Public space reports

Municipality of Leiden

If something needs to be fixed or cleaned up in public spaces, it can be reported to the municipality through Signals. This is a municipality notification system.

Last change on 16th of April 2025, at 6:33 (CET) | Publication Standard 1.0

• Publication category: Other algorithms
• Impact assessment: Field not filled in.
• Status: In use

General information

Theme

Public Order and Safety

Begin date

2024-12

Contact information

[e-mail]

Responsible use

Goal and impact

If something needs to be fixed or cleaned up in public spaces, it can be reported to the municipality through Signals. This is a municipal reporting system. A dangerous traffic situation, housing nuisance or nuisance from people and the hospitality industry 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 needs to choose a category, and the call is handled faster because it goes to the right department.

Considerations

Field not filled in.

Human intervention

If the algorithm cannot find an appropriate category based on the text written by the notifier, the notification is put on the "other" category by the algorithm. A human assessment then takes place and the notification is still assigned to an appropriate category.

Risk management

There are few risks in this algorithm. It places a notification in the right category and gets it to the attention of the right department faster. If the algorithm cannot place a notification in a category with sufficient certainty, it ends up in the 'Other' category. Leiden municipality employees review those notifications and manually put them in the right category. So if the algorithm does not work properly, it takes a little longer for the report to reach the right department. The reporter can add contact details (telephone number and/or e-mail address) if he or she wants to be kept informed. These details are stored securely and are not used by the algorithm.

Legal basis

Depending on the type of report, but often it is a municipal task that is carried out. This is because the law states that the municipality has certain responsibilities in public spaces.

Link to Processing Index

Verwerkingsregister Gemeente Leiden

Operations

Data

Notifications

The dataset consists of notifications made previously (free text field). Initially, we used 150,000 notifications from past years to train the algorithm. It is regularly retrained with new notifications and implemented corrections to existing notifications. If colleagues from affiliated departments see an incorrect categorisation, (see Human supervision) they correct it manually in the notification system. These corrections are used in re-training. We are investigating whether retraining 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 it is not explicitly requested.

E-mail address and phone number for follow-up questions

Someone making a report can leave their phone number and/or e-mail address if they want. We then keep the reporter informed of the progress and we have the option to call 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

Architecture of the model

The text of the notification is divided into individual words. Each word from a notification is analysed to see how unique it is to that notification, compared to 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.