# Clustering to Reduce Spatial Data Set Size

arXiv: https://arxiv.org/abs/1803.08101 · HTML: https://mlsanity.com/paper/1803.08101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Predicting Airbnb Rental Prices Using Multiple Feature Modalities](https://mlsanity.com/paper/2112.06430/) (2021)

## Abstract (arXiv)

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.