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edible_Cities_AHP_Survey.tex
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% Document title
\title{\textbf{\textsf{Introducing a procedure to perform the Analytic Hierarchy Process with own survey data obtained from \emph{SoSci Survey} platform using R-package \emph{ahpsurvey}}}}\author[1]{Bj\"orn Kasper (\href{mailto:kasper.bjoern@bgetem.de}{kasper.bjoern@bgetem.de})}
\affil[1]{Test and Certification Body for Electrical Engineering at BG ETEM}\author[2]{Henriette John (\href{mailto:h.john@ioer.de}{h.john@ioer.de})}
\affil[2]{Leibniz Institute of Ecological Urban and Regional Development}\date{\today; version 0.5 (pre-release)}
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breaklinks=true, % so long urls are correctly broken across lines
bookmarksnumbered=true,
pdfauthor=Bj\"orn Kasper,
pdftitle=Introducing a procedure to perform the Analytic Hierarchy Process with own survey data obtained from \emph{SoSci Survey} platform using R-package \emph{ahpsurvey},
colorlinks=true,
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\begin{document}
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\maketitle\thispagestyle{empty}
\begin{center}
\includegraphics[width=0.9\textwidth]{images/Cover_image.pdf}
\end{center}
\vfill
\begin{abstract}
Anyone who wants to seriously deal with the emerging topic of our time ``Artificial Intelligence (AI)'' cannot avoid dealing with the basic mathematical models and algorithms from the field of ``Machine Learning (ML)'' as a subset of AI. However, someone who opens the door for the first time to this equally very exciting as well as arbitrarily complex and, at first glance, confusing world will very quickly be overwhelmed. Here, it is a good idea to consult introductory and systematic tutorials. Therefore, this Getting Started tutorial systematically demonstrates the typical ML work process step-by-step using the very powerful and performant ``Support Vector Classifier (SVC)'' and the widely known and exceptionally beginner-friendly ``Iris Dataset''. Furthermore, the selection of the ``correct'' SVC kernel and its parameters are described and their effects on the classification result are shown.
\end{abstract}
\vfill\noindent
\begin{center}
\begin{tabular}{>{\centering}m{0.2\textwidth}m{0.65\textwidth}}
\begin{minipage}{\linewidth}
\includegraphics{images/CC_BY-SA_40.png}
\end{minipage}
&
\begin{minipage}{\linewidth}
This work is licensed under a \href{https://creativecommons.org/licenses/by-sa/4.0/}{Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)}.
\end{minipage}
\end{tabular}
\end{center}
\newpage
% Activate own page style
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\tableofcontents
\hypertarget{introduction}{%
\section{Introduction}\label{introduction}}
The \textbf{Analytic Hierarchy Process (AHP)} is a common and now widely
used method to decide on an alternative based on several different
criteria (see \cite{Wikipedia_AHP}). Often the weighting of the
respective criteria is done by a small number of decision makers or even
a single decision maker. The AHP is then relatively easy to implement,
both organizationally and technically.
Much less often, the weighting of criteria used for decision-making is
done by a variety of different stakeholders. However, especially for
decision-making with high social relevance, the involvement of many
stakeholders is very important. At this point, the following questions
arise:
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
How do I collect the data?
\item
Which software or tool can perform an AHP with data from the survey of
a large number of stakeholders?
\item
How can data collection and processing be combined in a way that is
both organizationally and technically sensible and time-effective?
\end{enumerate}
As part of the DFG-funded project ``Edible Cities'', the objective was
to evaluate different forms of urban agriculture using AHP with regard
to their sustainability. The prerequisite for this was the involvement
of numerous stakeholders from various target groups in the weighting of
the previously selected criteria and sub-criteria. The pairwise
comparisons of criteria and sub-criteria required for weighting should
be performed by means of an online survey, which should ideally be
followed directly by the AHP calculation automatically. Since the
stakeholders interviewed here were predominantly people from the
non-scientific environment, the survey had to be structured to suit
these target groups.
