Combining ICT and User Participation to Enable Smarter Cities

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Combining ICT and User Participation to give place to
Smarter Cities through Open Government
21 March 2018, Deusto Business School, Bilbao, Spain
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es

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Agenda
1. Introduction: Technological and methodical pillars for Smarter
Environment Enablement
2. Part I: Smarter Environments Theoretical Grounding
– What is a Smart Environment?
– Technological enablers: IoT, Web of Data and Persuasive Technologies
– Technology mediated Human Collaboration: need for co-creation
– Killer application domains: Open Government & Age-friendly cities
3. Part II: Review of core enablers for Smarter Environments
– Co-creation methodologies: Design for Thinking
– Internet of Things and Web of Things
– Web of Data: Linked Data, Crowdsourcing & Big Data
4. Part III: WeLive Case Study
– WeLive as Open Government enabling methodology and platform
– Reflections on the need for collaboration among stakeholders to realize Smarter Cities
5. Conclusions and practical implications

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Context (I)
• Two of the biggest challenges faced by
current society are:
–continuous urbanisation process
–progressive population ageing
• In Spain, 70% population lives in urban
centres, where 18,4% > 65 years

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Context (II)
• Cities are responsible for 70% world’s GDP and 80% of CO2
emissions
– Cities are more efficient environments urbanisation process
• Public spending associated to ageing in Europe (pensions,
health systems, long life caring and learning) is about 20% of
GDP
• Important to conceive and adopt solutions to counter-back
the social and economic effects of urbanisation and ageing in
society
– Solution: technology + social innovation

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Smart Environments
• 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

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Challenges for Smarter Cities
• Enable life, work and leisure
environments which allow our self-
fulfilment without disregarding basic
needs and their development in
welfare society
• Answer to the urbanization
demands in a economically feasible,
socially inclusive and sustainable
manner
– BUT… apply traditional solutions to
the needs of urban development 
unsustainable urban ecology
footprint
• Generate more electricity or new
water resources not addressing
inefficiencies in distribution

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ICT as levers of Smarter Cities (I)
• ICTs will help in the urbanization process only if the following
three premises are fulfilled:
1. Social equity
2. Economic feasibility and
3. Environmental sustainability
• ICTs are key to leverage the existing infrastructure and
maximize the socioeconomic throughput
• A more rational and extensive usage of ICT in cities and places  a
quicker and more economic fulfilment of urban challenges

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ICT as levers of Smarter Cities (II): 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 improving 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 or Physical Spaces
Analytics
• Great potential but huge difficulty associated!

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ICT as levers of Smarter Cities (III): 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)

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ICT as levers of Smarter Cities (IV): 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
– 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

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ICT as levers of Smarter Cities (V):
Ethical Implications
• Personal data are the “new petrol” of XXI century, being exploited by big
corporations such as Google, Apple (publicity + marketing) BUT …
– There are multiple distributed personal data silos among different Internet
providers and institutions which have to be interoperable
– There is a need for individuals to have a greater control of their own personal data
• Governments must:
– Regulate, protect, legislate to guarantee the rights and opportunities of such data
providers (we)
– Legislate and manage non-functional aspects (accessibility – technological
inclusion, privacy, data protection and ethics to achieve responsible technological
solutions
• Urban knowledge management is a key to undertake innovation in the public
sector and achieve the implication of private agents for a more fruitful public-
private partnership
– Need to progress from Urban Open Data to Open Knowledge!!!

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Personal Data
• Defined as "any information
relating to an identified or
identifiable natural person
("data subject")”

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From Open Data to Open Knowledge

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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

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Smart Diamond of Smart Cities

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Smart City Applications
• sadfafd

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Ambient Assisted Cities: Age-
friendly Smart Cities
• The main attribute of a Smart City is efficiency
• An Age-friendly city is an inclusive and accessible
urban environment that promotes active ageing
• The main attributes of an Ambient Assisted
(Smarter) City are:
– Livable
– Accessible
– Healthy
– Inclusive
– Participative
[WHO Global Network of Age-friendly Cities]

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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 apps 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!!!

