pois_contour: Checks the fit of a Poisson Distribution
In ptools: Tools for Poisson Data

pois_contour

R Documentation

Checks the fit of a Poisson Distribution

Description

Provides contours (for use in graphs) to show changes in Poisson counts in a pre vs post period.

Usage

pois_contour(
  pre_crime,
  post_crime,
  lev = c(-3, 0, 3),
  lr = 5,
  hr = max(pre_crime) * 1.05,
  steps = 1000
)

Arguments

`pre_crime,`	vector of crime counts in the pre period
`post_crime,`	vector of crime counts in the post period
`lev,`	vector of Poisson Z-scores to draw the contours at, defaults to `c(-3,0,3)`
`lr,`	scaler lower limit for where to draw the contour lines, defaults to `5`
`hr,`	scaler upper limit for where to draw the contour lines, defaults to `max(pre_crime)*1.05`
`steps,`	scaler how dense to make the lines, defaults to 1000 steps

Details

Provides a set of contour lines to show whether increases/decreases in Poisson counts between two periods are outside of those expected by chance according to the Poisson distribution based on the normal approximation. Meant to be used in subsequent graphs. Note the approximation breaks down at smaller N values, so below 5 is not typically recommended.

Value

A dataframe with columns

x, the integer value
y, the y-value in the graph for expected changes (will not be below 0)
levels, the associated Z-score level

References

Drake, G., Wheeler, A., Kim, D.-Y., Phillips, S. W., & Mendolera, K. (2021). The Impact of COVID-19 on the Spatial Distribution of Shooting Violence in Buffalo, NY. CrimRxiv. https://doi.org/10.21428/cb6ab371.e187aede

Examples

# Example use with NYC Shooting Data pre/post Covid lockdowns
# Prepping the NYC shooting data
data(nyc_shoot)
begin_date <- as.Date('03/01/2020', format="%m/%d/%Y")
nyc_shoot$Pre <- ifelse(nyc_shoot$OCCUR_DATE < begin_date,1,0)
nyc_shoot$Post <- nyc_shoot$Pre*-1 + 1
# Note being lazy, some of these PCTs have changed over time
pct_tot <- aggregate(cbind(Pre,Post) ~ PRECINCT, data=nyc_shoot@data, FUN=sum)
cont_lines <- pois_contour(pct_tot$Pre,pct_tot$Post)
# Now making an ugly graph
sp <- split(cont_lines,cont_lines$levels)
plot(pct_tot$Pre,pct_tot$Post)
for (s in sp){
  lines(s$x,s$y,lty=2)
}
# Can see it is slightly overdispersed, but pretty close!
# See https://andrewpwheeler.com/2021/02/02/the-spatial-dispersion-of-nyc-shootings-in-2020/
# For a nicer example using ggplot

ptools documentation built on Feb. 16, 2023, 10:40 p.m.