To the authors' knowledge, the only online tool available for conducting
a survey in combination with a directly subsequent criteria weighting is
``AHP-OS'' from \href{https://bpmsg.com}{BPMSG (Business Performance
Management Singapore)}.
Reflecting the above requirements, especially with regard to the target
groups, ``AHP-OS'' appeared unsuitable, however, as it is too strongly
designed to generate consistent datasets. For this purpose, participants
are asked to reconsider their decisions several times, if necessary, and
to adjust them in the direction of consistency. This very scientific
approach would not have been appropriate for the intended target groups.
Therefore, the authors followed the approach of separating the
stakeholder survey and the subsequent weighting of the criteria in
organizational and technical terms.
This paper introduces, for the first time, a procedure in which data
collected using the online survey platform
\href{https://www.soscisurvey.de}{SoSci Survey} are subsequently
processed with the \texttt{ahpsurvey} package (see
\cite{Vignettes_ahpsurvey_2019}). This package is exclusively available
for the statistical programming language \textbf{R} and, to the authors'
knowledge, is the only tool that meets the requirements outlined in the
previous sections.
\begin{verbatim}
@TODO:
The following remains to be revised and adjusted.
\end{verbatim}
Why we use a
\href{https://en.wikipedia.org/wiki/Project_Jupyter}{Jupyter} notebook
to to publish the R program examples:
Jupyter is a new \textbf{open source} alternative to the proprietary
numerical software
\href{https://en.wikipedia.org/wiki/Wolfram_Mathematica}{Mathematica}
from \textbf{Wolfram Research} that is well on the way to become a
\textbf{standard for exchanging research results}
(\cite{Scientific_Paper_obsolete_2018};
\cite{Future_of_Research_Paper_2018}).
Originally Jupyter was intended as an IDE for the programming languages
\textbf{Julia} and \textbf{Python}. Besides that it is also possible to
install other interpreter kernels, such as the
\textbf{\href{https://irkernel.github.io/installation/}{IRkernel}} for
\textbf{R}. This can be interesting if the IDE \textbf{RStudio Desktop}
is not available on the target platform used. For example, it is very
difficult to install RStudio on the ARM-based embedded computer
\textbf{Raspberry Pi} due to many technical dependencies. In contrast,
using the R kernel in JupyterLab on the Raspberry Pi works very well and
performant.
\hypertarget{loading-of-used-r-packages-and-definition-of-global-functions}{%
\section{Loading of used R packages and definition of global
functions}\label{loading-of-used-r-packages-and-definition-of-global-functions}}
\hypertarget{install-missing-packages-if-not-present-yet}{%
\subsection{Install missing packages if not present
yet}\label{install-missing-packages-if-not-present-yet}}
In order to load the R packages used in the next sections, they must be
installed in the R environment. The following function checks for the
presence of the packages and installs the missing ones.
\textbf{Attention:} For some R packages, several dependencies have to be
installed first using the package manager of the operating system,
e.g.~\texttt{apt\ install\ \textless{}package\ name\textgreater{}}.
In general, the use of R version \(\geq\) 4.0 is strongly recommended.
In particular, the \texttt{ahpsurvey} package, which is essential for
calculating the AHP, depends on the \texttt{randomNames} package.
However, this is only available starting with R version \(\geq\) 4.0
(refer to
\href{https://cran.r-project.org/web/packages/randomNames/index.html}{randomNames:
Generate Random Given and Surnames}).
