Crime and Collision in The City of Angels

For one of my UCI research papers I was exploring R's "ggmap" package for spatial visualization of Particulate Matter emissions in the Ports of Long Beach and Los Angeles area. "ggmap" is pretty easy to learn and makes beautiful maps. As a weeknight project I decided to spatially visualize Crime and Collision in Los Angeles, CA. Raw LAPD crime and collison data for 2013 is available here.

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# Install required packages
install.packages('ggmap')
install.packages('ggplot2')

library(ggmap)
library(ggplot2)

setwd('/Users/Ankoor/Desktop/ML with R/LA Crime')

# Read Data
crime <- read.csv("LAPD_Crime_and_Collision_Raw_Data_for_2013.csv", stringsAsFactors = FALSE)

Latitude and Longitude data is in string format: "(34.0496, -118.265)". Need to clean this column by removing "(" and ")" then splitting the string "34.0496, -118.265" into separate Longitude and Latitude columns based on comma and single space: ", "

# Cleaning Location Column to extract Longitude and Latitude
crime$Location <- gsub("\\(|\\)", "", crime$Location.1)
temp_1 <- sapply(strsplit(crime$Location, ", ", fixed = TRUE), "[",1)
temp_2 <- sapply(strsplit(crime$Location, ", ", fixed = TRUE), "[",2)
crime$Lat <- as.numeric(temp_1)
crime$Long <- as.numeric(temp_2)

# Keeping necessary Columns
names(crime)
keep <- c('DATE.OCC', 'TIME.OCC', 'AREA.NAME', 'Crm.Cd','Crm.Cd.Desc', 'Lat', 'Long')
crime <- crime[keep]


Date is in string format: "12/31/2013". Need to convert date from "string" to "date" format used in R and then get weekdays.

# Cleaning Date Column
crime$DATE.OCC <- as.Date(crime$DATE.OCC, "%m/%d/%Y")
crime$Day <- weekdays(crime$DATE.OCC)

Time data is in 24-hour format (Military time). To visualize temporal variation in crimes I decided to assign Time in 4 quarters of a day as follows: 0 to 600 hours = First Quarter, 601 to 1200 hours = Second Quarter, 1201 to 1800 hours = Third Quarter and 1801 to 2400 hours = Fourth Quarter

# Quarters in a day

crime$Quarter <- crime$TIME.OCC

crime$Quarter[which(crime$TIME.OCC < 600)] <- 'First'

crime$Quarter[which(crime$TIME.OCC >= 600 & crime$TIME.OCC < 1200)] <- 'Second'

crime$Quarter[which(crime$TIME.OCC >= 1200 & crime$TIME.OCC < 1800)] <- 'Third'

crime$Quarter[which(crime$TIME.OCC >= 1800)] <- 'Fourth'

Now creating maps

# Get Longitude and Latitude 
geocode("Los Angeles") 

# Get Google Map
LA = c(lon = -118.2437, lat =  34.05223)
LA.map = get_map(location = LA, zoom = 11, maptype = 'terrain')

# Plotting Crime Density Map
ggmap(LA.map, extent = "normal", maprange=FALSE) %+% crime + aes(x = Long, y = Lat) + 
        stat_density2d(aes(fill = ..level.., alpha = ..level..), size = 5, bins = 20, geom = 'polygon') + 
        scale_fill_continuous(low = 'black', high = 'red', name = "Crime\nDensity") +
        scale_alpha(range = c(0.05, 0.25), guide = FALSE) + 
        coord_map(projection = "mercator", 
                  xlim = c(attr(LA.map, "bb")$ll.lon, attr(LA.map, "bb")$ur.lon), 
                  ylim = c(attr(LA.map, "bb")$ll.lat, attr(LA.map, "bb")$ur.lat)) + 
        theme(legend.justification=c(1,0), legend.position=c(1,0),axis.title = element_blank(), text = element_text(size = 14)) 
       

Vehicle Collision/Accident Maps

# Creating subset of Crime data based on Crime Code Description for Collision
collision <- subset(crime, Crm.Cd.Desc == 'TRAFFIC DR #')
names(collision)[5]<-"Collision"

