From traffic jams and unsafe road conditions to potential threats to our personal safety such as vandalism and poor street lighting, there are many risks that can affect our well-being and quality of life. The main problem is that we are not always aware of these risks and are therefore unable to respond appropriately. These information gaps can lead to unsafe decisions and dangerous situations.
This is exactly where the CityGuard project comes in. Digitalization enables us to exchange relevant information in real time via the internet in order to support each other. A freely available platform can help raise awareness of possible dangers and improve people’s safety. The use of geodata and the collective ‘crowd intelligence’ can create a more comprehensive picture of the present level of safety. This enables users of the platform to make informed decisions and protect themselves from potential dangers. An application that implements such a platform addresses the socially relevant issue of safety and promotes cohesion and solidarity within the (local) community.
The CityGuard project is based on a data challenge launched by the inovex GmbH in cooperation with the Digital and Data Literacy in Teaching Lab as the project sponsor, aiming to develop the concept and basic functionalities of a crowd remote sensing app.
Our goal is to explore the extent to which a data-driven platform can contribute to improving safety in the city and the quality of life. In particular, we want to find solutions to the following questions:
- What technologies and infrastructure are needed for such an application and for collecting and evaluating the data?
- How can the safety of areas in the city be measured using simple questions in order to obtain meaningful data that is not significantly distorted by false reports and spam, while ensuring the anonymity of users and other aspects of data protection?
The project aims to develop a prototype app for classifying and visualizing dangerous areas in the city center, providing users with a platform for exchanging data on risk factors. The app will display a city map showing both the user’s location and areas relevant to selected risk factors. Furthermore, an algorithm could be implemented within the project or beyond to calculate routes that make it easier to avoid these areas.
In addition, a concept is to be developed and implemented that enables a responsible mechanism for the automatic collection and evaluation of data using remote crowd sensing. To collect the necessary data, users can submit hazard reports and give the all-clear for certain areas. The app should support various categories of risk factors such as vandalism, accidents, pollution/littering, road damage/sidewalk damage, pest infestation, storm damage, alcohol abuse, and inadequate street lighting. By combining these features, the project offers an innovative solution for improving safety in the city center through the active participation of users.