Analytics Edge: Unit 1 - An Analytical Detective

Background Information on the Dataset

Crime is an international concern, but it is documented and handled in very different ways in different countries. In the United States, violent crimes and property crimes are recorded by the Federal Bureau of Investigation (FBI). Additionally, each city documents crime, and some cities release data regarding crime rates. The city of Chicago, Illinois releases crime data from 2001 onward online.

Chicago is the third most populous city in the United States, with a population of over 2.7 million people. The city of Chicago is shown in the map below, with the state of Illinois highlighted in red.

There are two main types of crimes: violent crimes, and property crimes. In this problem, we’ll focus on one specific type of property crime, called “motor vehicle theft” (sometimes referred to as grand theft auto). This is the act of stealing, or attempting to steal, a car. In this problem, we’ll use some basic data analysis in R to understand the motor vehicle thefts in Chicago.

Please download the file mvtWeek1.csv. for this problem (do not open this file in any spreadsheet software before completing this problem because it might change the format of the Date field). Here is a list of descriptions of the variables:

ID: a unique identifier for each observation
Date: the date the crime occurred
LocationDescription: the location where the crime occurred
Arrest: whether or not an arrest was made for the crime (TRUE if an arrest was made, and FALSE if an arrest was not made) Domestic: whether or not the crime was a domestic crime, meaning that it was committed against a family member (TRUE if it was domestic, and FALSE if it was not domestic) Beat: the area, or “beat” in which the crime occurred. This is the smallest regional division defined by the Chicago police department. District: the police district in which the crime occurred. Each district is composed of many beats, and are defined by the Chicago Police Department.
CommunityArea: the community area in which the crime occurred. Since the 1920s, Chicago has been divided into what are called “community areas”, of which there are now 77. The community areas were devised in an attempt to create socially homogeneous regions.
Year: the year in which the crime occurred.
Latitude: the latitude of the location at which the crime occurred.
Longitude: the longitude of the location at which the crime occurred.

R Exercises

Read the dataset mvtWeek1.csv. into R, using the read.csv function, and call the data frame “mvt”. Remember to navigate to the directory on your computer containing the file mvtWeek1.csv first. It may take a few minutes to read in the data, since it is pretty large. Then, use the str and summary functions to answer the following questions.

# Load the dataset
mvt = read.csv("mvtWeek1.csv")

How many rows of data (observations) are in this dataset?

# Output the string
str(mvt)
## 'data.frame':    191641 obs. of  11 variables:
##  $ ID                 : int  8951354 8951141 8952745 8952223 8951608 8950793 8950760 8951611 8951802 8950706 ...
##  $ Date               : Factor w/ 131680 levels "1/1/01 0:01",..: 42824 42823 42823 42823 42822 42821 42820 42819 42817 42816 ...
##  $ LocationDescription: Factor w/ 78 levels "ABANDONED BUILDING",..: 72 72 62 72 72 72 72 72 72 72 ...
##  $ Arrest             : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
##  $ Domestic           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Beat               : int  623 1213 1622 724 211 2521 423 231 1021 1215 ...
##  $ District           : int  6 12 16 7 2 25 4 2 10 12 ...
##  $ CommunityArea      : int  69 24 11 67 35 19 48 40 29 24 ...
##  $ Year               : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
##  $ Latitude           : num  41.8 41.9 42 41.8 41.8 ...
##  $ Longitude          : num  -87.6 -87.7 -87.8 -87.7 -87.6 ...

If you type str(mvt) in the R console, the first row of output says that this is a data frame with 191,641 observations.

How many variables are in this dataset?

# Output the string
str(mvt)
## 'data.frame':    191641 obs. of  11 variables:
##  $ ID                 : int  8951354 8951141 8952745 8952223 8951608 8950793 8950760 8951611 8951802 8950706 ...
##  $ Date               : Factor w/ 131680 levels "1/1/01 0:01",..: 42824 42823 42823 42823 42822 42821 42820 42819 42817 42816 ...
##  $ LocationDescription: Factor w/ 78 levels "ABANDONED BUILDING",..: 72 72 62 72 72 72 72 72 72 72 ...
##  $ Arrest             : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
##  $ Domestic           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Beat               : int  623 1213 1622 724 211 2521 423 231 1021 1215 ...
##  $ District           : int  6 12 16 7 2 25 4 2 10 12 ...
##  $ CommunityArea      : int  69 24 11 67 35 19 48 40 29 24 ...
##  $ Year               : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
##  $ Latitude           : num  41.8 41.9 42 41.8 41.8 ...
##  $ Longitude          : num  -87.6 -87.7 -87.8 -87.7 -87.6 ...

