Statistics, Department of

 

The R Journal

Date of this Version

12-2021

Document Type

Article

Citation

The R Journal (December 2021) 13(2); Editor: Dianne Cook

Comments

Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

One of the most common applications of spatial data analysis is detecting zones, at a certain scale, where a point-referenced event under study is especially concentrated. The detection of such zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology, or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is typical that hotspot detection procedures are entirely focused on the determination of zones where an event is (overall) highly concentrated. It is less common, by far, that such procedures focus on detecting zones where a specific type of event is overrepresented in comparison with the other types observed, which have been denoted as differential risk hotspots. The R package DRHotNet provides several functionalities to facilitate the detection of differential risk hotspots within a linear network. In this paper, DRHotNet is depicted, and its usage in the R console is shown through a detailed analysis of a crime dataset

Download

Share

COinS