Public space reports

Municipality of Almere

If something needs to be fixed or cleaned up on the street or in a park, it can be reported to the municipality through Signals, the open source notification system of for and by municipalities.

Last change on 30th of September 2025, at 12:04 (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

01-2023

Contact information

[e-mail]

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 through Signalen, the open source reporting system of for and by municipalities. 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 best fitted with (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

Field not filled in.

Human intervention

All reports categorised with less than 40% certainty are forwarded to the Action Service Centre. A human assessment then takes place and the report 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 Action Service Centre).

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. Staff at the Action Service Centre review those reports 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 personal data if he or she wants to be kept informed. This data is stored securely and not used by the algorithm.

Operations

Data

Notifications

The dataset consists of notifications made previously (free text field). Initially, we used 300,000 notifications from past years to train the algorithm. It is regularly retrained with new notifications and implemented corrections to existing notifications. If the Action Service Centre or departments see an incorrect categorisation, (see Human Monitoring) they correct it manually in the notification system. These corrections are used in re-training. 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 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 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

Architecture of the model

The text of the notification is divided into individual words. Each word in 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.