This can be problematic especially with slightly older systems, e.g.~on
the operating system \textbf{Raspbian buster} for the very well-known
\textbf{Raspberry Pi}, R is only available in version 3.5.2. Upgrading R
in Raspbian following the instructions on
\url{https://cran.rstudio.com/bin/linux/debian/\#debian-buster-stable}
has not succeeded for the authors so far.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{1}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{c+c1}{\PYZsh{} List of R packages that are used in this script}
\PY{n}{list.of.packages}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{c}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{data.table\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{ggplot2\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{tidyr\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{dplyr\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{magrittr\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{ahpsurvey\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{knitr\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{IRdisplay\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{forcats\PYZdq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Query the already installed packages and save the missing ones in a new list}
\PY{n}{missing.packages}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n}{list.of.packages}\PY{p}{[}\PY{o}{!}\PY{p}{(}\PY{n}{list.of.packages}\PY{+w}{ }
\PY{+w}{ }\PY{o}{\PYZpc{}in\PYZpc{}}\PY{+w}{ }\PY{n+nf}{installed.packages}\PY{p}{(}\PY{p}{)}\PY{p}{[}\PY{p}{,}\PY{l+s}{\PYZdq{}}\PY{l+s}{Package\PYZdq{}}\PY{p}{]}\PY{p}{)}\PY{p}{]}
\PY{c+c1}{\PYZsh{} Install missing packages}
\PY{n+nf}{if}\PY{p}{(}\PY{n+nf}{length}\PY{p}{(}\PY{n}{missing.packages}\PY{p}{)}\PY{p}{)}\PY{+w}{ }\PY{p}{\PYZob{}}
\PY{+w}{ }\PY{n+nf}{install.packages}\PY{p}{(}\PY{n}{missing.packages}\PY{p}{)}
\PY{p}{\PYZcb{}}\PY{+w}{ }\PY{n}{else}\PY{+w}{ }\PY{p}{\PYZob{}}
\PY{+w}{ }\PY{n+nf}{print}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{All required packages are installed.\PYZdq{}}\PY{p}{)}
\PY{p}{\PYZcb{}}
\end{Verbatim}
\end{tcolorbox}
\begin{Verbatim}[commandchars=\\\{\}]
[1] "All required packages are installed."
\end{Verbatim}
\hypertarget{load-r-packages}{%
\subsection{Load R packages}\label{load-r-packages}}
After proving in the previous section that all required R packages are
installed, they can be loaded in the following subsections.
\hypertarget{load-package-data.table}{%
\subsubsection{\texorpdfstring{Load package
\texttt{data.table}}{Load package data.table}}\label{load-package-data.table}}
The \texttt{data.table} package is used for \textbf{reading and editing
tables}.
\textbf{Note:} This package inherits from \texttt{data.frame}.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{2}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{data.table}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-ggplot2}{%
\subsubsection{\texorpdfstring{Load package
\texttt{ggplot2}}{Load package ggplot2}}\label{load-package-ggplot2}}
The package \texttt{ggplot2} is used to \textbf{plot beautiful
diagrams}.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{3}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{ggplot2}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-packages-knitr-and-irdisplay}{%
\subsubsection{\texorpdfstring{Load packages \texttt{knitr} and
\texttt{IRdisplay}}{Load packages knitr and IRdisplay}}\label{load-packages-knitr-and-irdisplay}}
The \texttt{kable()} function from the package \texttt{knitr} is used to
output dataframes as markdown tables.
The \texttt{display\_markdown()} function from the package
\texttt{IRdisplay} is used to \textbf{render markdown tables} in the
notebook as well as in the compiled PDF output.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{4}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{knitr}\PY{p}{)}
\PY{n+nf}{library}\PY{p}{(}\PY{n}{IRdisplay}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-tidyr}{%
\subsubsection{\texorpdfstring{Load package
\texttt{tidyr}}{Load package tidyr}}\label{load-package-tidyr}}
The package \texttt{tidyr} is used to \textbf{reshape dataframes} and
provides functions like \texttt{gather()} or \texttt{spread()}. Some
examples for the application can be found here:
\href{https://uc-r.github.io/tidyr}{Reshaping your data with tidyr}.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{5}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{tidyr}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-dplyr}{%
\subsubsection{\texorpdfstring{Load package
\texttt{dplyr}}{Load package dplyr}}\label{load-package-dplyr}}
The package \texttt{dplyr} is necessary to \textbf{manipulate
dataframes} using functions like \texttt{select()}, \texttt{mutate()}
and \texttt{left\_join()}.