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• 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 has enough
quality?
– W3C has created the PROV Data Model, for provenance
interchange
Citizen Participation

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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

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Agenda

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Smarter Public Open Spaces
• Smarter Spaces spaces 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 spaces
deployment
Smarter Places= Internet of Things + Broad Data + Citizen
Participation through Smartphones + Analytics

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Internet of Things … connecting
information, people and things

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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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Industrial Internet of Things (IIoT)
• The Industrial Internet of Things (IIoT)
represents industry-oriented applications
where:
– Devices are machines operating in industrial,
transportation, energy or medical
environment
– Data volumes and rates tend to be from
sustained to relatively high
– Applications are mission and or safety
critical, e.g. the failure of a smart grid impacts
life and economy, misbehaving of a smart
traffic system can threaten drivers
– IIoT applications tend to be “system-centric”

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Value of IoT
• Information within the Internet of Things creates value in a
never-ending value loop consisting of 5 stages (CREATE … to ACT):

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iBeacon – a class of BLE devices that broadcast their
identifier to nearby portable electronic devices (I)

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iBeacon – a class of BLE devices that broadcast their
identifier to nearby portable electronic devices (II)

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IoT & Cloud Computing
Interdependency
• Cloud computing and IoT are tightly coupled
– The growth of IoT and the rapid development of
associated technologies create a widespread connection of
“things.”
• Leads to production of large amounts of data, which needs
to be stored, processed and accessed
– Cloud computing as a paradigm for big data storage and
analytics
• The combination of cloud computing and IoT will
enable new monitoring services and powerful
processing of sensory data streams.

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Semantic Web
• Problems with current web:
– The meaning of the web is not understandable by
machines
• Semantic Web creates a universal information
Exchange means, providing semantics to the
documents in the web
– Adds meaning understandable by machines in the Web
– Applies intelligent techniques which explore semantics
– Lead by Tim Berners-Lee from W3C
• Mission  “turning existing web content into
machine-readable content“: Web of Data

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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/
• Example:
– https://datahub.io/dataset/morelab

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Linked Data Principles
1. Uses URIs to identify things
2. Uses HTTP URIs to enable those
things to be dereferenced by both
people and user agents
3. Provides useful info (structured
description and metadata) about a
thing/concept referenced by an URI
4. Includes links to other URIs to
improve related information
discovery in the web

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Linked Data Life Cycle
• Linked Data must go through several stages (several
iterations on Linkage) before are ready for exploitation:

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Linked Data Example
http://…/isb
n978
Programming the
Semantic Web
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://juanseque
da.com/id
hasReview
hasReviewer
description
name
sameAs
livesIn
Juan Sequedaname
http://dbpedia.org/Austin

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Schema.org
• Initiative launched in 2011 by Bing, Google, Yahoo and then Yandex
• Objective: “create and support a common set of schemas for structured data
mark-up on web pages.”
– Propose to use their schemas to annotate contents in a web page with metadata
• Metadata are recognized by search engines and other parsers, thus accessing to the
“meaning” of portals
• Their vocabularies were inspired by earlier formats like Microformats, FOAF,
GoodRelations and OpenCyc
• Offer schemas in the following domains
(http://schema.org/docs/schemas.html):
– Events, health, organization, person, place, product, offer, revisión and so on.
• To map declarations in microdata to RDF the following tools can be used:
http://tools.seochat.com/category/schema-generators
• More info at: http://schema.org/
• Examples:
– http://schema.org/CreativeWork
– http://paginaspersonales.deusto.es/dipina/ (microdata.reveal Chrome plugin)

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Schema.org
• Linked Data highlights the Entities and Relationships in your data:
Record
Title: "War and Peace"
Author: "Leo Tolstoy 1828-‐1910"
ISBN: 0307266931
Entity
(http://worldcat.org/entity/work/id/115206288)
Type: Work
Name: "War and Peace"
Author: http://worldcat.org/entity/person/id/1234
Entity
(http://worldcat.org/entity/person/id/1234)
Type: Person
Name: "Leo Tolstoy"
Born: 1828
Died: 1910
Birthplace: http://worldcat.org/entity/place/id/8976
Entity
(http://worldcat.org/entity/place/id/8976)
Type: Place
Name: "Yasnaya
Polyana“
SameAs: http://geonames.org/468686

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Google Knowledge Graph
• Knowledge Graph is a knowledge base used by Google to
improve the results obtained by its search engine by means of
semantic search over information collected from a wide range of
resources
– It was added to Google search engine in 2012
• https://en.wikipedia.org/wiki/Knowledge_Graph
• Provides structured and detailed information about a given topic,
together with a list of links to other sites
• This information is derived from multiple resources including CIA
World Factbook, Freebase and Wikipedia
– Its semantic network contains more than 570 million objects and more
than 18 million facts about – and relations among – those different
objects which are used to understand the meaning of the terms used in the
search query

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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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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/Baeza
– https://en.wikipedia.org/wiki/International_University_of_Andaluc%C3%ADa

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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:
http://live.dbpedia.org/sparql

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• 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/