# Get Stamen Map
LA.map = qmap(location = LA, zoom = 11, source = "stamen", maptype = 'toner')

# Plotting Collision Map (I used color = #cb181d" from Color Brewer)
LA.map + geom_point(data = collision, aes(x = Long, y = Lat), size = 2, alpha = 0.1, color = "#cb181d")

# Plotting Collision Map to Visualize Weekday Variation in Collisions
LA.map + geom_point(data = collision, aes(x = Long, y = Lat), size = 2, alpha = 0.1, color = "#0c2c84") + facet_wrap(~ Day)

# Plotting Collision Map to Visualize Temporal (Quarter based) Variation in Collisions
LA.map + geom_point(data = collision, aes(x = Long, y = Lat), size = 2, alpha = 0.1, color = "#0c2c84") + facet_wrap(~ Quarter)

# Plotting Collision Density Map
geocode("Hollywood") 
LA = c(lon = -118.3287, lat =  34.09281)
LA.map = get_map(location = LA, zoom = 11, maptype = 'terrain')

ggmap(LA.map, extent = "normal", maprange=FALSE) %+% collision + aes(x = Long, y = Lat) + 
        stat_density2d(aes(fill = ..level.., alpha = ..level..), size = 2, bins = 15, geom = 'polygon') + 
        scale_fill_gradient(low = "red", high = "#081d58", name = "Collision\nDensity") + 
        scale_alpha(range = c(0.05, 0.3), guide = FALSE) + 
        coord_map(projection = "mercator", 
                  xlim = c(attr(LA.map, "bb")$ll.lon, attr(LA.map, "bb")$ur.lon), 
                  ylim = c(attr(LA.map, "bb")$ll.lat, attr(LA.map, "bb")$ur.lat)) + 
        theme(legend.justification=c(1,0), legend.position=c(1,0),axis.title = element_blank(), text = element_text(size = 14))

# Creating subset of Crime data based on Crime Code Description for Violent Crimes
violent <- subset(crime, Crm.Cd.Desc == 'ROBBERY' | Crm.Cd.Desc == 'ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT' |
                          Crm.Cd.Desc == 'RAPE, ATTEMPTED' | Crm.Cd.Desc == 'CRIMINAL HOMICIDE' | 
                          Crm.Cd.Desc == 'CRIMINAL HOMICIDE' | Crm.Cd.Desc == 'ASSAULT WITH DEADLY WEAPON ON POLICE OFFICER' |
                                 Crm.Cd.Desc == 'RAPE, FORCIBLE' | Crm.Cd.Desc == 'HOMICIDE (NON-UCR)')

names(violent)[5] <-"Violent"

violent$Violent <- factor(violent$Violent)


# Plotting Violent Crime Density Map
geocode("Vernon, CA")
LA = c(lon = -118.2301, lat =  34.0039)
LA.map = get_map(location = LA, zoom = 12, maptype = 'terrain')
ggmap(LA.map, extent = "normal", maprange=FALSE) %+% violent + aes(x = Long, y = Lat) + 
        stat_density2d(aes(fill = ..level.., alpha = ..level..), size = 2, bins = 10, geom = 'polygon') + 
        scale_fill_gradient(low = "black", high = "red", name = "Violent Crime\nDensity") + 
        scale_alpha(range = c(0.05, 0.3), guide = FALSE) + 
        coord_map(projection = "mercator", 
                  xlim = c(attr(LA.map, "bb")$ll.lon, attr(LA.map, "bb")$ur.lon), 
                  ylim = c(attr(LA.map, "bb")$ll.lat, attr(LA.map, "bb")$ur.lat)) + 
        theme(legend.justification=c(1,0), legend.position=c(1,0),axis.title = element_blank(), text = element_text(size = 14)) 
     

# Plotting Collision Map to Visualize Weekday Variation in Violent Crimes

LA.map = qmap(location = LA, zoom = 11, source = "stamen", maptype = 'toner')

LA.map + geom_point(data = violent, aes(x = Long, y = Lat), size = 2, alpha = 0.1, color = "red") + facet_wrap(~ Day)