If you type str(mvt) in the R console, the first row of output says that this is a data frame with 11 variables.

Using the “max” function, what is the maximum value of the variable “ID”?

# Find the maximum value 
max(mvt$ID)
## [1] 9181151

You can compute the maximum value of the ID variable with max(mvt$ID).

What is the minimum value of the variable “Beat”?

# Output the summary
z = summary(mvt) 
kable(z)

ID	Date	LocationDescription	Arrest	Domestic	Beat	District	CommunityArea	Year	Latitude	Longitude
Min. :1310022	5/16/08 0:00 : 11	STREET :156564	Mode :logical	Mode :logical	Min. : 111	Min. : 1.00	Min. : 0	Min. :2001	Min. :41.64	Min. :-87.93
1st Qu.:2832144	10/17/01 22:00: 10	PARKING LOT/GARAGE(NON.RESID.): 14852	FALSE:176105	FALSE:191226	1st Qu.: 722	1st Qu.: 6.00	1st Qu.:22	1st Qu.:2003	1st Qu.:41.77	1st Qu.:-87.72
Median :4762956	4/13/04 21:00 : 10	OTHER : 4573	TRUE :15536	TRUE :415	Median :1121	Median :10.00	Median :32	Median :2006	Median :41.85	Median :-87.68
Mean :4968629	9/17/05 22:00 : 10	ALLEY : 2308	NA	NA	Mean :1259	Mean :11.82	Mean :38	Mean :2006	Mean :41.84	Mean :-87.68
3rd Qu.:7201878	10/12/01 22:00: 9	GAS STATION : 2111	NA	NA	3rd Qu.:1733	3rd Qu.:17.00	3rd Qu.:60	3rd Qu.:2009	3rd Qu.:41.92	3rd Qu.:-87.64
Max. :9181151	10/13/01 22:00: 9	DRIVEWAY - RESIDENTIAL : 1675	NA	NA	Max. :2535	Max. :31.00	Max. :77	Max. :2012	Max. :42.02	Max. :-87.52
NA	(Other) :191582	(Other) : 9558	NA	NA	NA	NA’s :43056	NA’s :24616	NA	NA’s :2276	NA’s :2276

# Calculates the minimum
min(mvt$Beat)
## [1] 111

If you type summary(mvt) in your R console, you can see the summary statistics for each variable. This shows that the minimum value of Beat is 111. Alternatively, you could use the min function by typing min(mvt$Beat).

How many observations have value TRUE in the Arrest variable (this is the number of crimes for which an arrest was made)?

# Output the summary
z = summary(mvt) 
kable(z)

ID	Date	LocationDescription	Arrest	Domestic	Beat	District	CommunityArea	Year	Latitude	Longitude
Min. :1310022	5/16/08 0:00 : 11	STREET :156564	Mode :logical	Mode :logical	Min. : 111	Min. : 1.00	Min. : 0	Min. :2001	Min. :41.64	Min. :-87.93
1st Qu.:2832144	10/17/01 22:00: 10	PARKING LOT/GARAGE(NON.RESID.): 14852	FALSE:176105	FALSE:191226	1st Qu.: 722	1st Qu.: 6.00	1st Qu.:22	1st Qu.:2003	1st Qu.:41.77	1st Qu.:-87.72
Median :4762956	4/13/04 21:00 : 10	OTHER : 4573	TRUE :15536	TRUE :415	Median :1121	Median :10.00	Median :32	Median :2006	Median :41.85	Median :-87.68
Mean :4968629	9/17/05 22:00 : 10	ALLEY : 2308	NA	NA	Mean :1259	Mean :11.82	Mean :38	Mean :2006	Mean :41.84	Mean :-87.68
3rd Qu.:7201878	10/12/01 22:00: 9	GAS STATION : 2111	NA	NA	3rd Qu.:1733	3rd Qu.:17.00	3rd Qu.:60	3rd Qu.:2009	3rd Qu.:41.92	3rd Qu.:-87.64
Max. :9181151	10/13/01 22:00: 9	DRIVEWAY - RESIDENTIAL : 1675	NA	NA	Max. :2535	Max. :31.00	Max. :77	Max. :2012	Max. :42.02	Max. :-87.52
NA	(Other) :191582	(Other) : 9558	NA	NA	NA	NA’s :43056	NA’s :24616	NA	NA’s :2276	NA’s :2276

If you type summary(mvt) in your R console, you can see the summary statistics for each variable. This shows that 15,536 observations fall under the category TRUE for the variable Arrest.

