This document discusses smart cities and the role of data and analytics in creating smarter cities. It covers topics like what makes a city smart, the importance of citizen participation and crowdsourcing, using IoT and linked open data to generate insights. It also discusses challenges around ensuring quality of user-generated data and the need for human-centric collaborative services that leverage big data, crowdsourcing and engagement to improve quality of life in cities.

1. 1
Sustainable Mobility Summer Programme
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing,
Engagement & Co-production
Modulo 4: Bike Intelligence Centers, Data Analytics, Dashboards, Internet of Things
24 de Junio de 2021
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://www.morelab.deusto.es
@dipina

2. 2
Agenda
• Smarter Cities
• ICT levers for Smarter Cities
• Citizen collaboration & co-creation for Smarter
Cities
• Data-centric collaborative web apps

3. 3
What is a Smart City?
• Smart City is a place where urban services are
improved in efficiency by applying ICT, for the
benefit of its inhabitants and economic
development
• Smart Territories innovative geographic areas,
able to build their own competitive advantages
taking into account their context
• Smart Places→ balance among economic
competitiveness, social cohesion, innovative
creativity, democratic governance and
environmental sustainability
– Satisfying the basic and self-fulfilment needs in
the Maslow pyramid

4. 4
Smarter Cities
• Smarter Cities → cities that do not only manage their
resources more efficiently but also are aware of the
citizens’ needs.
– Human/city interactions leave digital traces that can be
compiled into comprehensive pictures of human daily facets
– Analysis and discovery of the information behind the big
amount of Broad Data captured on these smart cities’
deployment
Smarter Cities = Internet of Things + Broad Data + Citizen
Participation through Smartphones + Urban Analytics

5. 5
ICT as levers of Smarter Cities: Big | Open
| Personal Data
• Big potential for enterprises, social entities and governments
if there is a better usage of infrastructure and information
(IoT + Open + Personal data) in urban environments:
– Big Data: extensive analysis of heterogeneous urban data to offer
answers, indicators and visualizations to help improvinng the decision
criteria upon the challenges of cities and territory management
• It will allow us to progress towards more disruptive
approaches
– All agents should benefit from a more efficient usage of data
processing technology to give place to Urban Analytics
• Great potential but huge difficulty associated!

6. 6
ICT as levers of Smarter Cities: Open
Collaboration
• Smarter environments cannot only be
reached through technological solutions
– We have to take advantage of the huge potential
of collective intelligence and citizenship capacity
to generate knowledge through crowdsourcing
techniques (or open distributed collaboration)

7. 7
ICT as levers of Smarter Cities: Social
Innovation + Open Government
• Digital Social Innovation: collaboration of citizenship through new
technologies to co-create knowledge and solutions addressed to an ample
range of social needs through Internet, e.g.:
– Social networks for those that suffer chronic diseases
– Platforms for citizen collaboration (Bike Intelligence Centre)
– Open data for transparency and good government linked to public
expenditure
• Collaborative Open Government: combination of Technology and
participation of different agents and sectors of society in government
– Give answer to citizen needs and demands with reduced time and budgets
– Improve the business environment providing better services to enterprises
and citizenship
– Adapt the service provision to the needs of a more digital economy

8. 8
What is a Smart Sustainable City?
A smart sustainable city is an innovative city that uses
information and communication technologies and
other means to improve quality of life, efficiency of
urban operation and services, and competitiveness,
while ensuring that it meets the needs of present and
future generations with respect to economic, social and
environmental aspects
https://itunews.itu.int/en/5215-What-is-a-smart-sustainable-city.note.aspx

9. 9
Smart Diamond of Smart Cities

10. 10
The need for Participative Cities
• Not enough with the traditional resource efficiency
approach of Smart City initiatives
• “City appeal and dynamicity” will be key to attract and
retain citizens, companies and tourists
• Only possible by user-driven and centric innovation:
– The citizen should be heard, EMPOWERED!
» Urban services to enhance the experience and interactions of the
citizen, by taking advantage of the city infrastructure
– The information generated by cities and citizens must be linked
and processed
» How do we correlate, link and exploit such humongous data for all
stakeholders’ benefit?
• Demand for Big (Linked) Data for enabling Urban Analytics!!!

