Clustering to Reduce Spatial Data Set Size

Geoff Boeing

No nzhdc, SocArXiv from Center for Open Science

Abstract: 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.

Date: 2018-03-22
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:nzhdc

DOI: 10.31219/osf.io/nzhdc

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