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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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Data has changed
• 90% of the world’s data
was created in the last two
years
• 80% of enterprise data is
unstructured
• Unstructured data growing
2x faster than structured

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IoT & Big DataTensHundredsThousandsMillionsBillionsConnections
Internet of Things
Machine-to-Machine
Isolated
(autonomous, disconnected)
Monitored
Smart Systems
(Intelligence in Subnets of Things )
Telemetry
and
Telematics
Smart Homes
Connected Cars
Intelligent Buildings
Intelligent Transport
Systems
Smart Meters and Grids
Smart Retailing
Smart Enterprise
Management
Remotely controlled
and managed
Building
automation
Manufacturing
Security
Utilities
Internet of Things
Sensors
Devices
Systems
Things
Processes
People
Industries
Products
Services
Growth in connections generates
an unparalleled scale of data
Source: Machina Research 2014

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Analytics in the Smart City: Data-
driven decision making

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Nature of Broad Data: IoT + User-
generated + Open Data
• Heterogeneity makes IoT devices, people’s and governments’
data hardly interoperable
• Data collected is multi-modal, diverse, voluminous and often
supplied at high speed
• Broad Data management imposes heavy challenges on
information systems

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What is Big Data?
• "Big Data are high-volume, high-velocity, and/or high-variety
information assets that require new forms of processing to
enable enhanced decision making, insight discovery and
process optimization“ Gartner, 2012
– The term “Big Data” was originated within the open source
community, where an efforts was made to develop analytics
processes which were faster and more scalable than the ones
available at traditional Data Warehousing, and could extract value
from the immense non-structured data volumes daily produced by
web users
• It is an opportunity to encounter perceptions in new
emerging types of data and contents, to:
– Make the business more agile
– Answer complex queries which were before beyond your scope

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

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Agenda

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Why WeLive?
Current public services are built following an administration-
centric approach which results into a low usage of those services.
PAs are facing key socioeconomic challenges such as
demographic change, employment, mobility, environment… plus
the squeeze on public finances
Citizens expectations in terms of burden reduction, efficiency and
personalisation are growing…
Cities and territories should be transformed into hubs of welfare,
innovation and economic growth giving place to Smarter Cities or
territories.

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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

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WeLive 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
Citizens CompaniesP. Administration

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WeLive approach
Stakeholder Collaboration + Public-private Partnership →
IDEAS >> APPLICATIONS >> MARKETPLACE
WeLive offers tools to transform the needs into ideas
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
1
2
3

63
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

64
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

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CO-CREATION through Building Blocks
• A building block captures and encapsulates a digital capability of a smart city
and is made available to developers through a REST API.
– Typically give access to one or more datasets and applies some business logic around
them, e.g. filtering, aggregation, augmentation/correction of an existing dataset.
– Encapsulates business logic reused through public composite service (app) building
• Features:
– Adaptability & Personalization
• Same logic applied to distinct datasets, e.g. osm-query-Bilbao, BB Events in distinct cities
– Reusability & Composability
• Enable reuse by composition in many apps
– Simplify consumption
• Aggregation, filtering, augmentation, correction of datasets
• Cross-cutting common interest business logic
– Different facets
• Core, common and domain/dataset/app-specific building blocks

66
WeLive co-creation support
Infrastructure

67
WeLive Co-creation Process, where CO-DESIGN
= CO-IDEATION + CO-IMPLEMENTATION
Ideation Board:
Collaborative tool to
map NEEDS into
CHALLENGES giving
place to IDEAS
CO-IDEATION
Council CHALLENGES +
Stakeholders’ NEEDs and
comments
Refined Selected IDEAS

68
CO-IDEATION can not
be only techology
driven, supported by
Game Designs,
questionnaries, focus
groups

69
CO-
IMPLEMENTATION
Artefacts Available in Service
Catalogue(BBs, mashups,
datasets) + Ideas specification
from Ideation Board
New Artefacts (BBs (Mashups),
datasets, Apps + Mockups)
published in Services Catalogue
Service Composer:
Facilitates creation
of mash-ups (BBs)
WeLive RESTful API &
Developer’s documentation:
Programming API to access
WeLive capabilities

70
CO-IMPLEMENTATION
requires good
documentation,
support, dynamization
events (hackathons)

71
WeLive Co-creation Process: CO-EXPLOITATION
CO-
EXPLOITATION:
co-
maintenance
+ co-business
Artefacts published in Services
Catalogue + Analytics
dashboard indicators & User
feedback
Revenues for co-creators +
Cloud Providers & Platform
Manager fees + PA better public
service to citizenship
Analytics Dashboard:
Review impact of
WeLive co-creation
and assets
Service Catalogue & CNS
Marketplace: Browse, select &
choose, use, install WeLive
artefacts