11. 11
• Smart Cities seek the participation of citizens:
– To enrich the knowledge gathered about a city
not only with government-provided or networked
sensors' provided data, but also with highly
dynamic user-generated data
• BUT, how can we ensure that users and their
generated data can be trusted and have enough
quality?
Citizen Participation

12. 12
• There is a need to analyze the impact that
citizens may have on improving, extending
and enriching the data
– Quality of the provided data may vary from one
citizen to another, not to mention the possibility
of someone's interest in populating the system
with fake data
• Duplication, miss-classification, mismatching and data
enrichment issues
Problems associated to User-provided Data

13. 13
Urban Intelligence / Analytics
• Broad Data aggregates data from heterogeneous sources:
– Open Government Data repositories
– User-supplied data through social networks or apps
– Public private sector data or
– End-user private data
• Humongous potential on correlating and analysing Broad Data in the city
context:
– Leverage digital traces left by citizens in their daily interactions with
the city to gain insights about why, how and when they do things
– We can progress from Open City Data to Open Data Knowledge
• Energy saving, improve health monitoring, optimized transport system,
filtering and recommendation of contents and services

14. 14
From Open Data to Open Knowledge

15. 15
Internet of Things (IoT): Motivation
• Do you want to know how many
steps you have walked?
• Do you want to know how many
kilometres you have driven?
• How many watts have you
consumed?
• How to improve the bike routes
in your city?
• Internet of Things can tell you
this and much more

16. 16
Internet of Things … connecting information,
people and things

17. 17
6 facts about IoT
1. IoT is the term used to describe any kind of application that
connected and made “things” interact through the Internet
2. IoT is a communication network connecting things which
have naming, sensing and processing abilities
3. IoT is the next stage of the information revolution, i.e. the
inter-connectivity of everything from urban transport to
medical devices to household appliances
4. Intelligent interactivity between human and things to
exchange information & knowledge for new value creation
5. IoT is not just about gathering of data but also about the
analysis and use of data
6. IoT is not just about “smart devices”; it is also about devices
and services that help people become smarter

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Sensors

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Connectivity

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People & Process

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Nature of Data in IoT
• Heterogeneity makes IoT devices hardly interoperable
• Data collected is multi-modal, diverse, voluminous
and often supplied at high speed
• IoT data management imposes heavy challenges on
information systems

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IoT Key Components

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IoT Architecture
Sensing Layer
Communication Layer
Management Layer

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Personal data: SmartWatch & Health-
promoting Data Devices

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Quantified Self & Life Logging
• Quantified self is self-knowledge through self-tracking with technology
– Movement to incorporate technology into data acquisition on aspects of a
person's daily life in terms of inputs (e.g. food consumed, quality of
surrounding air), states (e.g. mood, arousal, blood oxygen levels), and
performance (mental and physical)
• Self-monitoring and self-sensing through wearable sensors (EEG, ECG, video, etc.)
and wearable computing → lifelogging
• Application areas:
– Health and wellness improvement
– Improve personal or professional productivity
• Products and companies:
– Apple Watch, Fitbit tracker, Jawbone UP, Pebble, Withings scale

26. 26
Human-mediated Mobile Sensing
• The combination of varied data sources such as Humans,
SmartPhones and sensors gives place to Mobile
Sensing/Participatory Sensing & CrowdSensing → Broad Data

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User-generated Data: Google Maps vs.
Open Street Map
• OSM is an excellent cartographic product driven by user contributions
• Google Maps has progressed from mapping for the world to mapping from the world,
where cartography is not the end product, but rather the necessary means for:
– Google’s autonomous car initiative, combine sensors, GPS and 3D maps for self-driving cars.
– Google’s Project Wing: a drone-based delivery systems to make use of a detailed 3D model
of the world to quickly link supply to demand
• By connecting the geometrical content of its Google Maps databases to digital traces
that it collects, Google can assign meaning to space, transforming it into place.
– Mapping by machines if not about “you are here”, but to understand who you are, where
you should be heading, what you could be doing there!