How many observations have a LocationDescription value of ALLEY?

# Output the summary
z = summary(mvt) 
kable(z)

ID	Date	LocationDescription	Arrest	Domestic	Beat	District	CommunityArea	Year	Latitude	Longitude
Min. :1310022	5/16/08 0:00 : 11	STREET :156564	Mode :logical	Mode :logical	Min. : 111	Min. : 1.00	Min. : 0	Min. :2001	Min. :41.64	Min. :-87.93
1st Qu.:2832144	10/17/01 22:00: 10	PARKING LOT/GARAGE(NON.RESID.): 14852	FALSE:176105	FALSE:191226	1st Qu.: 722	1st Qu.: 6.00	1st Qu.:22	1st Qu.:2003	1st Qu.:41.77	1st Qu.:-87.72
Median :4762956	4/13/04 21:00 : 10	OTHER : 4573	TRUE :15536	TRUE :415	Median :1121	Median :10.00	Median :32	Median :2006	Median :41.85	Median :-87.68
Mean :4968629	9/17/05 22:00 : 10	ALLEY : 2308	NA	NA	Mean :1259	Mean :11.82	Mean :38	Mean :2006	Mean :41.84	Mean :-87.68
3rd Qu.:7201878	10/12/01 22:00: 9	GAS STATION : 2111	NA	NA	3rd Qu.:1733	3rd Qu.:17.00	3rd Qu.:60	3rd Qu.:2009	3rd Qu.:41.92	3rd Qu.:-87.64
Max. :9181151	10/13/01 22:00: 9	DRIVEWAY - RESIDENTIAL : 1675	NA	NA	Max. :2535	Max. :31.00	Max. :77	Max. :2012	Max. :42.02	Max. :-87.52
NA	(Other) :191582	(Other) : 9558	NA	NA	NA	NA’s :43056	NA’s :24616	NA	NA’s :2276	NA’s :2276

