'Aoristic' is one of the past tenses in Greek and represents an uncertain occurrence time. Aoristic analysis suggested by Ratcliffe (2002) is a method to analyze events that do not have exact times of occurrence but have starting times and ending times. For example, a property crime database (e.g., burglary) typically has a starting time and ending time of the crime that could have occurred. Aoristic analysis allocates the probability of a crime incident occurring at every hour over a 24-hour period. The probability is aggregated over a study area to create an aoristic graph. Using crime incident data with lat/lon, DateTimeFrom, and DateTimeTo, functions in this package create a total of three (3) kml files and corresponding aoristic graphs: 1) density and contour; 2) grid count; and 3) shapefile boundary. (see also: https://sites.google.com/site/georgekick/software)
|Date of publication||2015-01-10 12:06:14|
|Maintainer||George Kikuchi <firstname.lastname@example.org>|
|License||GPL (>= 2)|
aoristic: Sample data of crime (df) and council district (spdf)
aoristic.all.graph: Creating a data frame for an aoristic graph using all data
aoristic.density: aoristic graph by grid count
aoristic.df: creating a data.frame for aoristic analysis
aoristic.grid: aoristic graph by grid count
aoristic-package: Creating a kml file with aoristic graph: Aoristic Analysis...
aoristic.shp: aoristic graph by shapefile boundary
aoristic.spdf: creating a spatial.polygon.data.frame for aoristic analysis
arlington: arlington burglary incident data (data frame)
CouncilDistrict: Council district (spatial polygon data frame)
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