72
WeLive Co-creation Process: CO-EXPLOITATION

73
Illustrating CO-DESIGN: CO-IDEATION and
binding after CO-IMPLEMENTATION
Challenge
Launch
Ideas
Contest
Idea
Formulation
Selection
Binding
with
artefacts

74
Illustrating CO-CREATION: CO-IMPLEMENTATION (CO-
DESIGN) followed by publication (CO-EXPLOITATION)
Select datasets &
BBs
Generate
datasets &
BBs
Create app
Bind BBs and
datasets to
app
Publish app
15/03/2018

75
Exemplary participation and collaboration
dynamics used
Game Design Session
Gamified sessions
CO-IDEATION
CO-IMPLEMENTATION & CO-MAINTENANCE
Session where a set of basic elements and characters are
presented, based on them new services & apps concepts
are identified, voted and selected
Session where new co-created services & apps are
publicly presented and stakeholders discover, comment
and provide feedback to enhance them as result of using
them whilst having fun

76
Game Design Session Elements
Game board Challenges
Data resources Users Utilities (BBs)

77
WeLive makes it EASY and FEASIBLE to
CO-CREATE Open Data Applications
Innovators
Data
Publishers
Application
Developers
Building Block
Developers
Platform
Provider
Citizens
NEEDS
CO-CREATION TEAM
easy-to-use
mobile app
SOLUTION

78
Co-Business Model
Innovators
Data
Providers
Application
Developers
Building Block
Developers
Platform
Provider
APPLICATION
SALES
REVENUE
SHARING
IN-APP
ADVERTISING

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Full Co-Creation Lifecycle
Support
co-businessco-maintenanceco-implementationco-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

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70 BBs
452 Datasets
– Bilbao (168),
Helsinki (50), Trento
(201) & Novi Sad (33)
40 Apps –
21 in Google Play & 8
with Service Composer
Exploitable Asset Ecosystem and Open
Source platform produced by WeLive

82
WeLive: Open Government Co-creation
Enabling Infrastructure & methodology
PARTICIPATION
• Goes beyond co-Ideation enabling also
participation in whole co-creation
• Inclusive interfaces: Multilingual interfaces;
Wizard to post new ideas; Drag and drop
interface to enable non-programmers to
assemble simple apps
ACCOUNTABILITY
• Allows tracing journey from NEEDs to IDEAS
into APPs composed of DATASETS and BUILDING
BLOCKS
• Analytics dashboard enables to understand
impact of platform and apps
• Integration with CNS Marketplace enables
artefacts business model
COLLABORATION
• Marketplace to foster public/private
partnerships & CDV component enables to
reuse personal data across apps
• User & programming interfaces for different
stakeholders, i.e. civil servants, citizens, SMEs,
entrepreneurs/developers collaboration
• Novel collaboration techniques to foster co-
creation: Game Design and Gamified User
Evaluations
TRANSPARENCY
• Goes beyond Open Data portals
• User-generated data is also allowed to
complement public open data
• From raw datasets into well-document easy to
consumed micro-services, i.e. BB concept

83
Agenda
1. Introduction:
– Context: societal urbanization and ageing
– Interdependence analysis: Ambient Assisted Cities
– ICT & Social Innovation leading towards Smarter Cities
2. Technologies for enablement of Smarter Cities:
– Internet of Things
– Web of Data
– Crowdsourcing
3. Building Smarter Cities
– Broad Data Analysis Tools
– European projects about Smarter Ambient Assisted Cities
4. Conclusion

84
Conclusion
• We need cooperative cities and territories which are
inclusive, participative, aware to the needs of all societal
sectors
• Technologies are the solution to do more with what we
have,
– without having to invest in big amounts, but taking advantage of all
the information that is already available, transforming knowledge,
democratizing its access and usage, protecting and regulating its
usage, and easing decision making among different actors
• Users must be included, we can only progress towards
Smarter Cities if citizens are truly empowered.

