Anthropogenic pressure data
We have collected all relevant data to ensure the application of
this index. Selective sorting based on the most reliable and
recent data has been carried out in order to guarantee the good
quality of the results. Finally, a tabular database has been
established by indexing this information.
The basic data were taken from the study reports provided
by the National Coastal Commission (CNL), the Ministry of
the Environment and Renewable Energies (MEER), and the
Ministry of Interior, Local Authorities and Spatial Planning
(MICLAT). The main documents are as follows: State of
coastal fishing in the study area (2018); Master Plan for the
Development and Urban Planning of Algiers PDAU (2016);
Land Use Plan POS - Algiers - (2016); study of the Maritime
Public Domain Delimitation of Algiers DDPM (2014);
General Population and Housing Census RGPH (2011);
Boundaries of Algiers coastal domain (2007); Census of discharge points - Algiers (2007); Coastal Development
Programme PAC (2005); Coastal Cadastre of Algiers (2004).
To measure the variables of regulatory protection index, we initiated a survey of workers in government
organizations. The survey assessed the levels of legislative protection and the application of this protection to
monitor the criteria of environmental quality that
Mauvais (1997) identified: Water quality, state of living
resource, state of non-living resource and state of landscape, and the application of the measures on the field.
A total of 10 government organizations participated in
the survey: five employees from each organization. The
questionnaire is composed of 10 questions about the
level of environmental protection in the Algerian legislation and its application in the field.
Normalization, weighting, and valued function
In literature, the term “standardization” refers to the
transformation of indicator values measured on different
scales and units into values without a unit on a common
scale (Fritzsche et al. 2017). The data needed to develop a vulnerability index are expressed in qualitative and
quantitative terms and are frequently available at different scales and are expressed in different units of measurement. It is therefore common to assign a rank to
each variable to indicate its contribution to vulnerability
(McLaughlin et al. 2010). The weighting of the different
variables relative to each other was not performed due
to the varying number of value judgments hidden behind the combined weights (McLaughlin et al. 2010).
In this study, a semi-quantitative assessment using scores
to evaluate the sensitivity of the environment and the intensity
of anthropogenic pressures was carried out.
In order to assess environmental sensitivity, factors were
ranked in order of sensitivity, from most sensitive to least
sensitive, on a scale of 1 to 5, where 1 represents the most
sensitive level for each factor and 5 represents the least
sensitive.
For intensity of anthropogenic pressures, a ranking is
established according to the intensity of the pressures
exerted by the different levels of each factor on the
territory. A scale from 1 to 4 was chosen, with 1 contributing the most to the intensity of pressure from each
factor and 4 contributing the least. Tables 1 and 2 illustrate the rules used to classify the factors constituting
the two vulnerability sub-indices.
In order to make the indicator maps comparable, the values
of the sub-indices are normalized according to the following
formula:
Dimension index DI
ð Þ
¼
Sum of variable scores−Minimum possible scores
Sum max of the scores−Minimum possible scores
This equation converts the original indicator values (each
expressed in its own unit of measurement) to dimensionless
scores based on a range of 0–1 (McLaughlin et al. 2010;
Aretano et al. 2014; Yoo et al. 2014).
To determine the values of ISEEL and IPA, we have
chosen to adjust the values of their sub-indices on a
scale from 0 to 5 on the basis of the DI*5 formula,
which will give the following sub-indices and index
formulas:
For ISEEL sub−indexes : Sub−index
¼ Mean Factors
ð
Þ−1
ð
Þ *1:25:
The final ISEEL is calculated by averaging the four
values of the sub-indices, as shown in the following
formula: ISEEL = (Sb + Sg + Ch + Ip)/4
For the IPA sub−indexes : Sub−index
¼ Mean Factors
ð
Þ−1
ð
Þ *5=3
The IPA and ISEEL indices are calculated by averaging the
values of the sub-indices, as shown in the following formula:
Index ¼
Mean Sub−index
ð
Þ
Number of sub−indices
Using fifths (0–20%, 20–40%, 40–60%, 60–80%, and 80–
100%) and characterizations of very low - low - medium -
high - very high, each calculated value falls within the relevant
fifth then the coastal region is characterized accordingly
(Doukakis 2005). The result of this work will be mapped as
follows:
42675
Environ Sci Pollut Res (2020) 27:42670–42684
Author's personal copy
We have collected all relevant data to ensure the application of
this index. Selective sorting based on the most reliable and
recent data has been carried out in order to guarantee the good
quality of the results. Finally, a tabular database has been
established by indexing this information.
