go. There wasn’t one perfect answer — no one place was perfect for everyone. Each of us had different criteria for choosing a country and city to live in.

Since Kontur is a GIS company, we had our own layers of data that we could turn into indexes and combine, providing the analysis we needed. All our layers are in the same hexagonal shape, so doing the math with them was easy.

We thought this idea was worth exploring. We decided to create a tool because if we succeeded, it would have many other uses. Finding the best place on the map is a common task.

Gathering Criteria

We did a big survey within the company to find out what criteria are important when choosing a country and city to move to. Even if criteria overlapped, their importance varied for each person.

The main criteria were:

• Cost of living (we could use Gross Domestic Product for this)
• Ease of legalization
• Safety
• Healthcare quality
• Climate
• Environmental quality

There were also less obvious criteria, like the presence of an IT community, frequency of media events, cycling infrastructure, proximity to mountains, landmarks, and more.

We ended up with a long list of layers needed for this analysis. We asked our GIS experts to find reliable sources for this data. Many of these layers are now in the Kontur dataset catalog.

Setting Requirements

The research helped us understand what this tool should be like:

• The ability to select and mathematically “combine” (find the weighted average) layers.
• We can’t “combine” dollars and years of legalization — values need to be automatically converted into indexes, like 0 for minimum and 1 for maximum.
• For each layer, we need to define what 0 and 1 mean. For example, in years needed for legalization, 0 (minimum) is good and 1 (maximum) is bad, but for GDP, it’s the opposite.
• For some, waiting 10 years for a passport is a lot, while for others, even 3 years is too much. We need to redefine the minimum and maximum for each layer.
• Some people didn’t want to consider countries where safety was lower than their current location. There should be an option to exclude places that don’t meet a certain safety level, no matter how good the other criteria are.

Proof-of-Concept

Our Product Development Director recognized this as classic multi-criteria GIS analysis, which helped us during the analysis phase. We prepared the logic for transforming layers into indexes and calculating the final score by combining layers. We decided to build the first prototype on the frontend without saving data in the database. This looked like data input where a JSON string with instructions on how to calculate the color for a hexagon was inserted, performing calculations on the fly.

And it worked! At the Proof-of-Concept stage, we realized that all the pieces fit together, and everyone could find a place to relocate.

From Concept to Real Product

We started talking to clients, gathering other possible uses for this product. We found it could be used in many fields and industries, like urban development, climate change visualization, business planning, and more. We decided to turn it into a full-fledged product.

Since then, we’ve added many layers, search functionality, convenient buttons, distribution charts, enriched it with AI analytics of the selected area, and named it Atlas.

Now our colleagues and anyone interested can access all data layers and conveniently choose a place to relocate. Here’s an example of the analysis from one of our colleague according to his preferences:

By the way, he successfully relocated. He found a place with good safety, low taxes, easy legalization, doesn’t worry about global warming, and everything is going well for him.