Instrumentation & Measurement Magazine 24-7 - 39

The Use of a Data-Driven
Digital Twin of a Smart City:
A Case Study of Ålesund, Norway
Pierre Major, Guoyuan Li, Hans Petter Hildre, and Houxiang Zhang
O
ne objective of smart cities is to produce smart decisions
that will address sustainability and climate
change through big data and citizen engagement.
However, to achieve this, local governments must address two
barriers to citizen engagement: concerns about data privacy
and the difficulty big data poses to nonexperts. Big data by its
nature produces a wide range of varying insights; however, it
is difficult for nonexperts to understand the situations these
insights describe, much less determine, and communicate priorities
and solutions.
We propose that high-quality, 3D graphical digital twins
(GDTs) of cities can be used to create 4D visualizations of
geolocalized time series to address the barriers to citizen engagement.
Such digital twins are increasingly available and
affordable. The method proposed here was applied in a city
council meeting in Ålesund, Norway. It uses off-the-shelf hardware
and a game-engine to create immersive environments
that convey multivariate data in a data-agnostic manner. It
combines geographical information system (GIS) data, descriptive
and predictive demographics, and internet of things
(IoT) data from traffic telemetry, meteorology, telecommunication,
and power. We demonstrate the benefits of the approach
in multiple case studies related to the impact of Covid-19 in cities.
The main contribution of this paper is the presentation of
a novel, smart city GDT framework. It is lightweight for the
IT-infrastructure; scalable in geographical size; transferable
to other locations; versatile about data sources; privacy compliant;
presents high-quality and reliable data; and leverages
current advances in metrology.
Motivation for Data-Driven Digital Twins
of Smart Cities
Cities are currently facing major challenges: accelerating population
concentration, increasing traffic congestion and air
pollution, and climate change. Urbanization is underway
worldwide; for example, current trends indicate that 70% of
the European population was urban in 2018 and 80% will be
by 2050. According to the World Health Organization, 4 to 8
million people die prematurely because of air pollution every
year. Two thirds of the world's biggest cities are coastal and
will be impacted by rising sea level and more frequent extreme
weather. Cities are also complex systems of systems, and therefore
the problems they face are systemic and cannot be solved
solely by optimizing or upgrading the existing cyber-physical
infrastructure.
Addressing the short-term and long-term issues cities
face will require high-quality data-and effective use of such
data. While big data offer the kind of comprehensive picture
effective decision-making requires, it poses considerable challenges,
such as the following:
◗ Difficulty with accessing the data and identifying which
data are pertinent to a particular issue.
◗ Difficulty presenting data such that nonexperts can
understand it.
◗ Finding a balance between high spatial resolution data
and data privacy to preserve citizens' trust in local
authorities which take general data protection regulation
(GDPR) very seriously.
◗ Ensuring that data are reliable and accurate.
Researchers have drawn attention to the challenges affecting
the use of big data by local governments. For example, [1]
calls for a smarter exploitation of useful data and critical interpretation
of relevant measurements and assessment of their
uncertainty. The author of [2] lays out concerns related to privacy
and accountability. Using a GDT, the authors in [3] resort
to a semantic approach and data aggregation as a means of reducing
data storage and transport, but do not address privacy.
By contrast, [4] proposes a geo-encryption approach to IoT
sensor data stored on cloud servers as a means to address security,
privacy, and cross-border legal issues. We argue in this
paper that anonymization by spatially aggregating data can
create GDPR-compliant data to address these issues.
This work was supported by Offshore Simulator Centre, the Norwegian Research Council (NRC) " Data-driven ship models for
rapid virtual prototyping, " industrial Ph.D. under Grant Number 285949, and NRC " Smart Plan " Grant Number 310056.
October 2021
IEEE Instrumentation & Measurement Magazine
1094-6969/21/$25.00©2021IEEE
39

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