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Linked Data
• “A term used to describe a recommended best practice for
exposing, sharing, and connecting pieces of data, information,
and knowledge on the Semantic Web using URIs and RDF.“
• Allows to discover, connect, describe and reuse all sorts of data
– Fosters passing from a Web of Documents to a Web of Data
• In September 2011, it had 31 billion RDF triples linked through 504 millions of
links
• Thought to open and connect diverse vocabularies and semantic
instances, to be used by the Semantic community
• URL: http://linkeddata.org/

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Example of Linked Data Modelling
http://…/isb
n978
Programming the
SemanticWeb
978-0-596-15381-6
Toby Segaran
http://…/publi
sher1
O’Reilly
title
name
author
publisher
isbn
http://…/isb
n978
sameAs
http://…/rev
iew1
Awesome
Book
http://…/rev
iewer
Juan Sequeda
http://juansequed
a.com/id
hasReview
hasReviewer
description
name
sameAs
livesIn
Juan Sequeda
name
http://dbpedia.org/Austin

30. 30
Actionable Knowledge from Urban Data
• Don’t care about the sensors, care about knowledge
extracted from their data correlation & interpretation!
– Data is captured, communicated, stored, accessed and shared
from the physical world to better understand the surroundings
– Sensory data related to different events can be analysed,
correlated and turned into actionable knowledge
– Application domains: e-health, retail, green energy, transport,
manufacturing, smart cities/houses

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Data Understanding through Linked Statistics
& Visualizations

32. 32
Crowdsourcing
• Crowdsourcing: process of obtaining needed services, ideas, or
content by soliciting contributions from a large group of people,
especially an online community, rather than from employees or
suppliers
– Collective intelligence is shared or group intelligence that emerges
from the collaboration, collective efforts, and competition of many
individuals and appears in consensus decision making.
• Some good examples:
– Wikipedia & WikiData (its data version)
• Tutorial: https://en.wikipedia.org/wiki/Wikipedia:Tutorial
– Open Street Map
– Stack Overflow: a language-independent collaboratively edited
question and answer site for programmers

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CrowdSensing
• Individuals with sensing and computing devices collectively
share data and extract information to measure and map
phenomena of common interest

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Wikipedia
• Goal is to create a comprehensive and neutrally written summary of existing
mainstream knowledge about a topic.
– Allows you to create, revise, and edit articles
– All information should be actually cited to reliable sources to evidence it is verifiable
• Information is edited through wiki markup (wikitext) or newer VisualEditor
– Wikipedia uses text codes called wiki tags to create particular elements (e.g. headings)
• Cheat sheet: https://en.wikipedia.org/wiki/File:Wiki_markup_cheatsheet_EN.pdf
• An infobox is a fixed-format table designed to be added to the top right-hand
corner of articles to present a summary of some unifying aspect that the articles
share and sometimes to improve navigation to other interrelated articles.
– https://en.wikipedia.org/wiki/Wikipedia:List_of_infoboxes
– https://en.wikipedia.org/wiki/Template:Infobox_organization
• Examples about editing Wikipedia contents:
– https://en.wikipedia.org/wiki/University_of_Deusto
– https://en.wikipedia.org/wiki/International_University_of_Andaluc%C3%ADa

