PoliVisu Toolbox on How to Use Data Visualisations to Support Policy Work

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Basic Information



Latest update



Tool type

Guidance document / Manual

Application area

  • Data gathering
  • Analysis, scenarios and measure selection
  • Appraisal and assessment
  • Evaluation and monitoring
  • Dissemination and communication

Target Audience

  • Small cities
  • Medium-sized cities
  • Large cities
  • Metropolitan regions
  • Other


The aim of the PoliVisu Toolbox(link is external) is to help Public Sector decision-makers learn how to use big data to create visualisations that support their policy work. The tool has been designed to support policy areas related to - but not limited to - transport and mobility.

The Toolbox provides a window to the potential that data can provide for policy-makers and allows users to browse through its contents freely, using selected elements as inspiration for data use in policy, themselves. The backbone of the Toolbox is a series of case studies. These are structured narratives based on real-life examples of data supported policymaking. Each story contains links to related features in the Toolbox that support the story, for example, from the data used to the software tools, the visualisation created, and resulting policy elements.

Visit the online Toolbox to read case studies that look into how the use of data visualisations assisted in making roads around schools safer, supporting people during COVID-19 lockdown, and using communication strategies to improve mobility and modal shift, amongst many others. The tool also provides detailed information for a list of 'Policy Making Ingredients' which includes:

  • Datasets
  • Dataset Types
  • Policy Elements
  • Policy Processes
  • Software Tools
  • Techniques
  • Visualisation Types

Good Example

Thanks to the guidance provided in the Toolbox, pilot cities now have increased awareness of the benefits of data visualisations for more collaborative and effective policy making. Overall Civil servants are more aware that using big data with visualisation tools for analysis of problems can have greater depth as many layers of data relating to the physical and social world can be considered together.

Some results are specific to each pilot city:

  • Issy-les-Moulineaux: The city obtained traffic data that it previously didn’t have. This data was then used to build the Issy Mobility Dashboard, which enabled local policy makers to assess the impact of roadworks on local traffic conditions.
  • Gent: The city obtained proxy data on student locations that was later used to inform planning decisions e.g. where to build student residences?
  • Pilsen: The city harnessed big data from traffic detectors and is now using this information to plan and evaluate traffic policy measures e.g. should the city build a new tram line to university campus? Should a street be converted into a pedestrian zone?
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