3.2 A Data Centric Approach for Urban Level Complex
Assessments
Just like the accuracy and precision of an LCA study being highly dependent on
data quality, urban metabolism models also rely on obtaining and generating
realistic data as pointed earlier. This leads the practitioner to a data centric approach
for urban level complex assessments which can be based on a trend that builds upon
business intelligence towards a smart city approach operating via large quantities of
complex data.
In their comprehensive literature review study, Beloin-Saint-Pierre et al. have
categorised the urban metabolism modelling strategies into Black Box, Grey
Box and Network approaches. Starting from the basic simplified input and output
data of the Black Box Approach, the Grey-Box Approach studies further level of
inner flows and the Network Approach defines the links on all inner components of
the studied system. The main drawback of the Network Approach is identified to be
the challenging implementation of analyses due to the big amount of data. This fact
can be easily accepted as a reason of complexity yet still being the solution towards
answering multi-dimensional analyses that can be very beneficial for urban level
decision making [19].
The handling of such complex data can be studied through the business intelligence approach, which has roots in the database management field and benefits
from various data collection, extraction, and analysis technologies [20].
Development in data analytics technologies has led to the Internet as a platform and
eventually cloud-based data hosting and analyses capabilities. Business intelligence
has evolved because the amount of data generated through the internet and smart
devices has grown exponentially altering how organizations and individuals use
information [21]. Current technological advancements in the data analytics fields
and accessibility of complex data analytics algorithms within the cloud services
offer a promising and reliable platform to realize data centric assessments.
The age of “big data” has also led to the transparency, accessibility and interoperability of data repositories. EU has initiated and mobilised the INSPIRE directive
[22], identifying standards for data sets, and their distributed services. Urban level
data management and referencing standards have also been emerging. One of these
standards is the BSI Guide to establishing a model for data interoperability, which
aims to look beyond the current use of data to facilitate city services, and encourage
decision-makers to explore the reuse of data as a resource to innovate the future
direction of systems and services. This standard identifies four key types of insight to
be required when sharing data in a city; operational insight examining the characteristics of urban elements, critical insight for real time monitoring, analytical insight
for exploring the data ecosystem to determine patterns, and strategic insight for
examining outcomes related to strategic objectives [23].
298
D. Başoğlu et al.
Assessments
Just like the accuracy and precision of an LCA study being highly dependent on
data quality, urban metabolism models also rely on obtaining and generating
realistic data as pointed earlier. This leads the practitioner to a data centric approach
for urban level complex assessments which can be based on a trend that builds upon
business intelligence towards a smart city approach operating via large quantities of
complex data.
In their comprehensive literature review study, Beloin-Saint-Pierre et al. have
categorised the urban metabolism modelling strategies into Black Box, Grey
Box and Network approaches. Starting from the basic simplified input and output
data of the Black Box Approach, the Grey-Box Approach studies further level of
inner flows and the Network Approach defines the links on all inner components of
the studied system. The main drawback of the Network Approach is identified to be
the challenging implementation of analyses due to the big amount of data. This fact
can be easily accepted as a reason of complexity yet still being the solution towards
answering multi-dimensional analyses that can be very beneficial for urban level
decision making [19].
The handling of such complex data can be studied through the business intelligence approach, which has roots in the database management field and benefits
from various data collection, extraction, and analysis technologies [20].
Development in data analytics technologies has led to the Internet as a platform and
eventually cloud-based data hosting and analyses capabilities. Business intelligence
has evolved because the amount of data generated through the internet and smart
devices has grown exponentially altering how organizations and individuals use
information [21]. Current technological advancements in the data analytics fields
and accessibility of complex data analytics algorithms within the cloud services
offer a promising and reliable platform to realize data centric assessments.
The age of “big data” has also led to the transparency, accessibility and interoperability of data repositories. EU has initiated and mobilised the INSPIRE directive
[22], identifying standards for data sets, and their distributed services. Urban level
data management and referencing standards have also been emerging. One of these
standards is the BSI Guide to establishing a model for data interoperability, which
aims to look beyond the current use of data to facilitate city services, and encourage
decision-makers to explore the reuse of data as a resource to innovate the future
direction of systems and services. This standard identifies four key types of insight to
be required when sharing data in a city; operational insight examining the characteristics of urban elements, critical insight for real time monitoring, analytical insight
for exploring the data ecosystem to determine patterns, and strategic insight for
examining outcomes related to strategic objectives [23].
298
D. Başoğlu et al.