Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.
Reports Public Space
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
- Nature and Environment
- Public Order and Safety
- Space and Infrastructure
Begin date
Contact information
Link to publication website
Responsible use
Goal and impact
If something needs to be fixed or cleaned up on the street or in a park, it can be reported to the municipality via Signalen, the online reporting system. A dangerous traffic situation or nuisance from people and catering establishments can also be reported. Previously, people had to choose which category their report fitted best (e.g. 'nuisance' or 'street furniture'), so that the report went to the right department in the municipality. But the municipality is a complex organisation and the list of categories is long. As a result, people did not always choose the right category. This sometimes caused delays in processing notifications. Therefore, we now use an algorithm that recognises words, e.g. 'rubbish' and 'pavement'. On this basis, it determines which category the report best fits and which department should handle it. So the caller no longer has to choose a category, and the call is handled faster because it goes to the right department.
Considerations
Signals was chosen based on the common ground principle - no longer depending on supplier.
Human intervention
All reports categorised with less than 40% certainty are forwarded to the Customer Contact Centre (CCC). A human assessment then takes place and the call is still categorised. Reports that are wrongly forwarded to an incorrect category are also manually placed in the correct category by the responsible department (sometimes via the KCC).
Risk management
At its core, the system places a notification in the right category so that it comes to the attention of the right department faster.
If the algorithm cannot place a notification in a category with sufficient certainty, it is labelled as 'other'. A KCC employee checks these notifications and manually puts them in the correction category. The risk in this application is that it takes slightly longer until the report reaches the right department.
The reporter can add personal data if he or she wants to be kept informed. This data is stored securely and not used by the algorithm.
Link to Processing Index
Operations
Data
Notifications
The dataset consists of notifications made previously (free text field). Initially, we used notifications from past years to train the algorithm. It is regularly retrained with new notifications and implemented corrections to existing notifications. If the KCC or the service department see an incorrect categorisation, (see Human supervision) they correct it manually in the notification system. These corrections are used in retraining. We are investigating whether re-training the algorithm can be automated in the future.
We cannot disclose this dataset in this register. Because the data comes from a free text field, it may contain personal data, although this is explicitly not requested.
Email address and telephone number for follow-up questions
Someone who makes a report can leave his or her telephone number and/or email address if he or she wishes. We will then keep the reporter informed of progress and have the option of calling him or her back. This information is not kept longer than necessary for this purpose and is therefore not used by the algorithm.
Technical design
The text of the notification is broken down into individual words. Each word from a notification is analysed to see how unique it is to that notification, set against the total collection of words ('TF-IDF' or 'term frequency-inverse document frequency'). As a result, a word like 'the' or 'thank you' gets a low weight and a word like 'rubbish' gets a higher weight.
From that combination of words, logistic regression (a machine-learning technique) is then used to determine which category the report belongs to and thus which department within the municipality the report is most likely to fit.