Presentation for the workshop “Participatory data for innovation” on the Data Week 2023.

1. Supporting the decision-making in urban transformation with the use of disruptive technologies
H2020 URBANITE Project
Data Week 2023
Luleå, 14th June 2023
Grant Agreement No. 870338 URBANITE 1

2. URBANITE at a glance
Grant Agreement No. 870338 URBANITE 2
1st April 2020 – 30th June 2023
11 partners from 6 European countries
H2020-SC6-TRANSFORMATIONS-2019

3. Grant Agreement No. 870338 URBANITE 3
Partners

4. Context
• Mobility transformation is changing the landscape of urban planning
and the management of mobility in cities. Public administrations and
policy makers need means to help them understand these new
scenarios, supporting them in making policy.
• Disruptive technologies to support policy – makers come into place,
not without generating controversy, and presenting specific challenges.
In addition, new legal, ethical and a policy frameworks including
guidelines, procedures and tools must be designed, always keeping in
mind a user-centered approach.

5. Grant Agreement No. 870338 URBANITE 5
Adoption of a Data - driven and Evidence - based Decision making in
the urban transformation field, specifically on Urban Mobility.
✓The use of data for better decision making
✓Involving public servants and citizens in the policy formulation process,
increasing their trust and capturing the vision of all stakeholders
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.
Objectives

6. URBANITE Solution
Pathways to provide public
administrations guidance on the
adoption of disruptive
technologies and data in their
policy making processes.
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 co-creation environment and a
set of approaches to help co-
design and co-create policy
guidelines with all involved
actors.
Decision-Support
System
SoPoLab
Data
Management
Platform
Recommendations
and
pathways
URBANITE Solution

7. Grant Agreement No. 870338 URBANITE 7
Objectives
Three views:
• Urban Mobility domain view. Use cases aligned with
sustainable urban mobility plans and local policies for
mobility and transport. City KPIs.
• Technical view. Data Management Platform and
supported methods and algorithms.
• Participative view. Process of co-creation sessions,
identifying social challenges associated with the use of
disruptive technologies

8. Grant Agreement No. 870338 URBANITE 8
Participatory sessions
Process of co-creation sessions through the Social Policy Lab, identifying social
challenges associated with the use of disruptive technologies.
Different sessions:
• “Ask: defining challenges and formulating shared values and principles”
• “Create: going into the details of challenges and designing roadmaps”
• “Policy: translating insights into practical policy and requirements”
• “Awareness Raising Activities for sustainable mobility” (MES)

9. SoPoLab (URBANITE Forum)
Grant Agreement No. 870338 URBANITE 9
▪ Enables participants to discuss, debate and document experiences,
challenges, and ideas with participants of the SOPO lab sessions
▪ Instance of Decidim, an open-source participatory democracy
platform
▪ It takes advantage of Decidim’s Assemblies:
▪ A group of members of an organization who meet to discuss and make
decisions about a specific area or scope of the organization
▪ The Urbanite Forum, is currently composed by:
▪ The public Urbanite General assembly used to disseminate project’s
information, incentivate participation of citizens, promote discussions and
debates,.
▪ Private assemblies, one for each use case: Amsterdam, Bilbao, Helsinki and
Messina

10. Data Management Platform
Grant Agreement No. 870338 URBANITE 10
• Data harvesting i.e. fetching data from various heterogenous sources,
transformation and mapping of data and metadata, and storing them
in dedicated databases.
• Ensures that the harvested data is checked and evaluated based on a
defined format that ensure interoperability. Vocabulary, model
definition and data harvesters, following the EU vocabularies and
DCAT-AP meta-data standards.
• Data curation (preparation) - before the actual publication i.e. data
quality checks, data transformation and annotation, data
anonymization and pseudonymization.
• Data aggregation and storage i.e. storage of data and its semantic
description (metadata), aggregation and deduplication of the data
that originates from distinct sources and retrieval of the stored data via
a provided API.
• Data gobernance capabilities, managing ownership and access to data.

11. Grant Agreement No. 870338
Data Analytics, simulation and recommendation

12. Grant Agreement No. 870338 URBANITE 12
More than technology… policies
• Data Collection and Merging
• Data Standards
• Data Infrastructure
• Governance and Accountability
• Use and Analysis
• Communication and capacitation
POLICIES
Extending the analysis and specifying through guides and templates (2021 SuM4AllTM - https://www.sum4all.org/)

13. Grant Agreement No. 870338 URBANITE 13
• DEVOps + DataOps
• Data Analysis
• Data Literacy
GUIDELINES
• DEVOps Capbility level
• DataOps Capbility level
• Data Ethics Maturity Level
• Data Literacy Maturity
MATURITY MODELS
• Harvesters
• Storage
• Anonymize
• Data Catalogue
• Data fusion/aggregation
• Data transformation
• Data preparation
• Scheduler
• Analytical Framework
• Exploratory data analysis
• Identity Management
• Virtual SoPoLab
• URBANITE UI
TOOLS AND TECHNICAL COMPONENTS
More than technology… guidelines and process

14. ▪ Open data previously 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 14
1st release (M15)
2nd and 3rd releases (m24)(M33)
About GDPR and data...

15. ▪The transfer of collected data from 3rd parties, defining a
transfer agreement among both parties
▪ Ring-Ring (AMS) Telraam (AMS)
▪The potential use of existing personal data on the cities to the
objectives of the project
▪ Mesm@rt (MES) Video cameras (Edge) (MES)
▪ Eurostats (All) Public bike rental (BIO/MES)
▪ O/D Matrix (WiFi based) (BIO)
Grant Agreement No. 870338 URBANITE 15
2nd and 3rd releases (m24)(M33)
About GDPR and data...

16. Grant Agreement No. 870338 URBANITE 16
Data Ethics
▪ Ethical aspects of data management
▪ Not only related to personal data
▪ Some steps:
▪ Define and understand public benefit and user
need
▪ Involve diverse expertise
▪ Comply with the law
▪ Review the quality and limitations of the data
▪ Evaluate and consider wider policy implications

17. ▪ 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 17
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

18. Grant Agreement No. 870338 URBANITE 18
Question of Trust and Confidence
Extending and specifying through guides and templates (2021 SuM4AllTM - https://www.sum4all.org/)
Trust about the use of disruptive technologies is a prerequisite for their application in matters of society and
governance. Trust applies on many levels in this regard and from the perspective of citizens and decision-makers
alike, for example:
• trust the data is accurate and reflective of peoples’ lived experiences;
• trust that decision-makers are knowledgeable about the nuances and limitations of the technology they are
using, and are comfortable that with using it;
• trust that decisions are made by humans (not technology);
• trust that privacy and other public values are ensured by the technical systems’ design;
• trust that the technology is open and transparent;
• trust that outputs of AI are explainable valid;
• trust that (human) decisions informed by AI outputs are explainable and valid;
• and more.
“The goal is not to develop trust amongst society (about the use of disruptive tech); but rather to build
technology and protocols that merit public trust because of their openness and alignment with public values”

19. Use Cases
Grant Agreement No. 870338 URBANITE 19
Mobility technicians and experts.
Socialization of simulation.
Operational integration
Bike Data Common. Mobility
technicians, IT responsabiles and
local stakeholders (3rd parties)
Mobility technicians, IT
responsabiles and city
subcontractors (Traffic and
consultancy)
City mobility technicians, urban
planning mayor and public
transport companies

20. SoPoLab
20
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