165 lines
5.1 KiB
TeX
165 lines
5.1 KiB
TeX
\documentclass[a4paper]{article}
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\iffalse
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\usepackage[L7x,T1]{fontenc}
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\usepackage[lithuanian]{babel}
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\else
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\usepackage[T1]{fontenc}
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\usepackage[english]{babel}
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\fi
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\usepackage[utf8]{inputenc}
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\usepackage{a4wide}
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\usepackage{csquotes}
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\usepackage[maxbibnames=99,style=authoryear]{biblatex}
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\usepackage[pdfusetitle]{hyperref}
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\usepackage{enumitem}
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\usepackage[toc,page,title]{appendix}
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\addbibresource{bib.bib}
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\usepackage{caption}
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\usepackage{subcaption}
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\usepackage{gensymb}
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\usepackage{varwidth}
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\usepackage{tabularx}
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\usepackage{float}
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\usepackage{tikz}
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\usepackage{minted}
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\usetikzlibrary{er,positioning}
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\definecolor{mypurple}{RGB}{117,112,179}
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\input{version}
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\newcommand{\DP}{Douglas \& Peucker}
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\newcommand{\VW}{Visvalingam--Whyatt}
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\newcommand{\WM}{Wang--M{\"u}ller}
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\title{
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Cartographic Generalization of Lines using free software \\
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(example of rivers) \\ \vspace{4mm}
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}
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\iffalse
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\fi
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\author{Motiejus Jakštys}
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\date{
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\vspace{10mm}
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Version: \VCDescribe \\ \vspace{4mm}
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Generated At: \GeneratedAt
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}
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\begin{document}
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\maketitle
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\begin{abstract}
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\label{sec:abstract}
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Current open-source line generalization solutions have their roots in
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mathematics and geometry, and are not fit for natural objects like rivers
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and coastlines. This paper discusses our implementation of \WM algorithm
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under and open-source license, explains things that we would had
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appreciated in the original paper and compares our results to different
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generalization algorithms.
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\end{abstract}
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\newpage
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\tableofcontents
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\listoffigures
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\newpage
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\section{Introduction}
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\label{sec:introduction}
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A number of cartographic line generalization algorithms have been researched,
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which claim to better process cartographic objects like lines. These fall into
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two rough categories:
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\begin{itemize}
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\item Cartographic knowledge was encoded to an algorithm (bottom-up
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approach). One among these are \cite{wang1998line}.
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\item Mathematical shape transformation which yields a more
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cartographically suitable down-scaling. E.g. \cite{jiang2003line},
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\cite{dyken2009simultaneous}, \cite{mustafa2006dynamic},
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\cite{nollenburg2008morphing}.
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\end{itemize}
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During research for the mentioned articles, prototype code has been written for
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most of the algorithms. However, none of them seem to be available for use
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except for the two "classical" ones -- {\DP} and {\VW}.
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\cite{wang1998line} is an algorithm specifically created for cartographic
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generalization and available for general use, though it is only currently
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available in a commercial product. This poses a problem for map creation in
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open source software: there is not a similar high-quality simplification
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algorithm to create down-scaled maps, so any cartographic work, which uses line
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generalization as part of its processing, will be of sub-par quality.
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We believe that availability of high-quality open-source tools is an important
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foundation for future cartographic experimentation and development, thus it
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it benefits the cartographic society as a whole.
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This paper will be reviewing and comparing two widely available algorithms that
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are often used for line generalization:
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\begin{itemize}
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\item \cite{douglas1973algorithms} via
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\href{https://postgis.net/docs/ST_Simplify.html}{PostGIS Simplify}.
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\item \cite{visvalingam1993line} via
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\href{https://postgis.net/docs/ST_SimplifyVW.html}{PostGIS SimplifyVW}.
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\end{itemize}
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Since both algorithms produce jaggy output lines, it is worthwhile to process
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those through a widely available \cite{chaikin1974algorithm} smoothing
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algorithm via \href{https://postgis.net/docs/ST_ChaikinSmoothing.html}{PostGIS
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ChaikinSmoothing}.
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\section{Visual comparison}
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\subsection{Comparison algorithms and parameters}
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\subsection{Combining bends}
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\section{Conclusions}
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\label{sec:conclusions}
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\section{Related Work and future suggestions}
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\label{sec:related_work}
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\printbibliography
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\begin{appendices}
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\section{Žeimena and Lakaja in context}
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\section{Code listings}
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For the curious users it may be useful to see how the analysis was executed.
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Also, given the source listings, it should be relatively straightforward to
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re-run the same analysis on a different area.
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The analysis was executed and report was generated on Ubuntu 20.04 with only
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system packages. This should be sufficient: {\tt postgis gdal-bin biber
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latexmk texlive-bibtex-extra python3-geopandas python3-pygments}.
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\subsection{Makefile}
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This file binds all the pieces together:
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\begin{itemize}
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\item Prepares the PostGIS database.
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\item Generates helper figures (sine waves, squares).
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\item Runs analysis on input files ({\DP}, {\VW}, Chaikin).
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\item Invokes {\tt latexmk} as a final report generation step.
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\end{itemize}
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\inputminted[fontsize=\small]{make}{Makefile}
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\subsection{layer2img.py}
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This file accepts a layer (or two) and generates a PDF image suitable for embedding into the report.
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\inputminted[fontsize=\small]{python}{layer2img.py}
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\subsection{db}
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Manages a PostGIS database in the Docker container. That way, the database can
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be torn down and re-created by automated tools like the {\tt Makefile} itself.
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\inputminted[fontsize=\small]{bash}{db}
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\end{appendices}
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\end{document}
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