Example data from the Chicago Data Portal

Description

A sample dataset of crime incidents in Chicago, IL from 2017-2019.

Usage

crimes

Format

A data frame with 25000 rows and 22 variables.

id

Unique identifier for the record.

case_number

The Chicago Police Department Records Division Number, which is unique to the incident.

date

Date when the incident occurred.

block

Partially redacted address where the incident occurred.

iucr

Illinois Unifrom Crime Reporting code (directly linked to primary_type and description)

primary_type

The primary description of the IUCR code.

description

The secondary description of the IUCR code, a subcategory of the primary description.

location_description

Description of the location where the incident occurred.

arrest

Indicates whether an arrest was made.

domestic

Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

beat

Indicates the police beat where the incident occurred.

district

Indicates the police district where the incident occurred.

ward

The ward (City Council district) where the incident occurred.

community_area

Indicates the community area where the incident occurred.

fbi_code

Indicates the National Incident-Based Reporting System (NIBRS) crime classification.

x_coordinate

X coordinate of the incident location (State Plane Illinois East NAD 1983 projection).

y_coordinate

Y coordinate of the incident location (State Plane Illinois East NAD 1983 projection).

year

Year the incident occurred.

updated_on

Date and time the record was last updated.

latitude

The latitude of the location where the incident occurred.

longitude

The longitude of the location where the incident occurred.

location

Concatenation of latitude and longitude.

Source

https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2/data

Batch Geocoding of Physical Addresses using the Google Maps API

Description

Geocodes a location (determines latitude and longitude from physical address) using the Google Maps API. Note that the Google Maps API requires registered credentials (Google Cloud Platform), see the ggmap package for more details at https://github.com/dkahle/ggmap. Note that when using this function you are agreeing to the Google Maps API Terms of Service at https://cloud.google.com/maps-platform/terms/.

Usage

geocode_address(location)

Arguments

Value

Returns a two column matrix with the latitude and longitude of each location queried.

Author(s)

Jamie Spaulding, Keith Morris

Examples

library(ggmap) #needed to register Google Cloud Credentials
register_google("**Google Cloud Credentials Here**")
addresses <- c("Milan Puskar Stadium, Morgantown, WV","Woodburn Hall, Morgantown, WV")
geocode_address(addresses)

Identify Repeat Crime Incidents

Description

This function identifies crime incidents which occur at the same location and returns a list of such incidents where each data frame in the list contains the RMS data for the repeat crime incidents. The data is based on the Chicago Police Department RMS structure.

Usage

id_repeat(data)

Arguments

Value

A list where each data frame contains repeat crime incidents for a given location.

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
crimes <- head(crimes, n = 1000)
out <- id_repeat(crimes)

Comparison of KDE Maps Across Specified Time Intervals

Description

This function calculates and compares the kernel density estimate (heat maps) of crime incident locations from two given intervals. The function returns a net difference raster which illustrates net changes between the spatial crime distributions across the specified intervals.

Usage

kde_int_comp(data, start1, end1, start2, end2)

Arguments

Value

Returns a shiny.tag.list object which contains three leaflet widgets: a widget with the calculated KDE from interval 1, a widget with the calculated KDE from interval 2, and a widget with a raster of the net differences between the KDE (heat maps) of each specified interval.

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
int_out <- kde_int_comp(crimes, start1="1/1/2017", end1="3/1/2017",
                                start2="1/1/2018", end2="3/1/2018")

Kernel Density Estimation and Heat Map Generation for Crime Incidents

Description

This function computes a kernel density estimate of crime incident locations and returns a 'Leaflet' map of the incidents. The data is based on the Chicago Police Department RMS structure and populates pop-up windows with the incident location for each incident.

Usage

kde_map(data, pts = NULL)

Arguments

Value

A Leaflet map with three layers: an 'ESRI' base-map, all crime incidents plotted (with incident info pop-up windows), and a kernel density estimate of those points.

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
crimes <- head(crimes, 1000)
library('leaflet') # needed to install basemap providers
kde_map(crimes)

Near Repeat Analysis of Crime Incidents with Crime Linkage Output

Description

This function performs near repeat analysis for a set of incident locations. The user specifies distance and time thresholds which are utilized to search all other incidents and find other near repeat incidents. From this an adjacency matrix is created for incidents which are related under the thresholds. The adjacency matrix is then used to create an igraph graph which illustrates potentially related or linked incidents (under the near repeat thresholds).

