Big Data in a Digital City. Key Insights from the Smart City Case Study

Big Data in a Digital CityKey Insights from the Smart City Case Study

Sonja Zillner , Sebnem Rusitschka Siemens AG

@BYTE_EU www.byte-project.eu

OverviewFocus, Approach and Results

• Macro-view of the creation of value from potentially massive amounts of urban data • that emerges through the digitalized interaction of a city’s users, • i.e. of citizens and businesses, with the urban infrastructure of resources such

as energy and transportation, social and administrative services, etc.

Focus of the Case Study

1. Desktop Research2. Focus group consisting of stakeholders from the “city,” “mobility” industry, “energy” industry,

city “technology solution providers”, as well as EU BYTE researchers3. Interviews complemented the focus group, and enabled us to clear follow-up questions or

collect alternative and/or more detailed perspectives. The interviewees were selected based on their field of work or study and their seniority.

Our Approach and Methodology

• General overview on the current state of big data in smart cities by examining data sources, data uses and flow as well as discussing technical challenges

• Identifying positive and negative societal externalities

Results


Data Sources

1. Mobility Data (violet)

2. Energy Data (green)

3. Environmental / Geo Data (orange)

4. Operational and Process data (blue)

• “Traditionally, like many other sectors, cities haven been managing only the necessary data – not all the data”

• Digitalisation is seen as main driver for the creation of big data sources

• A digitalized city encompasses a myriad of existing and potential new data sources, such as sensors

Various Origins of Data

More & More Data gets collected


Data Usage is facing technical challenges

12= de Montjoye et al. (2013): "Unique in the Crowd: The privacy bounds of human mobility".

• The way how data is being collected and stored massively influences how easily it can shared

• The majority of data generated remains in silos or old storage technologies or is not collected at high-resolution

Existing means of data collection and storage hinder the seamless exchange

1

• Data needs to be anonymized sufficiently • Data Privacy is seen as challenging task:

“Only four spatio-temporal points, approximate places and times, are enough to uniquely identify 95% of 1.5M people in a mobility database. The study further states that these constraints hold even when the resolution of the dataset is low, mobility datasets and metadata circumvent anonymity.”12

Privacy and security concerns need to be addressed2


Understanding the Data Flowallows to identify critical driver for smart city ecosystem

• to overcome established data silos• establishing the basis for the intermediation between its users, the citizen and its partners, the

resources

Cities require means for data sharing and exchange (e.g. data platform)

• Investment Dilemma: who pays? who operates?• Willingness to share data: who participates?

Challenge: How to kick start?

• All stakeholder groups highlighted that they could immediately benefit from integrated mobility data

• All stakeholder indicated their willingness to share mobility data if this can ensures that the overall traffic problems can be improved

• Given the situation that mobility becomes increasingly electrified, energy data will follow to become a data source of high relevance for this ecosystem

• However, for all these data flow scenarios modern platform techniques are required

Critical Driver for smart city ecosystem: Integrating mobility data


Economic externalities (excerpt)

• Investment dilemma in digital cities• high ROI is not possible by scaling• A single city represent s a rather

limited market opportunity

• As basis for data sharing across stakeholder, common platforms are needed• city’s complexity makes the kick-

start of a platform initiative difficult

Key findings

• Open source and open platforms are seen as promising for future data sharing

• Investments by the public sector into the data infrastructure and the subsequent opening of this infrastructure as a utility / commodity

Recommendation


Social and ethical externalities (excerpt)

• Immense potential of big data for social goods

• Privacy and Security concerns need to be addressed

• A debate on how to assure „enough“ equality as not all citizen will reap value from data in equal amounts is required

• The society in general needs to address the questions: What will men do when machines learn simple tasks?

Key findings


Legal externalities (excerpt)

• New sources of data create new ways that data can be misused.

• The legal framework needs an update with the core principal of putting the individual first

• The penalties of data misuse needs to be so high that they will prevent misuse

Key findings


Political externalities (excerpt)

• Big data business can weaken European economy, if big data monopoly of companies like Google, Amazon, etc. remains.

• Harmonization of legal framework across the European market is required for scaling up big data businesses

Key findings


Thank you for your attention!Any Questions?

Dr. Sonja ZillnerEmail: [email protected]


Focus Group of the Smart City Case StudySmart City Stakeholders Company Position Relevant Topics

Jaakko Salavuo City of Helsinki Head of Comms & IT Information & Communication Technologies in the CityBart Rosseau (repr. Gino Vertriest)

City of Ghent E-Strategy Open Data

Dave Carter University of Manchester Honorary Research Fellow Chair, European Connected Smart Cities Network | Centre

for Urban Policy Studies

André Dias CEIIA Portugal Head of Intelligent Systems Advanced smart mobility concepts: the mobi.me cloud platform; S3C, an infrastructure for monitoring smart cities.

Lean Doody (repr. Nicola Walt)

Arup Associate Director of Consulting Smart City Best Practices, Technology Solutions

Jean Dulac IEA Energy Analyst from Energy Demand Technology Unit

Urban planning and development policy, with an emphasis on transport and building technologies.

Peter Bjørn Larsen CLEAN Smart City Manager Smart City Hub

Ajit Joakar Future Text Innovation - Digital convergence – Mobile Web 2.0 IoT, Machine Learning, Cities

Francisco Rincón Siemens Corporate Development

Sustainable Cities Smart City Best Practices, Technology Solutions

Benjami Kott EnergyDeck CEO Energy platforms for tracking & benchmarking at community level

Prof. Hans Uszkoreit DFKI Chair Artificial Intelligence „A Legal Framework for the Virtual World”

Jamie Cudden City of Dublin Smart City Coordinator Dublink:d Big Data Open Data Platform

Sonja Zillner Siemens Senior Key Expert Semantics Big Data Value Association, cPPP

Mihai Sercaianu Make Better GIS Analyst Data availability, open data and citizen engagement


Smart City Focus Group Workshop


Cartography of Big Data Value in Digital CitiesThrough group discussions and break-out sessions with interdisciplinary experts as a focus group


Workshop Analysis PreviewEnd user need of time- & resource-efficient mobility may well be the spark that brings all other relevant data and stakeholders together


Interview Partner

Organization Industry sector

Technology adoption stage

Position on data value chain

Impact of IT in industry

European City Public Sector Early majority

AcquisitionAnalysisCurationStorageUsage

Strategic mode

Technology Provider

Start-up,Energy

Early adopter AcquisitionAnalysisStorageUsage

Turnaround mode

Technology Provider

Non-profit,Mobility

Early majority AcquisitionAnalysisStorageUsage

Turnaround mode

Technology Provider & Research

Multinational,Smart City

Early majority AcquisitionAnalysisStorageUsage

Strategic mode