trun_neg_exp_profile: CrimeStat Truncated Negative Exponential Model for Geographic...

In JSSpaulding/rgeoprofile: Geographic Profiling Methods for Serial Crime Analysis

CrimeStat Truncated Negative Exponential Model for Geographic Profiling

Description

An implementation of the truncated negative exponential decay model for serial crime analysis within 'CrimeStat'. This is a joint function composed of both the linear and the negative exponential. For distances proximal to the incidents, a positive linear function is defined from zero likelihood at distance zero to a location of peak likelihood. At the peak likelihood the function takes the form of a negative exponential, rapidly declining as distance increases.

Usage

trun_neg_exp_profile(lat, lon, dp = NULL, peak_lh = NULL, c = NULL, n = NULL)

Arguments

lat	a vector of latitudes for the crime incident series
lon	a vector of latitudes for the crime incident series
dp	radial distance for the peak likelihood (cutoff distance). If NULL, the default value for 'dp' is 4.2 (Levine 2013)
peak_lh	peak likelihood for the distance decay function. If NULL, the default value for 'peak_lh' is 13.8 (Levine 2013)
c	exponential constant for the negative exponential decay function. If NULL, the default value for 'c' is -0.06 (Levine 2013)
n	total number of cells within the spatial grid for the jeopardy surface. If NULL, the default value for '*n*' is 40,000.

Value

A data frame of points depicting a spatial grid of the hunting area for the given incident locations. Also given are the resultant summed values (score) for each map point. A higher resultant score indicates a greater the probability that point contains the offender's anchor point.

Author(s)

Jamie Spaulding, Keith Morris

References

Ned Levine, CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 4.0). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC, June 2013.

Examples

#Using provided dataset for the Boston Strangler Incidents:
data(desalvo)
test <- trun_neg_exp_profile(desalvo$lat, desalvo$lon)
g_map = sp::SpatialPixelsDataFrame(points = test[c("lons", "lats")], data = test)
g_map <- raster::raster(g_map)
# Assign a Coordinate Reference System for the Raster
raster::crs(g_map) <- sp::CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# Define a Parula Color Pallete for Resultant Jeopardy Surface
library(leaflet) #for mapping the geographic profile
pal <- colorNumeric(pals::parula(200), raster::values(g_map),
    na.color = "transparent")
leaflet() %>%
    addTiles() %>%
    addProviderTiles('Esri.WorldTopoMap', group = 'Topo') %>%
    addAwesomeMarkers(lng = -71.07357, lat = 42.41322, icon =
        awesomeIcons(icon = 'home', markerColor = 'green'), popup = 'Residence') %>%
    addRasterImage(g_map, colors = pal, opacity = 0.6) %>%
    addLegend(pal = pal, values = raster::values(g_map), title = 'Score') %>%
    addCircleMarkers(lng = desalvo$lon, lat = desalvo$lat, radius = 4, opacity = 1,
        fill = 'black', stroke = TRUE, fillOpacity = 0.75, weight = 2,
        fillColor = "red")

JSSpaulding/rgeoprofile documentation built on Feb. 8, 2023, 7:06 p.m.