Clustering to Reduce Spatial Data Set Size

TL;DR

This paper presents a machine learning-based clustering method to compress large spatial datasets by reducing redundancy, enabling more efficient analysis and visualization of spatial features.

Contribution

It introduces a density-based clustering approach specifically designed to reduce spatial data size by identifying representative features, addressing data redundancy issues.

Findings

Effective reduction of spatial data size

Preservation of key spatial features

Improved data processing efficiency

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.