autoplot.hspt_c

Plot the output produced by <code>hotspot_classify</code> with reasonable
default values.

Identify and understand clusters of points (typically representing
the locations of places or events) stored in simple-features (SF) objects.
This is useful for analysing, for example, hot-spots of crime events. The
package emphasises producing results from point SF data in a single step
using reasonable default values for all other arguments, to aid rapid data
analysis by users who are starting out. Functions available include kernel
density estimation (for details, see Yip (2020)
<doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis
and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot
classification (Chainey (2020) ISBN:158948584X).

Matt Ashby

sfhotspot

Hot-Spot Analysis with Simple Features

autoplot.hspt_c function

<dl><dt>object</dt>
<dd>An object with the class <code>hspt_c</code>, e.g. as produced by
<code>hotspot_classify</code>.</dd>
<dt>...</dt>
<dd>Currently ignored, but may be used for further options in future.</dd></dl>

Arguments

Plot map of hotspot classifications — autoplot.hspt_c

<dl>

<dt>object</dt>
<dd>An object with the class <code>hspt_c</code>, e.g. as produced by
<code>hotspot_classify</code>.</dd>


<dt>...</dt>
<dd>Currently ignored, but may be used for further options in future.</dd>

</dl>

autoplot.hspt_c: Plot map of hotspot classifications

Description

Usage

Value

Arguments