\textbf{Hint:} Annoying messages on package loading regarding masked
functions can be suppressed by setting the parameter
\texttt{warn.conflicts=FALSE} when calling the \texttt{library()}
function.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{6}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{dplyr}\PY{p}{,}\PY{+w}{ }\PY{n}{warn.conflicts}\PY{o}{=}\PY{k+kc}{FALSE}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-magrittr}{%
\subsubsection{\texorpdfstring{Load package
\texttt{magrittr}}{Load package magrittr}}\label{load-package-magrittr}}
The package \texttt{magrittr} provides the \textbf{pipe functionality}
and can be used to create more effective code for processing large
datasets. What pipes of the form like \texttt{\%\textgreater{}\%} are
and how to use them is described here:
\href{https://statistik-dresden.de/archives/15679}{R-Programmierung: Was
ist \texttt{\%\textgreater{}\%}?}.
\textbf{HINT:} The pipe functionality is already available by loading
the library \texttt{tidyr} - so you don't have to load it explicitly.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{7}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{magrittr}\PY{p}{,}\PY{+w}{ }\PY{n}{warn.conflicts}\PY{o}{=}\PY{k+kc}{FALSE}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-forcats}{%
\subsubsection{\texorpdfstring{Load package
\texttt{forcats}}{Load package forcats}}\label{load-package-forcats}}
The \texttt{fct\_inorder()} function from the package \texttt{forcats}
is used to \textbf{reorder the discrete levels of diagram axes}
according to the intended order of attributes.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{8}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{forcats}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{load-package-ahpsurvey}{%
\subsubsection{\texorpdfstring{Load package
\texttt{ahpsurvey}}{Load package ahpsurvey}}\label{load-package-ahpsurvey}}
The package \texttt{ahpsurvey} contains all the necessary mathematical
and statistical methods to run the \textbf{analytical hierarchy process
(AHP)}.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{9}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n+nf}{library}\PY{p}{(}\PY{n}{ahpsurvey}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{function-to-format-dataframes-as-markdown-tables}{%
\subsection{Function to format dataframes as markdown
tables}\label{function-to-format-dataframes-as-markdown-tables}}
Following function \textbf{formats given dataframes as markdown tables}
using the \texttt{kable()} function from the \texttt{knitr} package.
The \texttt{display\_markdown()} function from the package
\texttt{IRdisplay} is used to \textbf{render markdown tables} in the
notebook as well as in the compiled PDF output.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{10}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{func\PYZus{}render\PYZus{}md\PYZus{}tables}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{function}\PY{p}{(}\PY{n}{df\PYZus{}table}\PY{p}{,}\PY{+w}{ }\PY{n}{str\PYZus{}table\PYZus{}header}\PY{p}{)}\PY{+w}{ }\PY{p}{\PYZob{}}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} Format the dataframe as a markdown table using }
\PY{+w}{ }\PY{c+c1}{\PYZsh{} the \PYZsq{}kable()\PYZsq{} function from the \PYZsq{}knitr\PYZsq{} package.}
\PY{+w}{ }\PY{n}{table\PYZus{}out}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{kable}\PY{p}{(}
\PY{+w}{ }\PY{n}{df\PYZus{}table}\PY{p}{,}
\PY{+w}{ }\PY{n}{format}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{markdown\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} digits = 2,}
\PY{+w}{ }\PY{n}{caption}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n}{str\PYZus{}table\PYZus{}header}\PY{p}{)}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} print(table\PYZus{}out)}
\PY{+w}{ }\PY{n+nf}{display\PYZus{}markdown}\PY{p}{(}\PY{n+nf}{as.character}\PY{p}{(}\PY{n}{table\PYZus{}out}\PY{p}{)}\PY{p}{)}
\PY{p}{\PYZcb{}}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{prepare-raw-csv-input-data-from-sosci-survey-for-analytical-hierarchy-process-ahp}{%
\section{Prepare raw CSV input data from SoSci Survey for analytical
hierarchy process
(AHP)}\label{prepare-raw-csv-input-data-from-sosci-survey-for-analytical-hierarchy-process-ahp}}
The survey was conducted on the \href{https://www.soscisurvey.de/}{SoSci
Survey} platform and the results were exported as CSV files.
