Visualizing Motor Vehicle Thefts in Chicago (2001 - 2012)

Objective

Visualize motor vehicle thefts in Chicago between January 1, 2001 and December 31, 2012.

About the Data

This data comes from the Chicago Police Department. It contains information about motor vehicle thefts in Chicago between January 1, 2001 and December 31, 2012.

Data Preparation

Loading and viewing data

> mvt <- read.csv('mvt.csv', stringsAsFactors=FALSE) > head(mvt) Date Latitude Longitude 1 12/31/12 23:15 41.75628 -87.62164 2 12/31/12 22:00 41.89879 -87.66130 3 12/31/12 22:00 41.96919 -87.76767 4 12/31/12 22:00 41.76933 -87.65773 5 12/31/12 21:30 41.83757 -87.62176 6 12/31/12 20:30 41.92856 -87.75400

2. Creating new columns with extracted information

> str(mvt)
'data.frame':	191641 obs. of  3 variables:
 $ Date     : chr  "12/31/12 23:15" "12/31/12 22:00" "12/31/12 22:00" "12/31/12 22:00" ...
 $ Latitude : num  41.8 41.9 42 41.8 41.8 ...
 $ Longitude: num  -87.6 -87.7 -87.8 -87.7 -87.6 ...
> 
> mvt$Date <- strptime(mvt$Date, format="%m/%d/%y %H:%M")
> range(mvt$Date)
[1] "1/1/01 0:01" "9/9/12 9:50"
>
> mvt$Year <- format(mvt$Date, '%Y')
> mvt$Month <- months(mvt$Date)
> mvt$Day <- weekdays(mvt$Date)
> mvt$Hour <- mvt$Date$hour
>
> head(mvt)
	     Date Latitude Longitude Year    Month    Day Hour
1 2012-12-31 23:15:00 41.75628 -87.62164 2012 December Monday   23
2 2012-12-31 22:00:00 41.89879 -87.66130 2012 December Monday   22
3 2012-12-31 22:00:00 41.96919 -87.76767 2012 December Monday   22
4 2012-12-31 22:00:00 41.76933 -87.65773 2012 December Monday   22
5 2012-12-31 21:30:00 41.83757 -87.62176 2012 December Monday   21
6 2012-12-31 20:30:00 41.92856 -87.75400 2012 December Monday   20

Visualizations Process: Line Graphs and Heat Map

Installing and loading 'ggplot2' package

> install.packages('ggplot2') > library(ggplot2)

Motor vehicle thefts by year (2001 - 2012)

> mvtByYear <- as.data.frame(table(mvt$Year)) 
> colnames(mvtByYear) <- c('Year', 'Count')
> mvtByYear$Year <- as.numeric(as.character(mvtByYear$Year))
>
> ggplot(mvtByYear, aes(x = Year, y = Count)) + 
+   geom_line(aes(group = 1), size = 2, alpha = 0.5, color = 'blue') + 
+   scale_x_continuous(breaks = 2001:2012) + 
+   theme(axis.title.x = element_blank()) + 
+   ggtitle('Motor Vehicle Thefts by Year (2001 - 2012)')

Motor vehicle thefts by month (2001 - 2012)

> mvtByMonth <- as.data.frame(table(mvt$Month))
> colnames(mvtByMonth) <- c('Month', 'Count')
> mvtByMonth$Month <- factor(mvtByMonth$Month, 
+                            levels = c('January', 'February', 'March', 'April',
+                                       'May', 'June', 'July', 'August', 'September',
+                                       'October', 'November', 'December'))
>
> ggplot(mvtByMonth, aes(x=Month, y=Count)) + 
>   geom_line(aes(group=1), size = 2, alpha = 0.5, color = 'red') + 
>   theme(axis.title.x = element_blank(), 
>	      axis.text.x = element_text(angle = 45, hjust = 1)) +
>   ggtitle('Motor Vehicle Thefts by Month (2001 - 2012)')

Motor vehicle thefts by day of the week (2001 - 2012)

> mvtByDay <- as.data.frame(table(mvt$Day))
> colnames(mvtByDay) <- c('Day', 'Count')
> mvtByDay$Day <- factor(mvtByDay$Day,
+                            levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday',
+                                       'Friday', 'Saturday', 'Sunday'))
>
> ggplot(mvtByDay, aes(x = Day, y = Count)) + 
+   geom_bar(aes(fill = Day), stat = 'identity') +
+   theme(axis.title.x = element_blank()) + 
+   ggtitle('Motor Vehicle Thefts by Day of the Week (2001 - 2012)')

Motor vehicle thefts by hour (2001 - 2012)

> mvtByHour <- as.data.frame(table(mvt$Hour))
> colnames(mvtByHour) <- c('Hour', 'Count')
> mvtByHour$Hour <- as.numeric(as.character(mvtByHour$Hour))
>
> ggplot(mvtByHour, aes(x=Hour, y=Count)) + 
+   geom_line(aes(group=1), size=2, alpha=0.5, color = 'darkgreen') +
+   ggtitle('Motor Vehicle Thefts by Hour (2001 - 2012)')

Motor vehicle thefts by month and year (heatmap)

> mvtByMonthYear <- as.data.frame(table(mvt$Month, mvt$Year))
> colnames(mvtByMonthYear) <- c('Month', 'Year', 'Count')
> mvtByMonthYear$Year <- as.numeric(as.character(mvtByMonthYear$Year))
> mvtByMonthYear$Month <- factor(mvtByMonthYear$Month, 
+                                levels = c('January', 'February', 'March', 'April',
+                                           'May', 'June', 'July', 'August', 'September',
+                                           'October', 'November', 'December'))
>
> ggplot(mvtByMonthYear, aes(x = Month, y = Year)) +
+   geom_tile(aes(fill = Count)) + 
+   theme(axis.title.x = element_blank(), 
+         axis.text.x = element_text(angle = 45, hjust = 1)) +
+   scale_fill_gradient(low = 'white', high = 'red') +
+   scale_y_continuous(breaks = 2001:2012) + 
+   ggtitle('Motor Vehicle Thefts by Month and Year (2001 - 2012)')