85
Combining ICT and User Participation to give place to Open
Government
21 March 2018, Deusto Business School, Bilbao, Spain
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es

86
References
• Innovating the Smart Cities, Syam Madanapalli | IEEE Smart Tech
Workshop 2015, http://www.slideshare.net/smadanapalli/innovating-the-
smart-cities
• Kitchin, R., Lauriault, T. and McArdle, G. (2015) Knowing and governing
cities through urban indicators, city benchmarking and real-time
dashboards. Regional Studies, Regional Science 2: 1-28,
http://rsa.tandfonline.com/doi/full/10.1080/21681376.2014.983149
• Towards Smart City: Making Government Data Work with Big Data
Analysis, Charles Mok, 24 September 2015,
http://www.slideshare.net/mok/towards-smart-city-making-government-
data-work-with-big-data-analysis-53176591
• Mining in the Middle of the City: The needs of Big Data for Smart Cities, Dr.
Antonio Jara, http://www.slideshare.net/IIG_HES/mining-in-the-middle-
of-the-city-the-needs-of-big-data-for-smart-cities

87
References
• Schema.org - Extending Benefits, Richard Wallis,
http://www.slideshare.net/rjw/schemaorg-extending-
benefits?qid=9ecd1fa6-cfac-460d-9bbb-
065879c46aee&v=&b=&from_search=3
• SDA-SmartCity – Overview and Challenges, Alessandra Mileo.
Senior Research Fellow, Insight Centre for Data Analytics, NUI
Galway, http://ict-citypulse.eu/page/tutorial/smartcity-
tutorial-eswc2015_files/ESWC-SD-INTRO-small.pdf
• Internet of Things: Concepts and Technologies, Payam
Barnaghi,
http://www.slideshare.net/PayamBarnaghi/internet-of-
things-concepts-and-technologies

88
References
• Internet of Things towards Ubiquitous and Mobile Computing
– http://research.microsoft.com/en-
us/UM/redmond/events/asiafacsum2010/presentations/Guihai-
Chen_Oct19.pdf
• 5 key questions to ask about the Internet of Things
– http://www.slideshare.net/DeloitteUS/5-questions-the-iot-internet-of-things
• Internet Connected Objects for Reconfigurable Eco-systems
– https://docbox.etsi.org/workshop/2012/201210_M2MWORKSHOP/zz_POSTE
RS/iCore.pdf
• Internet of Things and Big Data – Bosch, August 2015
– https://www.bosch-
si.com/media/bosch_software_innovations/media_landingpages/connectedw
orld_1/bcw_2016/bcw_1/download_page_1/download_page/bcw16_mongo
db_collateral_followup_sponsor.pdf
• The internet of things and big data: Unlocking the power
– http://www.zdnet.com/article/the-internet-of-things-and-big-data-unlocking-
the-power/

89
References
• Deconstructing the Internet of Things
– https://jenson.org/deconstructing-the-iot/
• Mobile in IoT Context ? Mobile Applications in "Industry 4.0“
– http://www.slideshare.net/MobileTrendsConference/karol-kalisz-vitaliy-rudnytskiy-
mobile-in-iot-context-mobile-applications-in-industry-40
• Inside the Internet of Things (IoT) – A primer on the technologies building
the IoT – Deloitte
– http://dupress.com/articles/iot-primer-iot-technologies-applications/
• Internet of Things (IoT) - We Are at the Tip of An Iceberg – Dr. Mazlan
Abbas
– http://www.slideshare.net/mazlan1/internet-of-things-iot-we-are-at-the-tip-of-an-
iceberg
• Infographic: What are Beacons and What Do They Do?
– https://kontakt.io/blog/infographic-beacons/
• iBeacon
– https://en.wikipedia.org/wiki/IBeacon

90
References
• ITU News – What is a smart sustainable city?,
– https://itunews.itu.int/en/5215-What-is-a-smart-sustainable-
city.note.aspx
• Frost & Sullivan's Predictions for the Global Energy and
Environment Market,
– http://www.slideshare.net/FrostandSullivan/frost-sullivans-
predictions-for-the-global-energy-and-environment-market
• Fog Computing with VORTEX
– http://www.slideshare.net/Angelo.Corsaro/20141210-fog
• What Exactly Is The "Internet of Things"? – A graphic primer
behind the term & technologies
– http://postscapes.com/what-exactly-is-the-internet-of-things-
infographic

91
References
• The Big 'Big Data' Question: Hadoop or Spark?
– http://www.datasciencecentral.com/profiles/blogs/the-big-big-data-question-
hadoop-or-spark
• Hadoop vs. Spark: The New Age of Big Data
– http://www.datamation.com/data-center/hadoop-vs.-spark-the-new-age-of-
big-data.html
• Comparing 11 IoT Development Platforms
– https://dzone.com/articles/iot-software-platform-comparison
• 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
• Virtualisation and Validation of Smart City Data. Dr Sefki Kolozali. Dr
Payam Barnaghi

Combining ICT and User Participation to Enable Smarter Cities

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