In this main section the CSV files are prepared in such a way that in
the following main section the AHP can be carried out using the R
package \texttt{ahpsurvey}.
\hypertarget{set-globally-used-input-and-output-folders-for-preparing-raw-csv-data}{%
\subsection{Set globally used input and output folders for preparing raw
CSV
data}\label{set-globally-used-input-and-output-folders-for-preparing-raw-csv-data}}
The following global variables are used to store the input and output
folders for CSV file preparation.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{11}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{str\PYZus{}input\PYZus{}path\PYZus{}prep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{./input\PYZus{}data\PYZus{}from\PYZus{}survey\PYZdq{}}
\PY{n}{str\PYZus{}output\PYZus{}path\PYZus{}prep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{./output\PYZus{}data\PYZus{}manipulated\PYZdq{}}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{define-functions-to-prepare-the-survey-data-for-further-analysis}{%
\subsection{Define functions to prepare the survey data for further
analysis}\label{define-functions-to-prepare-the-survey-data-for-further-analysis}}
The following functions are used to read the survey data from the input
CSV files, to prepare the data structure for further analysis with the R
package \texttt{ahpsurvey} and to store the results in the output CSV
files.
\hypertarget{function-to-read-the-survey-data-from-csv-files-to-dataframe-objects}{%
\subsubsection{Function to read the survey data from CSV files to
dataframe
objects}\label{function-to-read-the-survey-data-from-csv-files-to-dataframe-objects}}
This function reads a CSV file and stores the data in four different
dataframes by selecting different columns for each. The four dataframes
contain the \textbf{main criteria}, the \textbf{environmental}, the
\textbf{social}, or the \textbf{economic sub-criteria}.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{12}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{func\PYZus{}readCSVdata\PYZus{}to\PYZus{}dataframes}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{function}\PY{p}{(}\PY{n}{str\PYZus{}CSVfilename}\PY{p}{)}\PY{+w}{ }\PY{p}{\PYZob{}}
\PY{+w}{ }
\PY{+w}{ }\PY{c+c1}{\PYZsh{} Criteria (main criteria)}
\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}1}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{fread}\PY{p}{(}
\PY{+w}{ }\PY{n}{file}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n}{str\PYZus{}CSVfilename}\PY{p}{,}\PY{+w}{ }\PY{n}{encoding}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{UTF\PYZhy{}8\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{n}{header}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{k+kc}{TRUE}\PY{p}{,}\PY{+w}{ }\PY{n}{sep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}t\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{n}{quote}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}\PYZdq{}\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} dec = \PYZdq{}.\PYZdq{}, row.var = \PYZdq{}CASE\PYZdq{},}
\PY{+w}{ }\PY{n}{select}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n+nf}{c}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{CASE\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AK01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AK02\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AK03\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK01\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK02\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK03\PYZus{}01\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK04\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK05\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RK06\PYZus{}01\PYZdq{}}\PY{p}{)}
\PY{+w}{ }\PY{p}{)}
\PY{+w}{ }
\PY{+w}{ }\PY{c+c1}{\PYZsh{} Environmental sub\PYZhy{}criteria}
\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}2}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{fread}\PY{p}{(}
\PY{+w}{ }\PY{n}{file}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n}{str\PYZus{}CSVfilename}\PY{p}{,}\PY{+w}{ }\PY{n}{encoding}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{UTF\PYZhy{}8\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{n}{header}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{k+kc}{TRUE}\PY{p}{,}\PY{+w}{ }\PY{n}{sep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}t\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{n}{quote}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}\PYZdq{}\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} dec = \PYZdq{}.\PYZdq{}, row.names = \PYZdq{}CASE\PYZdq{},}
