sf.crime.2012

2012 crime incident data from the city of San Francisco

datasets

Facilitates efficient polygon search using kd trees.
Coordinate level spatial data can be aggregated to higher geographical
identities like census blocks, ZIP codes or police district boundaries.
This process requires mapping each point in the given data set to a
particular identity of the desired geographical hierarchy. Unless efficient
data structures are used, this can be a daunting task. The operation
point.in.polygon() from the package sp is computationally expensive.
Here, we exploit kd-trees as efficient nearest neighbor search algorithm
to dramatically reduce the effective number of polygons being searched.

Markus Loecher

RapidPolygonLookup

POLYGON LOOKUP USING KD TREES

sf.crime.2012 function

A data frame with 20,000 randomly selected observations with the following variables and their types:<dl class="dl-horizontal">
	<dt><code>Date</code></dt><dd>character</dd>
	<dt><code>X</code></dt><dd>numeric</dd>
	<dt><code>Y</code></dt><dd>numeric</dd>
	<dt><code>violent</code></dt><dd>Factor</dd>
 </dl>

Format

Sample data with lat/long information — sf.crime.2012

A data frame with 20,000 randomly selected observations with the following variables and their types:<dl class='dl-horizontal'>
	<dt><code>Date</code></dt><dd>character</dd>
	<dt><code>X</code></dt><dd>numeric</dd>
	<dt><code>Y</code></dt><dd>numeric</dd>
	<dt><code>violent</code></dt><dd>Factor</dd>
 </dl>

sf.crime.2012: Sample data with lat/long information

Description

Usage

Arguments

Format

Details

Examples