The basic data were taken from the study reports provided
by the National Coastal Commission (CNL), the Ministry of
the Environment and Renewable Energies (MEER), and the
Ministry of Interior, Local Authorities and Spatial Planning
(MICLAT). The main documents are as follows: State of
coastal fishing in the study area (2018); Master Plan for the
Development and Urban Planning of Algiers PDAU (2016);
Land Use Plan POS - Algiers - (2016); study of the Maritime
Public Domain Delimitation of Algiers DDPM (2014);
General Population and Housing Census RGPH (2011);
Boundaries of Algiers coastal domain (2007); Census of discharge points - Algiers (2007); Coastal Development
Programme PAC (2005); Coastal Cadastre of Algiers (2004).
To measure the variables of regulatory protection index, we initiated a survey of workers in government
organizations. The survey assessed the levels of legislative protection and the application of this protection to
monitor the criteria of environmental quality that
Mauvais (1997) identified: Water quality, state of living
resource, state of non-living resource and state of landscape, and the application of the measures on the field.
A total of 10 government organizations participated in
the survey: five employees from each organization. The
questionnaire is composed of 10 questions about the
level of environmental protection in the Algerian legislation and its application in the field.
Normalization, weighting, and valued function
In literature, the term “standardization” refers to the
transformation of indicator values measured on different
scales and units into values without a unit on a common
scale (Fritzsche et al. 2017). The data needed to develop a vulnerability index are expressed in qualitative and
quantitative terms and are frequently available at different scales and are expressed in different units of measurement. It is therefore common to assign a rank to
each variable to indicate its contribution to vulnerability
(McLaughlin et al. 2010). The weighting of the different
variables relative to each other was not performed due
to the varying number of value judgments hidden behind the combined weights (McLaughlin et al. 2010).
In this study, a semi-quantitative assessment using scores
to evaluate the sensitivity of the environment and the intensity
of anthropogenic pressures was carried out.
In order to assess environmental sensitivity, factors were
ranked in order of sensitivity, from most sensitive to least
sensitive, on a scale of 1 to 5, where 1 represents the most
sensitive level for each factor and 5 represents the least
sensitive.
For intensity of anthropogenic pressures, a ranking is
established according to the intensity of the pressures
exerted by the different levels of each factor on the
territory. A scale from 1 to 4 was chosen, with 1 contributing the most to the intensity of pressure from each
factor and 4 contributing the least. Tables 1 and 2 illustrate the rules used to classify the factors constituting
the two vulnerability sub-indices.
In order to make the indicator maps comparable, the values
of the sub-indices are normalized according to the following
formula:
Dimension index DI
ð Þ
¼
Sum of variable scores−Minimum possible scores
Sum max of the scores−Minimum possible scores
This equation converts the original indicator values (each
expressed in its own unit of measurement) to dimensionless
scores based on a range of 0–1 (McLaughlin et al. 2010;
Aretano et al. 2014; Yoo et al. 2014).
To determine the values of ISEEL and IPA, we have
chosen to adjust the values of their sub-indices on a
scale from 0 to 5 on the basis of the DI*5 formula,
which will give the following sub-indices and index
formulas:
For ISEEL sub−indexes : Sub−index
¼ Mean Factors
ð
Þ−1
ð
Þ *1:25:
The final ISEEL is calculated by averaging the four
values of the sub-indices, as shown in the following
formula: ISEEL = (Sb + Sg + Ch + Ip)/4
For the IPA sub−indexes : Sub−index
¼ Mean Factors
ð
Þ−1
ð
Þ *5=3
The IPA and ISEEL indices are calculated by averaging the
values of the sub-indices, as shown in the following formula:
Index ¼
Mean Sub−index
ð
Þ
Number of sub−indices
Using fifths (0–20%, 20–40%, 40–60%, 60–80%, and 80–
100%) and characterizations of very low - low - medium -
high - very high, each calculated value falls within the relevant
fifth then the coastal region is characterized accordingly
(Doukakis 2005). The result of this work will be mapped as
follows:
42675
Environ Sci Pollut Res (2020) 27:42670–42684
Author's personal copy