# Tabulates the division of observations for the LocationDescription variable
table(mvt$LocationDescription)
## 
##                              ABANDONED BUILDING AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA     AIRPORT BUILDING NON-TERMINAL - SECURE AREA              AIRPORT EXTERIOR - NON-SECURE AREA 
##                                               4                                               4                                               1                                              24 
##                  AIRPORT EXTERIOR - SECURE AREA                             AIRPORT PARKING LOT  AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA                   AIRPORT VENDING ESTABLISHMENT 
##                                               1                                              11                                               5                                              10 
##                                AIRPORT/AIRCRAFT                                           ALLEY                                 ANIMAL HOSPITAL                                       APARTMENT 
##                                             363                                            2308                                               1                                             184 
##                                 APPLIANCE STORE                                   ATHLETIC CLUB                                            BANK                                   BAR OR TAVERN 
##                                               1                                               9                                               7                                              17 
##                                      BARBERSHOP                                   BOWLING ALLEY                                          BRIDGE                                        CAR WASH 
##                                               4                                               3                                               2                                              44 
##                                   CHA APARTMENT                         CHA PARKING LOT/GROUNDS               CHURCH/SYNAGOGUE/PLACE OF WORSHIP                                  CLEANING STORE 
##                                               5                                             405                                              56                                               3 
##                      COLLEGE/UNIVERSITY GROUNDS               COLLEGE/UNIVERSITY RESIDENCE HALL                    COMMERCIAL / BUSINESS OFFICE                               CONSTRUCTION SITE 
##                                              47                                               2                                             126                                              35 
##                               CONVENIENCE STORE                     CTA GARAGE / OTHER PROPERTY                                       CTA TRAIN                               CURRENCY EXCHANGE 
##                                               7                                             148                                               1                                               2 
##                                 DAY CARE CENTER                                DEPARTMENT STORE                          DRIVEWAY - RESIDENTIAL                                      DRUG STORE 
##                                               5                                              22                                            1675                                               8 
##                  FACTORY/MANUFACTURING BUILDING                                    FIRE STATION                                 FOREST PRESERVE                                     GAS STATION 
##                                              16                                               5                                               6                                            2111 
##                    GOVERNMENT BUILDING/PROPERTY                              GROCERY FOOD STORE                              HIGHWAY/EXPRESSWAY                       HOSPITAL BUILDING/GROUNDS 
##                                              48                                              80                                              22                                             101 
##                                     HOTEL/MOTEL                         JAIL / LOCK-UP FACILITY                  LAKEFRONT/WATERFRONT/RIVERBANK                                         LIBRARY 
##                                             124                                               1                                               4                                               4 
##                           MEDICAL/DENTAL OFFICE                             MOVIE HOUSE/THEATER                                       NEWSSTAND                    NURSING HOME/RETIREMENT HOME 
##                                               3                                              18                                               1                                              21 
##                                           OTHER                 OTHER COMMERCIAL TRANSPORTATION               OTHER RAILROAD PROP / TRAIN DEPOT                                   PARK PROPERTY 
##                                            4573                                               8                                              28                                             255 
##                  PARKING LOT/GARAGE(NON.RESID.)                 POLICE FACILITY/VEH PARKING LOT                                       RESIDENCE                                RESIDENCE-GARAGE 
##                                           14852                                             266                                            1302                                            1176 
##                         RESIDENCE PORCH/HALLWAY                   RESIDENTIAL YARD (FRONT/BACK)                                      RESTAURANT                                SAVINGS AND LOAN 
##                                              18                                            1536                                              49                                               4 
##                       SCHOOL, PRIVATE, BUILDING                        SCHOOL, PRIVATE, GROUNDS                        SCHOOL, PUBLIC, BUILDING                         SCHOOL, PUBLIC, GROUNDS 
##                                              14                                              23                                             114                                             206 
##                                        SIDEWALK                              SMALL RETAIL STORE                            SPORTS ARENA/STADIUM                                          STREET 
##                                             462                                              33                                             166                                          156564 
##                             TAVERN/LIQUOR STORE                                         TAXICAB                                 VACANT LOT/LAND                              VEHICLE-COMMERCIAL 
##                                              14                                              21                                             985                                              23 
##                          VEHICLE NON-COMMERCIAL                                       WAREHOUSE 
##                                             817                                              17

If you type summary(mvt) in your R console, you can see the summary statistics for each variable. This shows that 2,308 observations fall under the category ALLEY for the variable LocationDescription. You can also read this from table(mvt$LocationDescription).

In what format are the entries in the variable Date?

# Examine the dataset
mvt$Date[1] 
## [1] 12/31/12 23:15
## 131680 Levels: 1/1/01 0:01 1/1/01 0:05 1/1/01 0:30 1/1/01 1:17 1/1/01 1:50 1/1/01 10:00 1/1/01 10:12 1/1/01 11:00 1/1/01 12:00 1/1/01 13:00 1/1/01 15:00 1/1/01 15:30 1/1/01 16:00 ... 9/9/12 9:50

If you type mvt$Date[1] in your R console, you can see that the first entry is 12/31/12 23:15. This must be in the format Month/Day/Year Hour:Minute.

Now, let’s convert these characters into a Date object in R. In your R console, type

# Convert data into date object
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))

This converts the variable “Date” into a Date object in R. Take a look at the variable DateConvert using the summary function.

What is the month and year of the median date in our dataset?

# Output the summary
summary(DateConvert)
##         Min.      1st Qu.       Median         Mean      3rd Qu.         Max. 
## "2001-01-01" "2003-07-10" "2006-05-21" "2006-08-23" "2009-10-24" "2012-12-31"

If you type summary(DateConvert), you can see that the median date is 2006-05-21.

Now, let’s extract the month and the day of the week, and add these variables to our data frame mvt. We can do this with two simple functions. Type the following commands in R:

# Extact the month and day of the week and add them to our date frame
mvt$Month = months(DateConvert)
mvt$Weekday = weekdays(DateConvert)

This creates two new variables in our data frame, Month and Weekday, and sets them equal to the month and weekday values that we can extract from the Date object. Lastly, replace the old Date variable with DateConvert by typing:

# Replace the old variable
mvt$Date = DateConvert

In which month did the fewest motor vehicle thefts occur?

