117 lines
3.7 KiB
TeX
117 lines
3.7 KiB
TeX
\documentclass{article}
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\usepackage[L7x,T1]{fontenc}
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\usepackage[utf8]{inputenc}
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\usepackage{a4wide}
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\usepackage{csquotes}
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\usepackage[english]{babel}
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\usepackage[maxbibnames=99,style=authoryear]{biblatex}
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\addbibresource{bib.bib}
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\usepackage{hyperref}
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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{tikz}
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\usetikzlibrary{er,positioning}
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\title{
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Cartografic Generalization of Lines \\
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(example of rivers) \\ \vspace{4mm}
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}
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\iffalse
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a4: 210x297mm
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a6: 105x148xmm
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a7: 74x105mm
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a8: 52x74mm
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\fi
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\author{Motiejus Jakštys}
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\date{\today}
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\begin{document}
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\maketitle
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\newpage
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\section{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, thus emit poor cartographic output. Therefore, if one
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is using open-source technology to create a large-scale map, downscaled lines
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(e.g. rivers) will not be professionally scale-adjusted. This paper explores
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line generalization algorithms and suggests one for an avid GIS developer to
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implement. Once it is usable from within open-source GIS software (e.g. QGIS or
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PostGIS), rivers on these large-scale maps will look professionally downscaled.
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\section{Introduction}
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\label{sec:introduction}
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Cartographic generalization is one of the key processes of creating large-scale
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maps: how can one approximate object features, without losing its main
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cartographic properties? The problem is universally challenging across many
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geographical entities (\cite{muller1991generalization},
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\cite{mcmaster1992generalization}). This paper focuses on line generalization,
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using natural rivers as examples.
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Line generalization algorithms are well studied, tested and implemented, but
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they expose deficiencies in large-scale reduction (\cite{monmonier1986toward},
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\cite{mcmaster1993spatial}). Most of these techniques are based on mathematical
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shape representation, rather than cartographic characteristics of the line.
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In this paper we explore algorithms which are derived from cartographic
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knowledge and processes, so their output is as similar as an experienced
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cartographer would create, thus most correct and visually appealing.
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For comparison reasons, this article will be using a 4.4-kilometer subset of
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Žeimena near Jaunadaris village (see figure~\ref{fig:zeimena} on
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page~\pageref{fig:zeimena}). This location was chosen because it is a combination
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of straight and curved river shape, and author's familiarity with the location.
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\begin{figure}
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\centering
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\includegraphics[width=148mm]{zeimena}
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\caption{Žeimena near Jaunadaris}
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\label{fig:zeimena}
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\end{figure}
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\section{Mathematical and geometrical algorithms}
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To understand why geometrical algorithms are not entirely suitable for
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downscaling, let's pick some visual examples.
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\subsection{Douglas \& Peucker}
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\cite{douglas1973algorithms} is one of the most well-known line simplification
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algorithms, which is often used for generalization. It will simplify the line shape.
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Trying the same dataset with different tolerances for Douglas \& Peucker.
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\section{Algorithms based on cartographical knowledge}
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\cite{jiang2003line}, \cite{dyken2009simultaneous},
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\cite{mustafa2006dynamic}, \cite{nollenburg2008morphing}
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\section{My Idea}
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\label{sec:my_idea}
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\section{Related Work}
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\label{sec:related_work}
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\cite{stanislawski2012automated} studied different types of metric assessments,
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such as Hausdorff distance, segment length, vector shift, surface displacement,
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and tortuosity for the generalization of linear geographic elements. Their
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research can provide references to the appropriate settings of the line
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generalization parameters for the maps at various scales.
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\section{Conclusions and Further Work}
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\label{sec:conclusions_and_further_work}
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\printbibliography
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\end{document}
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