Usage

near_repeat_analysis(
  data,
  epsg,
  dist_thresh = NULL,
  time_thresh = NULL,
  tz = NULL
)

Arguments

Value

Returns a list of all near repeat series identified within the input data as igraph graph objects. This list can be used to generate plots of each series and to discern the near repeat linkages between the crime incidents.

Author(s)

Jamie Spaulding, Keith Morris

Examples

data(crimes)
nr_data <- head(crimes, n = 1000) #truncate dataset for near repeat analysis
out <- near_repeat_analysis(data=nr_data,tz="America/Chicago",epsg="32616")

Identification of Optimal Time and Distance Parameters for Near Repeat Analysis

Description

This function performs an evaluation of given crime incidents to reccomend parameters for near repeat analysis. A series of time and distance parameters are tested using a full factorial design using the set of incident locations to determine the frequency of occurrence given each set of parameters. The results of the full factorial assessment are then modeled through interpolation and the second derivative is calculated to determine the inflection point. The inflection point represents the change in frequency of detected incidents which near repeat. Determination of the inflection point is completed for both the time and distance domains.

Usage

near_repeat_eval(data, epsg, tz = NULL)

Arguments

Value

Returns a data frame with one instance (row) of two fields (columns). The fields are: distance and time. The instance indicates the optimal near repeat parameters for each. Note that distance is given in meters and time is given as days.

Author(s)

Jamie Spaulding, Keith Morris

Examples

data(crimes)
nr_dat <- subset(crimes, crimes$primary_type == "BURGLARY")
pars <- near_repeat_eval(data=nr_dat, tz="America/Chicago", epsg="32616")
pars

Time Series Forecast and Decomposition for Daily Crime Data

Description

This function transforms daily crime count data and plots the resultant components of a time series which has been decomposed into seasonal, trend, and irregular components using Loess smoothing. Holt Winters exponential smoothing is also performed for inproved trend resolution since data is in a daily format.

Usage

ts_daily_decomp(data, start)

Arguments

Value

Returns an object of class "stl" with the following components:

time.series: a multiple time series with columns seasonal, trend and remainder.

weights: the final robust weights (all one if fitting is not done robustly).

call: the matched call.

win: integer (length 3 vector) with the spans used for the "s", "t", and "l" smoothers.

deg: integer (length 3) vector with the polynomial degrees for these smoothers.

jump: integer (length 3) vector with the 'jumps' (skips) used for these smoothers.

inner: number of inner iterations

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
test <- ts_daily_decomp(data = crimes, start = c(2017, 1, 1))
plot(test)

Time Series Forecast for Daily Crime Data

Description

This function transforms traditional crime data into a time series and forecasts future incident counts based on the input data over a specified duration. The forecast is computed using simple exponential smoothing with additive errors. Returned is a plot of the time series, trend, and the upper and lower prediction limits for the forecast.

Usage

ts_forecast(data, start, duration = NULL)

Arguments

Value

Returns a plot of the time series entered (black), a forecast over the specified duration (blue), the exponentially smoothed trend for both the input data (red) and forecast (orange), and the upper and lower bounds for the prediction interval (grey).

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
ts_forecast(crimes, start = c(2017, 1, 1))

Time Series Decomposition for Monthly Crime Data

Description

This function transforms traditional crime data and plots the resultant components of a time series which has been decomposed into seasonal, trend and irregular components using Loess smoothing.

Usage

ts_month_decomp(data, start)

Arguments

Value

Returns an object of class "stl" with the following components:

time.series: a multiple time series with columns seasonal, trend and remainder.

weights: the final robust weights (all one if fitting is not done robustly).

call: the matched call.

win: integer (length 3 vector) with the spans used for the "s", "t", and "l" smoothers.

deg: integer (length 3) vector with the polynomial degrees for these smoothers.

jump: integer (length 3) vector with the 'jumps' (skips) used for these smoothers.

inner: number of inner iterations

Author(s)

Jamie Spaulding, Keith Morris

Examples

#Using provided dataset from Chicago Data Portal:
data(crimes)
test <- ts_month_decomp(crimes, 2017)
plot(test)