memphis_robberies

A dataset containing records of personal robberies recorded by police in
Memphis, Tennessee, in 2019.

datasets

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

memphis_robberies function

A simple-features tibble with 2,245 rows and four variables:<dl>
<dt>uid</dt>
<dd>a unique identifier for each robbery</dd><dt>offense_type</dt>
<dd>the type of crime (always 'personal robbery')</dd><dt>date</dt>
<dd>the date and time at which the crime occurred</dd><dt>geometry</dt>
<dd>the co-ordinates at which the crime occurred, stored in
simple-features point format</dd>
</dl>

Format

Personal robberies in Memphis in 2019 — memphis_robberies

A simple-features tibble with 2,245 rows and four variables:<dl>
<dt>uid</dt>
<dd>a unique identifier for each robbery</dd>

<dt>offense_type</dt>
<dd>the type of crime (always 'personal robbery')</dd>

<dt>date</dt>
<dd>the date and time at which the crime occurred</dd>

<dt>geometry</dt>
<dd>the co-ordinates at which the crime occurred, stored in
simple-features point format</dd>


</dl>

memphis_robberies: Personal robberies in Memphis in 2019

Description

Usage

Arguments

Format