\PY{+w}{ }\PY{n}{select}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n+nf}{c}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{CASE\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AU01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AU02\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AU03\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU01\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU02\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU03\PYZus{}01\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU04\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU05\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RU06\PYZus{}01\PYZdq{}}\PY{p}{)}
\PY{+w}{ }\PY{p}{)}
\PY{+w}{ }
\PY{+w}{ }\PY{c+c1}{\PYZsh{} Social sub\PYZhy{}criteria}
\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}3}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{fread}\PY{p}{(}
\PY{+w}{ }\PY{n}{file}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n}{str\PYZus{}CSVfilename}\PY{p}{,}\PY{+w}{ }\PY{n}{encoding}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{UTF\PYZhy{}8\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{n}{header}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{k+kc}{TRUE}\PY{p}{,}\PY{+w}{ }\PY{n}{sep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}t\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{n}{quote}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}\PYZdq{}\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} dec = \PYZdq{}.\PYZdq{}, row.names = \PYZdq{}CASE\PYZdq{},}
\PY{+w}{ }\PY{n}{select}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n+nf}{c}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{CASE\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AS01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AS02\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AS03\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS01\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS02\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS03\PYZus{}01\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS04\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS05\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RS06\PYZus{}01\PYZdq{}}\PY{p}{)}
\PY{+w}{ }\PY{p}{)}
\PY{+w}{ }
\PY{+w}{ }\PY{c+c1}{\PYZsh{} Economic sub\PYZhy{}criteria}
\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}4}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{fread}\PY{p}{(}
\PY{+w}{ }\PY{n}{file}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n}{str\PYZus{}CSVfilename}\PY{p}{,}\PY{+w}{ }\PY{n}{encoding}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{UTF\PYZhy{}8\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{n}{header}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{k+kc}{TRUE}\PY{p}{,}\PY{+w}{ }\PY{n}{sep}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}t\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{n}{quote}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{\PYZbs{}\PYZdq{}\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{c+c1}{\PYZsh{} dec = \PYZdq{}.\PYZdq{}, row.names = \PYZdq{}CASE\PYZdq{},}
\PY{+w}{ }\PY{n}{select}\PY{+w}{ }\PY{o}{=}\PY{+w}{ }\PY{n+nf}{c}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{CASE\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AW01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AW02\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{AW03\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW01\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW02\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW03\PYZus{}01\PYZdq{}}\PY{p}{,}
\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW04\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW05\PYZus{}01\PYZdq{}}\PY{p}{,}\PY{+w}{ }\PY{l+s}{\PYZdq{}}\PY{l+s}{RW06\PYZus{}01\PYZdq{}}\PY{p}{)}
\PY{+w}{ }\PY{p}{)}
\PY{+w}{ }
\PY{+w}{ }\PY{n}{output}\PY{+w}{ }\PY{o}{\PYZlt{}\PYZhy{}}\PY{+w}{ }\PY{n+nf}{list}\PY{p}{(}\PY{n}{df\PYZus{}mySurvey\PYZus{}1}\PY{p}{,}\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}2}\PY{p}{,}\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}3}\PY{p}{,}\PY{+w}{ }\PY{n}{df\PYZus{}mySurvey\PYZus{}4}\PY{p}{)}
