rgeoprofile (version 0.2.2)

cgt_profile: Criminal Geographic Targeting Model for Geographic Profiling (Rossmo Formula)

Description

An implementation of the criminal geographic targeting model for serial crime analysis developed by DK Rossmo. This function applies Rossmo's distance decay formula to a series of suspected crime incidents for geographic profiling and prediction of perpetrator home base.

Usage

cgt_profile(lat, lon, buffer = NULL, f = NULL, g = NULL, n = NULL)

Arguments

lat

a vector of latitudes for the crime incident series

lon

a vector of latitudes for the crime incident series

buffer

the radius for the buffer zone assumed by the distance decay model.

f

decay formula coefficient which changes the steepness of the decay curve after the buffer radius. If NULL, the default value for '*f*' is 1.2 as recommended by Rossmo (1995)

g

decay formula coefficient which changes the steepness of the decay curve before the buffer radius. If NULL, the default value for '*g*' is 1.2 as recommended by Rossmo (1995)

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.

References

DK Rossmo (2000). Geographic profiling. Boca Raton, FL: CRC Press.

DK Rossmo (1995). Geographic profiling: Target patterns of serial murderers. Diss. Theses (School of Criminology)/Simon Fraser University.

Examples

# NOT RUN {
#Using provided dataset for the Boston Strangler Incidents:
data(desalvo)
test <- cgt_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")
# }