dimensionality [7]. There are other contending approaches, like the monitoring and
analysis of individual well-being or behavioural domain indicators or determinants
independently in parallel, without hierarchical structuring and substantial synthesis into
fewer higher-level composite factors or score(s) [8]. Most, including the adopted and
followed approach works (like [9]), are comprehensively covered or referenced in the
Encyclopedia of Quality of Life and Well-Being Research (Springer 2014) that summarizes recent research works related to well-being and quality of life in spanned
various research and policy-making/implementation fields. Main advantages of a few
composite synthetic indicators/factors over a battery of multiple separate indicators,
namely:
• ability to summarize complex and multi-dimensional real-life phenomena or
domains (like well-being),
• easier for interpretation and comparison among (socio-demographic,
geospatial/regional…) groups or population clusters,
• more effective for comprehending overall trends, particularly when a number of the
underlying indicators denote opposing-trend changes,
are of crucial significance in usage and context settings of the PULSE project, with
over 60 indicators formalized in the initial knowledge-based well-being model topology from the systematization of collected data, and with
• visible set of indicators to various stakeholders (policy makers, researchers, general
public) needing to be minimal without omitting important underlying information,
• and collaboration, communication and comparison of complex dimensions by
various stakeholders needing to be most straightforward, facilitated and effective.
We therefore propose two complementary approaches for synthesis of the composite well-being indicators composed from underlying streamed IoT-sourced timeseries data in the context of PULSE. The indicators summarize multi-dimensional
aspects of citizen well-being and enable the assessment of individual and synthesized
collective urban well-being over time. The notion is illustrated through analysis within
the scope of four representative and characteristical key summary indicators of citizen
health and fitness, derived from activity and vital/health parameters measured, as stated, using wearable sensing devices: motility, physical activity, sleep quality and
cardio-respiratory health/fitness (Fig. 1).
In the first approach, daily and intra-daily underlying measurements (Table 1) are
used to estimate levels of adherence to rule- and range-based recommendations
matured from institutional knowledge of relevant authorities and population-significant
studies in the field, accumulated for over decades in the stated four example domains of
motility, physical activity, sleep quality and cardio-respiratory fitness [8, 10, 11].
The complementary data-driven statistical approach is predicated on standard
scores that denote the number of standard deviations that a given measurement deviates
from the sample mean. This approach allows comparison of individual scores to the
corresponding norm groups stratified by common socio-demographic parameters (age,
gender…), when considered conditionally independent nodes in the complete model
topology. It also allows to place a score for any individual and variable with respect to
alternative descriptive statistic or measure of central tendency (variable median,
Baseline Modelling and Composite Representation
157
analysis of individual well-being or behavioural domain indicators or determinants
independently in parallel, without hierarchical structuring and substantial synthesis into
fewer higher-level composite factors or score(s) [8]. Most, including the adopted and
followed approach works (like [9]), are comprehensively covered or referenced in the
Encyclopedia of Quality of Life and Well-Being Research (Springer 2014) that summarizes recent research works related to well-being and quality of life in spanned
various research and policy-making/implementation fields. Main advantages of a few
composite synthetic indicators/factors over a battery of multiple separate indicators,
namely:
• ability to summarize complex and multi-dimensional real-life phenomena or
domains (like well-being),
• easier for interpretation and comparison among (socio-demographic,
geospatial/regional…) groups or population clusters,
• more effective for comprehending overall trends, particularly when a number of the
underlying indicators denote opposing-trend changes,
are of crucial significance in usage and context settings of the PULSE project, with
over 60 indicators formalized in the initial knowledge-based well-being model topology from the systematization of collected data, and with
• visible set of indicators to various stakeholders (policy makers, researchers, general
public) needing to be minimal without omitting important underlying information,
• and collaboration, communication and comparison of complex dimensions by
various stakeholders needing to be most straightforward, facilitated and effective.
We therefore propose two complementary approaches for synthesis of the composite well-being indicators composed from underlying streamed IoT-sourced timeseries data in the context of PULSE. The indicators summarize multi-dimensional
aspects of citizen well-being and enable the assessment of individual and synthesized
collective urban well-being over time. The notion is illustrated through analysis within
the scope of four representative and characteristical key summary indicators of citizen
health and fitness, derived from activity and vital/health parameters measured, as stated, using wearable sensing devices: motility, physical activity, sleep quality and
cardio-respiratory health/fitness (Fig. 1).
In the first approach, daily and intra-daily underlying measurements (Table 1) are
used to estimate levels of adherence to rule- and range-based recommendations
matured from institutional knowledge of relevant authorities and population-significant
studies in the field, accumulated for over decades in the stated four example domains of
motility, physical activity, sleep quality and cardio-respiratory fitness [8, 10, 11].
The complementary data-driven statistical approach is predicated on standard
scores that denote the number of standard deviations that a given measurement deviates
from the sample mean. This approach allows comparison of individual scores to the
corresponding norm groups stratified by common socio-demographic parameters (age,
gender…), when considered conditionally independent nodes in the complete model
topology. It also allows to place a score for any individual and variable with respect to
alternative descriptive statistic or measure of central tendency (variable median,
Baseline Modelling and Composite Representation
157