Results of nearly all stops made by the Minneapolis Police Department for the year 2017.
A data frame with 51857 observations on the following 14 variables.
idNum
character vector of incident identifiers
date
a POSIXlt date variable giving the date and time of the stop
problem
a factor with levels suspicious
for suspicious vehicle or person stops and traffic
for traffic stops
citationIssued
a factor with levels no
yes
indicating if a citation was issued
personSearch
a factor with levels no
yes
indicating if the stopped person was searched
vehicleSearch
a factor with levels no
or yes
indicating if a vehicle was searched
preRace
a factor with levels white
, black
, east african
, latino
, native american
, asian
, other
, unknown
for the officer's assessment of race of the person stopped before speaking with the person stopped
race
a factor with levels white
, black
, east african
, latino
, native american
, asian
, other
, unknown
, officer's determination of race after the incident
gender
a factor with levels female
, male
, unknown
, gender of person stopped
lat
latitude of the location of the incident, somewhat rounded
long
latitude of the location of the incident, somewhat rounded
policePrecinct
Minneapolis Police Precinct number
neighborhood
a factor with 84 levels giving the name of the Minneapolis neighborhood of the incident
MDC
a factor with levels mdc
for data collected via in-vehicle computer, and other
for data submitted by officers not in a vehicle, either on foot, bicycle or horseback. Several of the variables above were recorded only in-vehicle
A few stops have been deleted, either because thesu location data was missing, or a few very rare categories were also removed. The data frame MplsDemo
contains 2015 demongraphic data on Minneapolis neighborhoods, using the same neighborhood names as this data file. Demographics are available for 84 of Minneaolis' 87 neighborhoods. The remaining 3 presumably have no housing.
# NOT RUN {
summary(MplsStops)
# }
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