35. 35
Wikidata & DBpedia
• Wikidata is a volunteer-created knowledge base of structured data that anyone
can edit
– Focused on structured data: possible for humans and computers alike to use the data
– Many ways to contribute to Wikidata: translate, write apps, add and edit data.
– It works with:
• Items – abstract concepts with theirs own and a unique identifier (Q###) and optionally a label,
description and aliases
• Statements are added to items: category of data as a property, while the data that describes
an item for a given property is known as a value.
– Example: entry for Everest mountain https://www.wikidata.org/wiki/Q513
– Documentation: https://www.wikidata.org/wiki/Wikidata:Tours
• DBpedia, a project to create a graph from Wikipedia data – allows users to
semantically query relationships and properties associated with Wikipedia
resources, including links to other related datasets
– Wikipedia articles consist mostly of free text, but also include structured information
embedded in the articles, such as "infobox" tables, categorisation information, images,
geo-coordinates and links to external Web pages.
• This structured information is extracted and put in a uniform dataset which can be queried:
https://dbpedia.org/sparql
• Sample queries

36. 36
• OpenStreetMap is a tool for creating and sharing map information.
– Anyone can contribute to OSM, and thousands of people add to the project
every day.
– After registering you can manipulate maps with the help of Edit with iD (in-
browser editor) or JOSM (Java OpenStreetMap editor)
– Represents physical features on the ground (e.g., roads or buildings) using tags
attached to its data structures (nodes, ways & relations).
– Data generated by the OpenStreetMap project are considered its primary
output.
• Its API accessed at: http://wiki.openstreetmap.org/wiki/API_v0.6
• Example (UNIA):
– http://www.openstreetmap.org/#map=18/37.99105/-3.46953
• Documentation: http://learnosm.org/en/

37. 37
Smart City Data
• Data is multi-modal and heterogeneous
• Noisy and incomplete
• Time and location dependent
• Dynamic and varies in quality
• Crowded sourced data can be unreliable
• Requires (near-) real-time analysis
• Privacy and security are important issues
• Data can be biased- we need to know our data!
• Data alone may not give a clear picture -we need contextual information,
background knowledge, multi-source information and obviously better
data analytics solutions…

38. 38
Smart Cities Data Exploitation
• Discovery: finding appropriate device and data sources
• Access: Availability and (open) access to data resources and data
• Search: querying for data
• Integration: dealing with heterogeneous devices, networks and data
(Semantic interoperability)
• Large-scale data mining, adaptable learning and efficient computing and
processing
• Interpretation: translating data to knowledge that can be used by people
and applications
• Scalability: dealing with large numbers of devices and a myriad of data
and the computational complexity of interpreting the data.

39. 39
Analytics in the Smart City: Data-
driven decision making

40. 40
IoT & Big Data
• The more data that is created, the better understanding and
wisdom people can obtain

41. 41
Scope of Advanced Analytics
Source: Moving from Descriptive to Cognitive Analytics on your Big Data Projects, Gene Villeneuve, IBM,
http://www.slideshare.net/ibmsverige/gene-villeneuve-moving-from-descriptive-to-cognitive-analytics
What could happen in the future?
Information Layer
How is data managed and stored?
How can everyone
be more right…
….more often?
Descriptive
What has already happened?
Predictive
Prescriptive
How can we achieve the best outcome?
Cognitive
How can we learn dynamically?
Business
Value
Business
Value
▪ Reasoning
▪ Learning
▪ Natural Language
▪ Alerts & Drill Down
▪ Ad hoc Reports
▪ Standard Reports
▪ Big Data Platforms
▪ Content Management
▪ RDBMS and Integration
▪ Machine learning
▪ Forecasting
▪ Statistical Analysis
▪ Optimization
▪ Rules
▪ Constraints
Big Data & Analytics

42. 42
IES Cities Project
• The IES Cities project promotes user-centric
mobile micro-services that exploit open data
and generate user-supplied data
– Hypothesis: Users may help on improving, extending
and enriching the open data in which micro-services
are based
• Its platform aims to:
– Enable user supplied data to complement, enrich and
enhance existing datasets about a city
– Facilitate the generation of citizen-centric apps that
exploit urban data in different domains

