6 Heat Vulnerability Index Development and Application in Medan City, Indonesia
89
computation (Monazzam et al. 2014). The most recent one is proposed by Blazejczyk
et al. (2012), i.e., the Universal Thermal Climate Index (UTCI), a non-occupational
heat stress index, which combines all the key climate factors into a single number. It is
designed to evaluate the outdoor thermal environment in the context of public health,
in reference to human thermo-physiology studies (Golbabaei et al. 2013). However,
there are still many factors that have not yet counted in the heat stress indices, such
as population characteristic and physical properties, which also affect heat stress
level (Kjellstron et al. 2016). For this purpose, we developed Heat Vulnerability
Index (HVI) which covered heat stress index, physical properties, and population
characteristics. The HVI should be assessed by the range of determinants of heatrelated health effects and through studying epidemiologic literature and consulting
public health experts (Bao et al. 2015).
In urban areas, HVI calculation needs to consider more comprehensively the local
climate variability particularly in relation to higher heat absorption of certain land use
types (e.g., vegetation and building coverage), surface properties (e.g., emissivity,
land surface temperature), and decreasing ventilation geometry of the urban area (Oke
1987; Stabler et al. 2005). These elements are responsible for micro-climate alternations in urban areas known as the Urban Heat Island (UHI). Previous studies clearly
showed that alternation of green spaces into build areas already drastically heat up
the environment and influences human health in urban areas (Kilbourne et al. 1982;
Tan et al. 2007). Additional anthropogenic heat release, particularly from increasing
energy consumption (i.e., electricity, gas, and oil) (Smith et al. 2009), contributes
to temperature increase. Furthermore, population characteristic such as age, poverty
level, health condition, and population density also affect heat-related illness (Kjellstron et al. 2016). Alongside the higher number of covariates needed to be included
in HVI estimation that brings a challenge of reducing larger set of variables into a
smaller one. Such methodological concern in heat vulnerability studies can be solved
through a Principal Component Analysis (PCA) method (Bao et al. 2015). The PCA
is a technique that allows an increase of the accuracy and objectively in analyzing
large multivariate datasets and to reduce their dimensionality (Jolliffe 2014).
6.3 Study Objectives
A number of studies and approaches have been undertaken globally to evaluate heatrelated risks. The vulnerability indices have been developed in the US (Reid et al.
2009), Europe (Wolf and McGregor 2013), Canada (Jay and Kenny 2010), Australia
(Coutts et al. 2007), and China (Bao et al. 2015). The aforementioned studies
were mainly conducted in the developed and mid-latitudes countries. However, the
city characteristic and climate pattern (i.e., inter-annual temperature variability is
low) in Indonesia are different from the aforementioned studies. Thus the specific
HVI is necessary to be developed. Nevertheless, until the present time, there are
no specific vulnerability indices developed specifically for Indonesia, despite, as
described earlier, highly prone heat-related risks. Moreover, in the face of urban
89
computation (Monazzam et al. 2014). The most recent one is proposed by Blazejczyk
et al. (2012), i.e., the Universal Thermal Climate Index (UTCI), a non-occupational
heat stress index, which combines all the key climate factors into a single number. It is
designed to evaluate the outdoor thermal environment in the context of public health,
in reference to human thermo-physiology studies (Golbabaei et al. 2013). However,
there are still many factors that have not yet counted in the heat stress indices, such
as population characteristic and physical properties, which also affect heat stress
level (Kjellstron et al. 2016). For this purpose, we developed Heat Vulnerability
Index (HVI) which covered heat stress index, physical properties, and population
characteristics. The HVI should be assessed by the range of determinants of heatrelated health effects and through studying epidemiologic literature and consulting
public health experts (Bao et al. 2015).
In urban areas, HVI calculation needs to consider more comprehensively the local
climate variability particularly in relation to higher heat absorption of certain land use
types (e.g., vegetation and building coverage), surface properties (e.g., emissivity,
land surface temperature), and decreasing ventilation geometry of the urban area (Oke
1987; Stabler et al. 2005). These elements are responsible for micro-climate alternations in urban areas known as the Urban Heat Island (UHI). Previous studies clearly
showed that alternation of green spaces into build areas already drastically heat up
the environment and influences human health in urban areas (Kilbourne et al. 1982;
Tan et al. 2007). Additional anthropogenic heat release, particularly from increasing
energy consumption (i.e., electricity, gas, and oil) (Smith et al. 2009), contributes
to temperature increase. Furthermore, population characteristic such as age, poverty
level, health condition, and population density also affect heat-related illness (Kjellstron et al. 2016). Alongside the higher number of covariates needed to be included
in HVI estimation that brings a challenge of reducing larger set of variables into a
smaller one. Such methodological concern in heat vulnerability studies can be solved
through a Principal Component Analysis (PCA) method (Bao et al. 2015). The PCA
is a technique that allows an increase of the accuracy and objectively in analyzing
large multivariate datasets and to reduce their dimensionality (Jolliffe 2014).
6.3 Study Objectives
A number of studies and approaches have been undertaken globally to evaluate heatrelated risks. The vulnerability indices have been developed in the US (Reid et al.
2009), Europe (Wolf and McGregor 2013), Canada (Jay and Kenny 2010), Australia
(Coutts et al. 2007), and China (Bao et al. 2015). The aforementioned studies
were mainly conducted in the developed and mid-latitudes countries. However, the
city characteristic and climate pattern (i.e., inter-annual temperature variability is
low) in Indonesia are different from the aforementioned studies. Thus the specific
HVI is necessary to be developed. Nevertheless, until the present time, there are
no specific vulnerability indices developed specifically for Indonesia, despite, as
described earlier, highly prone heat-related risks. Moreover, in the face of urban