\subsection{Generalization in cartography}

In map generalization one aims to reduce the data presented appropriate to the scale and/or purpose of the map \parencite{brophy1973automated}. This selection has been a manual process for a long time, such that geographic generalization has been developed into an art that can only be learned by years of apprenticeship and practice \parencite{brassel1990computergestutzte}. When using automation one could be concerned about a lower quality of maps. This is why many talk about computer assisted generalization where only subprocesses can be fully automated. 

Polyline simplification is the most basic topic in map generalization \parencite{ai2017envelope}. The problems of geographic cartography also apply here. So a number of algorithms have been developed to describe the work of cartographers as abstract, computer automatable processes. A selection of these algorithms will be explained in chapter \ref{ch:algorithms}. 

Cartography does not halt before digitalization. In the era of big data there is a large volume of map data available. Many come from collaborative projects like OpenStreetMap\footnote{\url{https://www.openstreetmap.org/}} (OSM) where volunteers submit freely available geographic information. To deliver this mass of data over the internet one can make use of the simplification processes described in this thesis. This is particularly useful as the information provided has usually no scale description. Automated simplification can bring appropriate data sizes while maintaining data usability \parencite{ai2017envelope}.
