zeimena.pdf

II/Referatas/mj-referatas.tex

@@ -31,28 +31,61 @@
 \section{Abstract}
 \label{sec:abstract}
 
-Ready-to-use, open-source line generalization solutions emit poor cartographic
-output. Therefore, if one is using open-source technology to create a
-large-scale map, downscaled lines (e.g. rivers) will look poorly. This paper
-explores line generalization algorithms and suggests one for an avid GIS
-developer to implement. Once it is implemented and integrated to open-source
-GIS solutions (e.g. QGIS), rivers on future large-scale maps will look
-professionally downscaled.
+Current open-source line generalization solutions have their roots in
+mathematics and geometry, thus emit poor cartographic output. Therefore, if one
+is using open-source technology to create a large-scale map, downscaled lines
+(e.g. rivers) will not be professionally scale-adjusted. This paper explores
+line generalization algorithms and suggests one for an avid GIS developer to
+implement. Once it is usable from within open-source GIS software (e.g. QGIS or
+PostGIS), rivers on these large-scale maps will look professionally downscaled.
 
 \section{Introduction}
 \label{sec:introduction}
 
 Cartographic generalization is one of the key processes of creating large-scale
 maps: how can one approximate object features, without losing its main
-cartographic properties?
+cartographic properties? The problem is universally challenging across many
+geographical entities (\cite{muller1991generalization},
+\cite{mcmaster1992generalization}). This paper focuses on line generalization,
+using natural rivers as examples.
 
-Generalization algorithms are well studied, tested and implemented, but they
-expose deficiencies in large-scale reduction (\cite{monmonier1986toward},
-\cite{mcmaster1993spatial}, \cite{jiang2003line}, \cite{dyken2009simultaneous},
-\cite{mustafa2006dynamic}, \cite{nollenburg2008morphing}).
+Line generalization algorithms are well studied, tested and implemented, but
+they expose deficiencies in large-scale reduction (\cite{monmonier1986toward},
+\cite{mcmaster1993spatial}). Most of these techniques are based on mathematical
+shape representation, rather than cartographic characteristics of the line.
 
-There are two main approaches to generalize lines in a map: geometric and
-cartographic.
+In this paper we explore algorithms which are derived from cartographic
+knowledge and processes, so their output is as similar as an experienced
+cartographer would create, thus most correct and visually appealing.
+
+We will be using a small part of Žeimena:
+
+\begin{figure}
+    \centering
+    \includegraphics[width=\textwidth]{zeimena}
+    \caption{Žeimena near Jaunadaris}
+\end{figure}
+
+\section{Mathematical and geometrical algorithms}
+
+To understand why geometrical algorithms are not entirely suitable for 
+downscaling, let's pick some visual examples.
+
+\subsection{Douglas \& Peucker}
+
+\cite{douglas1973algorithms} is one of the most well-known line simplification algorithm.
+
+
+\section{Algorithms based on cartographical knowledge}
+
+\cite{jiang2003line}, \cite{dyken2009simultaneous},
+\cite{mustafa2006dynamic}, \cite{nollenburg2008morphing}
+
+\section{My Idea}
+\label{sec:my_idea}
+
+\section{Related Work}
+\label{sec:related_work}
 
 \cite{stanislawski2012automated} studied different types of metric assessments,
 such as Hausdorff distance, segment length, vector shift, surface displacement,
@@ -60,17 +93,6 @@ and tortuosity for the generalization of linear geographic elements. Their
 research can provide references to the appropriate settings of the line
 generalization parameters for the maps at various scales.
 
-\section{The Problem}
-\label{sec:the_problem}
-
-\section{My Idea}
-\label{sec:my_idea}
-
-\section{The Details}
-\label{sec:the_details}
-
-\section{Related Work}
-\label{sec:related_work}
 
 \section{Conclusions and Further Work}
 \label{sec:conclusions_and_further_work}