Motor vehicle thefts by hour and day of the week (heatmap)

> mvtByHourDay <- as.data.frame(table(mvt$Day, mvt$Hour))
> colnames(mvtByHourDay) <- c('Day', 'Hour', 'Count')
> mvtByHourDay$Day <- factor(mvtByHourDay$Day, 
+                                levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday',
+                                           'Friday', 'Saturday', 'Sunday'))
> mvtByHourDay$Hour <- as.numeric(as.character(mvtByHourDay$Hour))
>
> ggplot(mvtByHourDay, aes(x = Hour, y = Day)) + 
+   geom_tile(aes(fill = Count)) + 
+   scale_fill_gradient(name = 'Motor Vehicle Thefts',
+                       low = 'white', high = 'red') + 
+   theme(axis.title.y = element_blank()) + 
+   ggtitle('Motor Vehicle Thefts by Hour and Day of the Week (2001 - 2012)') +
+   scale_x_continuous(breaks = seq(from=0, to=23, by=2))

Visualization Process: Maps

Installing and loading 'maps' and 'ggmap' packages

> install.packages('maps')
> install.packages('ggmap')
> library(maps)
> library(ggmap)

Loading a map of Chicago and plotting

> chicago <- get_map(location = "chicago", zoom = 11)
> ggmap(chicago)

Plot all cases of motor vehicle thefts between 2001 and 2012 raw

> ggmap(chicago) + 
+   geom_point(data = mvt, 
+              aes(x = Longitude, y = Latitude))

3. Plot the first 1000 cases of motor vehicle thefts in the data frame

> ggmap(chicago) + 
+   geom_point(data = mvt[1:1000,], 
+              aes(x = Longitude, y = Latitude))

Create a data frame with theft counts by sub-regions

> mvtByLongLat <- as.data.frame(table(round(mvt$Longitude, 2), round(mvt$Latitude, 2)))  # round the longitude and latitude coordinates to 2 decimal places
> colnames(mvtByLongLat) <- c('Longitude', 'Latitude', 'Count')
> head(mvtByLongLat)
  Longitude Latitude Count
1    -87.93    41.64     0
2    -87.92    41.64     0
3    -87.91    41.64     0
4     -87.9    41.64     0
5    -87.89    41.64     0
6    -87.88    41.64     0

Convert the Longitude and Latitude variables to numberic variables

> str(mvtByLongLat) 'data.frame': 1638 obs. of 3 variables: $ Longitude: Factor w/ 42 levels "-87.93","-87.92",..: 1 2 3 4 5 6 7 8 9 10 ... $ Latitude : Factor w/ 39 levels "41.64","41.65",..: 1 1 1 1 1 1 1 1 1 1 ... $ Count : int 0 0 0 0 0 0 0 0 0 0 ... > mvtByLongLat$Longitude <- as.numeric(as.character(mvtByLongLat$Longitude)) > mvtByLongLat$Latitude <- as.numeric(as.character(mvtByLongLat$Latitude)) > head(mvtByLongLat) Longitude Latitude Count 1 -87.93 41.64 0 2 -87.92 41.64 0 3 -87.91 41.64 0 4 -87.90 41.64 0 5 -87.89 41.64 0 6 -87.88 41.64 0

Plot points on the Chicago map

> ggmap(chicago) + 
+   geom_point(data = mvtByLongLat,
+              aes(x = Longitude, y = Latitude, color = Count, size = Count))

Remove regions with 0 theft case and r-eplot with a better color scheme

> head(mvtByLongLat)
  Longitude Latitude Count
1    -87.93    41.64     0
2    -87.92    41.64     0
3    -87.91    41.64     0
4    -87.90    41.64     0
5    -87.89    41.64     0
6    -87.88    41.64     0
> mvtByLongLat <- subset(mvtByLongLat, Count != 0)
> head(mvtByLongLat)
   Longitude Latitude Count
33    -87.61    41.64     3
40    -87.54    41.64     1
74    -87.62    41.65    54
75    -87.61    41.65    60
76    -87.60    41.65    28
77    -87.59    41.65     1
>
> ggmap(chicago) + 
+   geom_point(data = mvtByLongLat,
+              aes(x = Longitude, y = Latitude, color = Count, size = Count)) + 
+   scale_color_gradient(low = 'white', high = 'red') +
+   xlab('Longitude') + 
+   ylab('Latitude') + 
+   ggtitle('Map of Motor Vehicle Thefts (2001 - 2012)')

Re-plot to create a map with heatmap rendition

> ggmap(chicago) + 
+   geom_tile(data = mvtByLongLat,
+             aes(x = Longitude, y = Latitude))

> ggmap(chicago) + 
+   geom_tile(data = mvtByLongLat,
+             aes(x = Longitude, y = Latitude, alpha = Count))

> ggmap(chicago) + 
+   geom_tile(data = mvtByLongLat,
+             aes(x = Longitude, y = Latitude, alpha = Count),
+             fill = 'red')

> ggmap(chicago) + 
+   geom_tile(data = mvtByLongLat,
+             aes(x = Longitude, y = Latitude, alpha = Count),
+             fill = 'red') + 
+   xlab('Longitude') + 
+   ylab('Latitude') +
+   ggtitle('Map of Motor Vehicle Thefts (2001 - 2012)')