Clustering to Reduce Spatial Data Set Size

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We use a machine learning approach with density-based clustering to compress such spatial data into a set of representative features

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Year of publication:	2018
Authors:	Boeing, Geoff
Publisher:	[S.l.] : SSRN
Subject:	Regionales Cluster \| Regional cluster \| Clusteranalyse \| Cluster analysis \| Betriebsgröße \| Firm size \| Räumliche Verteilung \| Spatial distribution

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