43. 43
Bristol’s Democratree App

44. 44
WeLive H2020 Project Aim
WeLive provides tools and a methodology to promote co-
creation where data publishers, citizens and developers
can meet each other and co-design and co-exploit
personalized and sustainable public services for real needs
and actively take part in the value-chain of a municipality
or a territory
WeLive provides tools and a methodology to promote co-
creation where data publishers, citizens and developers
can meet each other and co-design and co-exploit
personalized and sustainable public services for real needs
and actively take part in the value-chain of a municipality
or a territory
Citizens
Citizens Companies
Companies
P. Administration
P. Administration

45. 45
Why current ICT support is not enough?
Beyond Open Data Government Portals
CITIZENS have
NO SKILLS or TOOLS to
utilize COMPLEX DATA
LOW BENEFITS
from OPEN DATA
published by CITIES

46. 46
WeLive approach
Stakeholder Collaboration + Public-private Partnership →
IDEAS >> APPLICATIONS >> MARKETPLACE
Stakeholder Collaboration + Public-private Partnership →
IDEAS >> APPLICATIONS >> MARKETPLACE
WeLive offers tools to transform the needs into ideas
WeLive offers tools to transform the needs into ideas
Tools to select the best Ideas and create the B. Blocks
Tools to select the best Ideas and create the B. Blocks
A way to compose the
Building Blocks into mass
market Applications which
can be exploited through the
marketplace
A way to compose the
Building Blocks into mass
market Applications which
can be exploited through the
marketplace
1
2
3
Video

47. 47
WeLive CO-CREATION Approach
• In WeLive, a two-phase CO-CREATION approach is accomplished:
– Diverse stakeholders participate in distinct collaborative activities and
events (CO-CREATION ACTIVITIES)
– The whole process is assisted by https://dev.welive.eu/ PLATFORM
– NEEDs are mapped into IDEAS which are realized into ARTEFACTS: Mobile
or Web Urban Apps

48. 48
Co-creation assets in WeLive
The methodological approach is based on four main concepts:
an emerging or existing NEED that a citizen submits to
the PA.
an open CHALLENGE call launched by the PA to involve
the users to participate to solve the reported need.
a possible solution IDEA proposed by a stakeholder to
solve a pending need or to participate to a challenge.
ARTEFACT: useful web service (Building Block), open
data or web/mobile app published addressing the
challenge to be consumed by the users
Resource /
Artefact
WeLive platform
Need
Challenge
Idea

49. 49
Full Co-Creation Lifecycle Support
co-business
co-maintenance
co-implementation
co-ideation
WeLive Platform
WeLive Hosting Environments
CO-DESIGN
The core WeLive Platform supports the first phases
of the co-creation lifecycle by giving tools for
innovating and implementing services together
CO-EXPLOITATION
WeLive Hosting Environments support co-maintenance
of co-created services. Preliminary co-business support
has been implemented into the CNS Marketplace
CO-CREATION
Co-creation of SUSTAINABLE services requires
support for both co-design and co-exploitation

50. 50
Co-creation assets in WeLive

51. 51
Crowdsourcing & gamification are not
enough to truly engage users …
• Civil servants are reluctant to moderate the
contents provided by end-users
• End users are usually initially motivated, but their
contributions are diminished in time:
– Receiving no feedback is discouraging
– If the benefit is not clear or reward immediate →
eventually user contributions diminish
• Conclusion: Human Computation is appealing but
requires (moderation + automatic quality
assessment) and continuous high involvement

52. 52
AUDABLOK: User-engagement for
for Data Refinement
• AUDABLOK explores how to turn consumers of open
data (public services) into prosumers:
– refining and enhancing contents, through incentivized
crowdsourcing, encouraging more proactive users
• Software framework to make open government data
portals increasable evolvable and sustainable in
time. HOW?
– By combining Human Computation & Internet of People
– KISS principle: Pull Request combined with Blockchain

