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@ -294,9 +294,12 @@ valuable characterization of the river.
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Sometimes low-water rivers in slender slopes have many bends next to each
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other. In low resolutions (either in small-DPI screens or paper, or when the
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river is sufficiently zoomed out, or both), the small bends will amalgamate to
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a unintelligible blob. Figure~\onpage{fig:amalgamate1} and
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figure~\onpage{fig:amalgamate2} are real-world examples where a river, normally
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1 or 2 pixels wide, creates a few pixels wide blob due to a number of bends.
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a unintelligible blob. Figure~\onpage{fig:pixel-amalgamation} illustrates two
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real-world examples where a bendy river, normally 1 or 2 pixels wide, creates a
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wide area, of which the shapes of the bend are unintelligible. In this example,
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classical algorithms would remove these bends altogether. A cartographer would
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retain a few of those distinctive bends, but would increase the distance
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between the bends, remove some of the bends, or both.
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\begin{figure}[h]
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\includegraphics[width=\textwidth]{amalgamate1}
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@ -304,9 +307,9 @@ figure~\onpage{fig:amalgamate2} are real-world examples where a river, normally
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\label{fig:pixel-amalgamation}
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\end{figure}
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Therefore, a more robust
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generalization algorithm is worthwhile for lookout.
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For the reasons discussed in this section, the "classical" {\DP} and {\VW} are
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not well suited for natural river generalization, and a more robust line
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generalization algorithm is worthwhile for to look for.
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\subsubsection{Modern approaches}
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@ -519,6 +522,12 @@ This type of illustration works quite well, since polygons created from bends
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are almost never overlapping, and discriminating different backgrounds is
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easier than discriminating different line shapes or colors.
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\subsection{Merging pieces of the river into one}
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NOTE: explain how different river segments are merged into a single line. This
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is not explained in the {\WM} paper, but is a necessary prerequisite. This is
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implemented in \texttt{aggregate-rivers.sql}.
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\subsection{Definition of a Bend}
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\label{sec:definition-of-a-bend}
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@ -694,7 +703,6 @@ takes to run this piece of the algorithm drops by $80\%$.
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\subsection{Attributes of a Single Bend}
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\textsc{Compactness Index} is "the ratio of the area of the polygon over the
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circle whose circumference length is the same as the length of the
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circumference of the polygon" \cite{wang1998line}. Given a bend, its
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@ -780,26 +788,50 @@ The smaller the distance $d$, the more similar the bends are.
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\subsection{Elimination Operator}
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NOTE: not implemented.
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\subsection{Combination Operator}
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NOTE: not implemented.
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\subsection{Exaggeration Operator}
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NOTE: not implemented.
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\section{Program Implementation}
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NOTE: this should provide a higher-level overview of the written code:
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\begin{itemize}
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\item State machine (which functions call when).
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\item Algorithmic complexity.
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\item Expected runtime given the number of bends/vertices, some performance
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experiments.
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\end{itemize}
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\section{Results of Experiments}
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NOTE: this can only be filled after the algorithm implementation is complete.
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\section{Conclusions}
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\label{sec:conclusions}
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NOTE: write when all the sections before this are be complete.
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\section{Related Work and future suggestions}
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\label{sec:related_work}
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NOTE: write after section~\ref{sec:conclusions} is complete.
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\printbibliography
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\begin{appendices}
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\section{Code listings}
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This section contains code listings of a subset of files tightly related to the
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{\WM} algorithm.
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\subsection{Re-generating this paper}
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\label{sec:code-regenerate}
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@ -810,8 +842,11 @@ Like explained in section~\ref{sec:reproducing-the-paper}, illustrations in
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\inputcode{bash}{extract-and-generate}
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\subsection{Algorithm code listings}
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\subsection{\texttt{ST\_SimplifyWV}}
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\inputcode{postgresql}{wm.sql}
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\subsection{\texttt{aggregate\_rivers}}
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\inputcode{postgresql}{aggregate-rivers.sql}
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\end{appendices}
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\end{document}
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