Kernel Density Estimation with Ripley’s Circumferential Correction

The revised version of the paper Kernel Density Estimation with Ripley’s Circumferential Correction is now online, on hal.archives-ouvertes.fr/.

In this paper, we investigate (and extend) Ripley’s circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique — based of properties of Gaussian kernels — to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.

There are new applications, and new graphs, too

Most of the codes can be found on https://github.com/ripleyCorr/Kernel_density_ripley (as well as datasets).



Cite this blog post
ewen (2014, October 21). Kernel Density Estimation with Ripley’s Circumferential Correction. Freakonometrics. Retrieved May 23, 2024, from https://doi.org/10.58079/ouxb

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.