53. 53
Engagement driven by Incentivization
• AUDABLOK improves citizen collaboration through
incentivisation (token economy) and recognition,
i.e., trustworthy recording of citizen collaborations
– Blockchain is used to deal with rewarding and recognition
aspects, i.e. higher co-creation of citizens

54. 54
Continuous
Engagement =
Pull request +
Blockchain

55. 55
AUDABLOK: Technical solution
• Integrate:
– Redmine – an open source issue management system for
handling issues raised from user-generated contributions
– Ethereum —an open source, public, blockchain-based
distributed computing platform and operating system
featuring smart contract (scripting) functionality—into
– CKAN open data management software—an open source
tool which makes data accessible by providing tools to
streamline publishing, sharing, finding, and using data.
• AUDABLOK behaves as smart oracle which feeds
Ethereum network, recording citizen-initiated Open
Data refinement transactions

56. 56
Creando un Sistema GIS Participativo
• Un sistema GIS cuenta comúnmente con tres subsistemas:
– Subsistema de datos: Se encarga de la entrada y salida de datos. Debe de
estar preparado para acoger datos espaciales.
– Subsistema de visualización y edición: Permite representar y editar los datos
de manera visual, normalmente con el uso de mapas y proyecciones.
– Subsistema de análisis: Ofrece funciones para analizar los datos espaciales.
• Aspectos a considerar:
– Sistemas de coordenadas (WebMercator —EPSG:3857, WGS84 —EPSG:4326)
y proyecciones (Mercator y la equirrectangular)
– Formatos de datos espaciales (GPX, KML, GeoJSON)
– Servidor de mapas (OpenMapTiles) y BBDD geospacial (PostGIS)
– Pintado de mapas y capas (OpenLayers)

57. 57
Bizkaia Bike Intelligence
• Available at: http://bizkaiabikeintelligence.deustotech.eu/en/datacentre
Frontend
API
Smart
Citizen
Web-server
Base de
datos
API
BIC
BID App
BikeCitizens
...
Otras
fuentes
(administraciones,
sensores fijos…)
Usuario final
Almacenamiento y
cálculo espacial
Servicio web,
procesamiento de
datos
Visualización y
tratamiento
dinámico
bizkaiabik....deustotech.eu

58. 58
Bizkaia Bike Intelligence Architecture
Django/GeoDjango
Django REST
framework
Postgres /
PostGIS
ORM
API
Smart
Citizen
Web Server
OpenLayers,
Bootstrap
Pintado y estilizado de
mapas
Frontend
BD
--BID data-->
<-- llamada a API--
--página web -->
<--URL petición--
templates
urls.py
views.py
HTTP POST/GET
UI
<-- llamada API--
Se encarga de presentar el
HTML y los archivos estáticos
correspondientes
Recoge los datos de la BD y los
renderiza junto con la plantilla
correspondiente
Gestiona las peticiones y les
asigna una vista

59. 59
Bizkaia Bike Intelligence Solution

60. 60
Conclusions
• Human-centric and driven technology must help us
progressing towards more sustainable territories
– Innovations associated to hardware and software, data
modelling and analytics should be combined with novel
user-engagement and collaboration approaches
• From data to knowledge, i.e. decision making there is
a long path
– Open Data, crowdsourced data must be leveraged to give
place to next generation smart data collaborative
solutions, e.g. Bike Intelligence Centre
• Smarter Cities: better informed people aided by
more human-centric technology and services.

61. 61
Sustainable Mobility Summer Programme
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing,
Engagement & Co-production
Modulo 4: Bike Intelligence Centers, Data Analytics, Dashboards, Internet of Things
24 de Junio de 2021
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://www.morelab.deusto.es
@dipina