How are our clients using their location data wisely?

Need inspiration on how location intelligence could add value for you? Enjoy a taste of some success stories.


De Watergroep

Asset health. Being the largest drinking water company in Flanders, De Watergroep needs to manage an extensive network of waterpipes within the Flemish region in a cost-effective manner. In order to have an intelligent investment plan for pipe replacement, De Watergroep chose to set up new processes and redesign existing ones. These processes are controlled on the basis of sensor data, service orders, GIS data, etc. These sources all have distinct requirements in terms of data availability, data quality, the system landscape and reporting. Within the Asset Management Distribution & Supply department, Locus supports by setting up an Asset Health Index, which indicates how well a specific waterpipe is expected to serve in the future. By using data processing techniques (cleaning, transformation, modeling) and (non)-geographical reporting tools, the Asset Health Index contributes to a more feasible investment plan, both short and long term.

Digital Wallonia

Mapping road defects. To optimize road infrastructure maintenance in a cost-efficient way, early detection and monitoring of road defects are essential. By applying Artificial Intelligence on e.g. panoramic imagery, road defects are detected in an automatic manner. Advantages are the possibility to increase the frequency of mapping road defects, the reproducibility and objectiveness of the classifier. When injecting this data in a GIS environment that contains additional information on e.g. traffic flux, accidents or dominance of certain transport modes, streets can be prioritized. Additional benefits are an increased lifespan of assers, road safety and comfort.


Smart Asset Management. How to balance cost management and structural investments required to face the challenges of tomorrow? To support this decision-making process, different risk models were developed which incorporate both the impact and probability of incidents. A multitude of data sources were collected, transformed and combined to tackle these questions in a holistic manner. Next to location bounded impact, in terms of casualties, the impact of breakdowns on the overall network is considered. Results are presented as reports, interactive dashboards and integrated in applications.



As one of the leading Telecommunication players, it is vital to stay ahead of the competition. Location-based data such as network, retail, demographics, competition, and others drive an all-encompassing strategy to reach a competitive advantage. Locus strives to propagate the value of location-based insights and best practices to transform raw data into actionable business insights. This is done through facilitating horizontal cooperation using location data as a lever to connect business information and by assisting in defining location data management and analysis strategies.

De Lijn

De Lijn

De Lijn plays a key role in the mobility of many commuters and travelers. To better serve these passengers, De Lijn is implementing a broad modernization project, which includes improved data collection and usage. Locus is in this process for the collection and processing of passenger data, for which two long-term goals exist. The first is the delivery of customized information to each customer, and the second is providing better insights in passenger flows for strategic decision making within the organization. To achieve these goals, Locus is currently assisting with the implementation of a centralized data platform, where various data flows are meshed and made available for data analysts and scientists. Building on top of the data platform, we also use our expertise in location intelligence to build passenger prediction models and interactive dashboards to support informed strategic decision making.

Ario logo


Non-alert driving is one of the main causes of heavy goods vehicles involved in road accidents. Demerstee Transport Solutions developed a sensor to monitor micro-steering corrections at a high frequency. These micro-corrections are indicative of a driver’s alertness and can, as such, be used as a drivers warning system. The pattern of this steering behavior is, however, vehicle and driver dependent, and should take into account contextual information such as road geometry. In cooperation with IMOB (UHasselt) and UZA, driving simulations were carried out to collect a vast amount of reference data. Using machine learning techniques (time-series analytics, pattern detection, forecasting) real-time sensor data and historical data are combined to assess a driver’s level of alertness.


Horse riders going ‘out’ sometimes lack a feeling of safety due to the remote locations they sometimes go to. In the case of a severe fall, no immediate help can be expected. Detecting those falls and sending a warning message would provide them a safety line. Based on position and other sensor data captured by mobile devices, a machine learning model is developed that is capable of detecting falls. The occurrence of noisy data, caused by e.g. the variety of mobile device sensors, high-speed riding or hurdles makes this a challenging task. Locus has set up a mobile agnostic predictive model, capable of handling factors influencing the incoming data, which assess real-time whether or not to send out an alarm.


Departement MOW

Heavy goods vehicles that make use of the Belgian road network are legally obliged to have a working ‘on board unit’ (OBU). This device tracks a vehicle’s location to deduce the distance traveled on toll roads. Additionally, these registrations also provide a rich data source, which is well suited for a variety of traffic studies. Moving from information to insights and decision-making poses additional challenges, certainly when the size of data to be processed becomes enormous. Concisely presenting a multitude of information to move towards a data-driven policy is critical to leverage the inherent value of this data. In the first phase, Locus has set-up a data pipeline that can handle the massive amount of track & trace data. Next, a data visualization tool was created, which combines the power of statistical and geographical information to ease the adoption of data-driven decisions. By providing straightforward self-service capabilities, users are offered the possibility to retrieve insights in an explorative way.

logo sport vlaanderen

Sport Vlaanderen

Sport Vlaanderen started a pilot project in which the daily activities of a group of test subjects are recorded and annotated using smartphone generated data. Over several months, a large dataset is captured, which is fit for multi-purpose data-analysis. Within the scope of the Proof of Concept, the recorded data is transformed to a format best suited to answer questions related to when, how often and where the test subjects practice sports. Not only does collecting such data allows us to gain insight into general sports participation, but it also provides valuable information regarding active modes of transportation and opportunities in the existing network of sports infrastructure.


Air Liquide

By French law, Air Liquide is obliged to manage information about its pipelines and spatial context, evaluate the risks of its transportation pipelines and report annually on these risks and their compensatory measures. To support this process, a Safety Studies Tool is developed on top of ArcGIS technology. This tool automates complex GIS analysis and allows business users to extract information in the form of predefined reports and maps. As a foundation for the tool, extensive data modeling and data migration process was executed.

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