Çok Büyük Konumsal Verinin Görselleştirilmesinde Karmaşıklık Yönetimi
Özet
Data visualization is becoming more challenging by the day due to a continuous increase in
the size of data to be visualized. Geographical data is no exception, especially considering
that smart phones enable almost anybody to produce location data. However, visualization
of such large data has to be done on a map, which be- comes crowded, cluttered and
unreadable at lower zoom levels. If we increase the zoom level, we can display more
details, but we lose the completeness of visuals. In this study, we consider a binary feature
to be visualized on a map. We apply three different approaches to recognize feature
clusters within the data. Each cluster then corresponds to a geographical region and one of
the two feature values. The visualization done like this results in a minor amount of
information loss. We compare these three methods with respect to entropy gain, memory
and speed by measuring this loss in terms of information entropy. Also, we provide
detailed enumerative results under different visualization scenarios.
