Welcome to our newsletter on everything related to impact data. In this newsletter we will cover:
- Interesting variable: Green cooling services in major European urban areas
- Interesting tool: Fire Information for Resource Management System FIRMS
- Interesting read: “How to price a data asset”
- Interesting dataset: Desktop images
Interesting variable: Green colling services in major European urban areas
In a recent paper, researchers around Alby Duarte Rocha from TU Berlin have written on “Unprivileged groups are less served by green cooling services in major European urban areas”. They were mapping income levels with green cooling services.
Green cooling services create urban cool islands and are generated through tree shades or evaporation. These green cooling services can lower air temperatures for the local communities. In other words, a lack of green cooling services leads to higher temperatures.
In the paper, they have shown maps for several European cities including Vienna, Berlin, Paris and London. These maps contain great information. For example, you can identify urban cool and heat islands. This is great information to identify areas for interventions.

Interesting tool: Fire Information for Resource Management System
In recent years, we have also seen the emergence of something called “Open-Source Intelligence”. Individuals have started to use satellite data, social media images or public registries. It is well documented in the book “We Are Bellingcat: An Intelligence Agency for the People”.
For example, if you are following the war in Ukraine you might have heard of NASA’s FIRMS which stands for Fire Information for Resource Management System. That is a very impressive tool to check developments in a war with little independent information provided. Even without knowing anything about the war you might guess where the fighting takes place.

Even without knowing anything about the war you might guess where the fighting takes place. Compare it to the map in January 2025 and you see large differences.

Interesting read: How to price a data asset
Nobody really knows how much to charge for data and what are you selling? In recent years, AI companies have been buying datasets and have changed some of the underlying assumptions. The other big purchaser of raw data have been quantitative hedge funds.
In the blog post “How to price a data asset”, the author outlines a few key principles which we often tend to forget:
- Data is additive: In general, you only need one software solution for any use cases but more data helps to take better decisions. Compare the recommendation algorithms of Amazon with a small retailer.
- Data value depends on the use case: It is somehow obvious but different organizations have a different willingness to pay for data. That means that there are no easy solutions which offers the conditions for OpenAI and a small start-up based somewhere in Vietnam.
- Data is rivalrous: A hedge fund might be willing to pay more for data showing the occupancy of parking lots when no other hedge funds can access the same dataset. This means that the value of the dataset decreases when sold to more entities.
There are also some nice examples. For example, foot traffic, e-mail receipts and credit card transactions in a mail all show the same pattern: What are people buying. However, you can add them together to have a more complete picture but in principle, they are functionally substitutes.
Some companies have found a solution by selling the data as a software solution such as ChatGPT, Bloomberg or consumer credit bureaus.
Interesting dataset: Desktop images
How doesn’t like nice desktop wallpapers. The Copernicus Data Space Gallery is uploading one satellite image every month. Check out the January edition.
