Clustering and cartographic simplification of point data set

Abstract

As a key aspect of the mapping process, clustering and cartographic simplification plays a vital role in assessing the overall utility of Geographic information system. Within the digital environment, a number of research have been undertaken to define this process. Clustering and cartographic simplification is more related to visualization but also an important tool in decision making. The underlying process is mostly embedded in the system rather leaving options for the user. It is useful to have alternative local methods than very much programmed, closed and embedded methods for better understandings of the details of this process. In this research an attempt has been taken to develop a method for cartographic simplification through clustering. A point data set has been segmented into two different cluster groups by using two classic clustering techniques (K‐means clustering algorithm & Agglomerative hierarchical algorithm) and then the cluster groups have been simplified for avoid point congestion in smaller scale map than the original scale map. The produced results show the segmented data into two different cluster groups and a simplified form of them. There exist some visual disturbances in the simplified data which generates scope for further research.

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