Determine hotspots maintenance Public Space

Municipality of Venlo

To optimise management and maintenance, an integral analysis is performed in which the various management tasks are compared spatially and substantively. This provides insight into where the biggest maintenance tasks lie and where disciplines can reinforce each other.

Last change on 8th of April 2026, at 8:15 (CET) | Publication Standard 1.0

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

General information

Theme

Space and Infrastructure

Begin date

2022-06

Contact information

[e-mail]

Responsible use

Goal and impact

This algorithm aims to display hotspots of maintenance works in public spaces and create a priority list.

Large-scale maintenance is carried out in conjunction as much as possible, avoiding unnecessary work and capital destruction through repeated work as much as possible

Considerations

In the algorithm, the available quality data are first converted into computable values so that the different maintenance tasks can be compared objectively. Next, all data are scaled and each management discipline is assigned its own weighting factor. This creates a double weighting: both the quality of the object and the relative importance of the type of maintenance count towards the total score.

Thereby, some disciplines are leading in programming. Asphalt and sewerage, for instance, generally have a heavier weighting, because delaying them could cause greater risks or consequential damage. Other disciplines, such as green management, are usually next in line and therefore receive a lower weighting factor. Taking this weighting systematically into account creates an integral prioritisation in which both technical urgency and management interest are given a place.

Human intervention

The results of the algorithm are discussed and evaluated annually during the Public Space Table (integral forum of the municipality of Venlo), where priorities are reviewed and tightened where necessary. During this evaluation, there is also a separate session with the managers of the various assets. In this meeting, they check whether any streets are missing that should have appeared on the priority list according to their professional expertise. In this way, the algorithm not only forms an objective basis, but the programming is enriched with the experience of the managers.

The 100 streets with the highest total score are then included in the maintenance programming and scheduled over several years. Interim adjustment is foreseen if necessary.

Reports from the community are not yet included in the model and do not affect the calculated outcome. In the annual discussion, the asset manager can prioritise a street higher later based on his experience and the reports from society he has in view. Small-scale maintenance based on the reports can be carried out by the asset manager within his regular management.

Risk management

The annual review and refinement of the model ensures that the algorithm is continuously improved and better suited to the current situation. In addition, the annual recurrence enables timely identification of new priorities or changes in priority status, allowing maintenance planning to be adjusted where necessary.

Elaboration on impact assessments

There is no high-risk AI, the algorithm is not impactful and no personal data is processed

Operations

Data

The data used in the algorithm comes from the municipal management system GISIB in which all asset data are recorded. In addition, recent inspection results are included, as well as additional maintenance files provided by managers. No personal data are processed in this algorithm.

Technical design

The algorithm is built using the Model Builder in QGIS. Within this model, a priority score is calculated for each street based on preset calculation rules. These calculation rules convert the quality of the assets into numerical values so that different management tasks can be compared.

The value of an asset can be composed of several components, such as the current quality, the expected maintenance year, the urgency of the required maintenance and other relevant factors. All assets are harmonised using one uniform numerical scale from 1 to 10, where 1 represents good quality and 10 represents poor quality.

In addition to this numerical calculation value, each asset is assigned a weighting factor. This factor reflects the relative importance of the respective management discipline. For example, disciplines with a higher risk or impact, such as asphalt and sewerage, may be weighted more heavily than, say, green space management. The final asset score is created by multiplying the quality score by the discipline weighting.

All asset scores within a street are added together to arrive at a total score per street. In the final step, this total score is multiplied by the number of overlapping management tasks in the street. This creates the priority score that indicates which streets have the largest and most urgent maintenance task.

The final result of the algorithm is visualised in a map layer in QGIS. The attribute table shows the underlying data per street, such as the individual scores per asset, the total score and the number of management tasks that converge within the same street.

External provider

Own development, Municipality of Venlo