In Germany, the number of electric vehicle charging points has increased from about 1,500 to more than 44,000 in 8 years. Despite this significant growth, there are still large gaps and low coverage in some areas. This has adversely affected the usability and adoption of electric vehicles. To address these issues, initiatives and funding have recently been launched. Companies operating in this particular sector have been actively working to expand the number of charging points throughout Germany and the European Union. The question now is what does the overall coverage look like, which regions lack electric charging stations and which regions should be prioritized? To help answer these questions, our Data Scientist has created a Proof of Concept called the Electric Vehicles Charging Station Hotspots app. Let’s take a closer look!

The app in a nutshell

The app was created with the idea of providing an easy and intuitive overview of the availability of electric charging stations in different areas of Germany. Here is a brief overview of how the app currently functions:

  • The app simplifies the display of charging stations by dividing the country into regions based on zip codes, resulting in just over 8,000 regions.

  • The app has a filter function that can be adjusted by the user. For example, all regions with less than 5 known chargers can be hidden.
  • The app can also display the ratio of the number of chargers to the size of a population or the area coverage of the region. Especially relative to area, most larger cities show spikes, as one would expect. This makes it more convenient for users to find locations that meet certain requirements.

  • To make it easier for users to search for possible locations of new charging stations, real-estate functionality has been built into the application. For example, it is possible to select the desired area by zip code and a certain range, then automatically open a web page on a real estate website for the desired search result.

Behind the scenes

The idea was to keep the app simple, streamlined and easily hostable on the web, so the app was built using Streamlit and Python. Streamlit is, as they proclaim on their own website “an open-source app framework for Machine Learning and Data Science teams to creature beautiful web apps in minutes”, which provided the UI elements needed for the app.

All data related to the charging stations, their properties and where they are located, came from the Open Chargemap Project. The API of Open Chargemap was used to retrieve data from relevant electric vehicle charging stations in Germany, including their location information. Subsequently, the retrieved data was cleaned and filtered for further use. After data cleaning, a total of more than 8,000 German zip codes were used to determine which station is within which area. Charging stations outside a particular zip code will only count if they do not exceed a certain user-defined threshold.


Within just two weeks, our Data Scientist had created a working version of the app. However, it certainly wasn’t without its challenges. One of the biggest hurdles during this project was obtaining data. The main dataset used was a cleaned up version of data retrieved from Open Chargemap. Other sources for this data were much more limited in terms of availability and what you could do with that data.

Future outlook

The app is still in a development phase, so further optimizations are needed. More features and functionalities will be added in the near future to make the app more robust. Do you have questions about the app or need help with a similar project? Don’t hesitate to contact us, our Data Science team is happy to help!

This blog is written in collaboration with Florian Fuss – Data Scientist at Conclusion Mediaan