1. Supporting the decision-making in urban transformation with the use of disruptive technologies
The Data Governance Act and Data-Driven Policymaking: Impact and Practical
Implementations
URBANITE Project
16/02/2021
Grant Agreement No. 870338 URBANITE 1

2. Grant Agreement No. 870338 URBANITE 2
Adoption of a Data - driven and Evidence - based Decision making in
the urban transformation field, specifically on Urban Mobility.
Adopting a user-centric approach:
• addressing the articulation of expectations, trust and attitude from
civil servants, citizens and other stakeholders in the use of disruptive
technologies as BigData, Data analytics, simulation, IOT, etc.
Four use cases, with potential application on the support on the design,
implementation (and monitoring) of Sustainable Mobility Urban Plan’s
measures.
Objectives

3. Grant Agreement No. 870338 URBANITE 3
Use data for better decision making (analysis, simulation and
prediction)
 Engage citizens and civil servants in the policy making process
increasing trust and capturing the vision of all actors
Get careful guidance on the adoption and implementation of
disruptive technologies (i.e. big data, artificial intelligence, cloud
computing, algorithms)
Asumptions

4. powerful analytics tools
that combine multiple data
sources with advanced
algorithms, simulation,
recommendation and
visualization.
a platform supporting
the entire data processing
chain from collection,
aggregation, provisioning to
using the data.
a digital co-creation
environment and a set of
approaches to help co-
design and co-create
policy guidelines with all
involved actors.
Grant Agreement No. 870338 URBANITE 4
Decision-
Support System
SoPoLab
Data
Management
Platform
URBANITE Solution
Results

5.  Open data currently available
 including identification and recruitment of participants, the
preparation of an informed consent procedure to implement for
individual participation
 The register and use of the virtual participation platform as a
complement of previous sessions
 The transfer of collected data from 3rd parties, defining a transfer
agreement among both parties (company and city use case)
 The potential use of existing personal data on the cities to the
objectives of the project
Grant Agreement No. 870338 URBANITE 5
1st release (M15)
2nd and 3rd releases (m24)(M33)
EU General Data Protection Regulation

6.  Guidance on the implementation and realisation of Trustworthy
AI, via a list of seven requirements that should be met
 human agency and oversight
 technical robustness and safety
 privacy and data governance
 transparency, explicability and explainability.
 diversity, non-discrimination and fairness:
Avoidance of unfair bias
Accessibility and universal design
Auditability.
 environmental and societal well-being
 accountability
Grant Agreement No. 870338 URBANITE 6
Actionability. Models to
yield insights of practical
value, so managers can
harness them :
• Usability
• Confidence
• Interpretability,
• Self-sustainability
• Scalability
Ethics Guidelines for Trustworthy Artificial Intelligence

7.  Legal framework for sharing data.
 More available data -> better policies, more efficient public services.
 Analyze the relationships among Actionability and the DGA
Grant Agreement No. 870338
URBANITE
7
Data Governance
Act
GDPR
AI Ethics
Security &
Privacy
Robustness Transparency,
Equity
Accountability
Data Governance Act

8. SoPoLab
Grant Agreement No. 870338 URBANITE 8
Data Governance Act. Opportunities

9. SoPoLab
9
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 870338
Web: www.urbanite-h2020.eu
Twitter: @urbaniteh2020
LinkedIn: www.linkedin.com/groups/69691
Slideshare: www.slideshare.net/URBANITEProject
GitHub: git.code.tecnalia.com/urbanite