\PY{+w}{ }
\PY{+w}{ }\PY{n+nf}{return}\PY{p}{(}\PY{n}{output}\PY{p}{)}
\PY{p}{\PYZcb{}}
\end{Verbatim}
\end{tcolorbox}
\hypertarget{function-to-adapt-the-exported-survey-data-to-saaty-scale}{%
\subsubsection{Function to adapt the exported survey data to Saaty
scale}\label{function-to-adapt-the-exported-survey-data-to-saaty-scale}}
For the \textbf{comparison of two attributes}, Saaty introduced a scale
of nine rating items shown in the following table (see
\cite{Saaty_AHP_1987}):
\begin{longtable}[]{@{}rl@{}}
\caption{Nine rating items according to Saaty (adapted from source:
\cite{Vignettes_ahpsurvey_2019})}\tabularnewline
\toprule()
Rating & Definition \\
\midrule()
\endfirsthead
\toprule()
Rating & Definition \\
\midrule()
\endhead
1 & Two attributes are equally important \\
2 & Between 1 and 3 \\
3 & The preferred attribute is slightly more important \\
4 & Between 3 and 5 \\
5 & The preferred attribute is moderately more important \\
6 & Between 5 and 7 \\
7 & The preferred attribute is strongly more important \\
8 & Between 7 and 9 \\
9 & The preferred attribute is absolutely more important \\
\bottomrule()
\end{longtable}
To be able to describe the comparison of two attributes uniquely,
negative values as well as positive values are introduced for the rating
items. \textbf{Negative} values prefer the \(Attribute\;X\) and
\textbf{positive} values the \(Attribute\;Y\). A value of \textbf{\(1\)}
means that both attributes are \textbf{equally weighted}, i.e.~equally
important. Note that the values \(0\) and \(\text{-}1\) \textbf{do not}
exist. Thus, Saaty's 17-step scale will result as follows:
\[Attribute\;X\;\;\;\;\text{-}9\;\text{-}8\;\text{-}7\;\ldots\;\text{-}3\;\text{-}2\;\;\;\textbf{1}\;\;\;2\;3\;\ldots\;7\;8\;9\;\;\;\;Attribute\;Y\]
The package \texttt{ahpsurvey} employs the 17-step scale according to
Saaty (see \cite{Vignettes_ahpsurvey_2019}).
On the one hand, Saaty's 17-step scale was not technically well
implementable on the survey platform used
\href{https://www.soscisurvey.de/}{SoSci Survey}. On the other hand, it
had been too fine-granular for the mostly non-scientific target groups
of participants.
Therefore, a \textbf{2-stage query} was chosen as an alternative
approach. In \textbf{stage 1}, it was first asked whether, and if so,
which of the two attributes was preferred and thus given a higher
weighting. Stage 1 resulted in the following encoding:
\begin{itemize}
\tightlist
\item
\(\text{-}1\): \(Attribute\;X\) and \(Attribute\;Y\) \textbf{equally
important}
\item
\(\;1\): \(Attribute\;X\) \textbf{more important} than
\(Attribute\;Y\)
\item
\(\;2\): \(Attribute\;X\) \textbf{less important} than
\(Attribute\;Y\)
\end{itemize}
With different weightings, the respondents in \textbf{stage 2} had to
decide how much more important the respective attribute was to them on a
scale of 1 to 8. Thereby, 1 corresponds to 2, 2 to 3, etc. of the rating
items according to Saaty. See the following figure:
\begin{figure}
\centering
\includegraphics{images/Saaty_scale_to_SoSciSurvey.png}
\caption{Transformation from the Saaty's 17-step scale to the encoded
scale applied by a 2-stage query (source: Kasper, license: CC BY-SA
4.0)}
\end{figure}
The survey results were exported from \emph{SoSci Survey} in the form of
an \textbf{encoded scale} as a CSV file. With the following function
\texttt{func\_adaptData2SaatyScale} this now has to be converted back to
the Saaty scale in order to run AHP with the R package
\texttt{ahpsurvey}.
The values of the encoding and those of the weighting have to be taken
from the CSV file from three different columns per pairwise attribute
comparison. For example, when comparing the two attributes
``Microclimate and Hydrology (Clim)'' and ``Biodiversity (BDiv)'', the
column \texttt{AU01} contains the encoding and the columns
\texttt{RU01\_01} or \texttt{RU02\_01} the respective weighting.
The following example shows the three possible cases and the conversion
to the Saaty scale.
\textbf{Case 1:}
\begin{itemize}
\tightlist
\item
if \texttt{AU01} \(= \text{-}1\), then set weighting \(= 1\)
\item
attributes \texttt{Clim} and \texttt{BDiv} are equally important
\item
values in columns \texttt{RU01\_01} or \texttt{RU02\_01} are ignored
\end{itemize}
\textbf{Case 2:}