# Tabulate the amount of motor vehicle thefs in each month
z = table(mvt$Month) 
kable(z)

Var1	Freq
April	15280
August	16572
December	16426
February	13511
January	16047
July	16801
June	16002
March	15758
May	16035
November	16063
October	17086
September	16060

If you type table(mvt$Month), you can see that the month with the smallest number of observations is February.

On which weekday did the most motor vehicle thefts occur?

# Tabulates the amount of mvt for each weekday
z = table(mvt$Weekday)
kable(z)

Var1	Freq
Friday	29284
Monday	27397
Saturday	27118
Sunday	26316
Thursday	27319
Tuesday	26791
Wednesday	27416

If you type table(mvt$Weekday), you can see that the weekday with the largest number of observations is Friday.

Which month has the largest number of motor vehicle thefts for which an arrest was made?

# Tabulates the number of mvts which an arrest was made for each month
z = table(mvt$Arrest,mvt$Month)
kable(z)

	April	August	December	February	January	July	June	March	May	November	October	September
FALSE	14028	15243	15029	12273	14612	15477	14772	14460	14848	14807	15744	14812
TRUE	1252	1329	1397	1238	1435	1324	1230	1298	1187	1256	1342	1248

If you type table(mvt$Arrest,mvt$Month), you can see that the largest number of observations with Arrest=TRUE occurs in the month of January.

Visualizing Crime Trends

First, let’s make a histogram of the variable Date. We’ll add an extra argument, to specify the number of bars we want in our histogram. In your R console, type

# Create a histogram
hist(mvt$Date, breaks=100)

In general, does it look like crime increases or decreases from 2002 - 2012?

While there is not a clear trend, it looks like crime generally decreases.

In general, does it look like crime increases or decreases from 2005 - 2008?

In this time period, there is a clear downward trend in crime.

In general, does it look like crime increases or decreases from 2009 - 2011?

In this time period, there is a clear upward trend in crime.

Now, lets see how arrests have changed over time by creating a boxplot

# Create a boxplot
boxplot(mvt$Date ~ mvt$Arrest)

Does it look like there were more crimes for which arrests were made in the first half of the time period or the second half of the time period?

If you look at the boxplot, the one for Arrest=TRUE is definitely skewed towards the bottom of the plot, meaning that there were more crimes for which arrests were made in the first half of the time period

For what proportion of motor vehicle thefts in 2001 was an arrest made?

# Tabulate the number of mvts which an arrest was made in each year
z = table(mvt$Arrest, mvt$Year)
kable(z)

	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
FALSE	18517	16638	14859	15169	14956	14796	13068	13425	11327	14796	15012	13542
TRUE	2152	2115	1798	1693	1528	1302	1212	1020	840	701	625	550

If you create a table using the command table(mvt$Arrest, mvt$Year), the column for 2001 has 2152 observations with Arrest=TRUE and 18517 observations with Arrest=FALSE. The fraction of motor vehicle thefts in 2001 for which an arrest was made is thus 2152/(2152+18517) = 0.1041173.

For what proportion of motor vehicle thefts in 2007 was an arrest made?

# Tabulate the amount of mvt in 2007 for which an arrest was made
z = table(mvt$Arrest, mvt$Year)
kable(z)

	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
FALSE	18517	16638	14859	15169	14956	14796	13068	13425	11327	14796	15012	13542
TRUE	2152	2115	1798	1693	1528	1302	1212	1020	840	701	625	550

If you create a table using the command table(mvt$Arrest, mvt$Year), the column for 2007 has 1212 observations with Arrest=TRUE and 13068 observations with Arrest=FALSE. The fraction of motor vehicle thefts in 2007 for which an arrest was made is thus 1212/(1212+13068) = 0.08487395.

For what proportion of motor vehicle thefts in 2012 was an arrest made?

# Tabulates the number of mvts for which an arrest was made in each year
z = table(mvt$Arrest, mvt$Year)
kable(z)

	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
FALSE	18517	16638	14859	15169	14956	14796	13068	13425	11327	14796	15012	13542
TRUE	2152	2115	1798	1693	1528	1302	1212	1020	840	701	625	550

If you create a table using the command table(mvt$Arrest, mvt$Year), the column for 2012 has 550 observations with Arrest=TRUE and 13542 observations with Arrest=FALSE. The fraction of motor vehicle thefts in 2012 for which an arrest was made is thus 550/(550+13542) = 0.03902924.

Since there may still be open investigations for recent crimes, this could explain the trend we are seeing in the data. There could also be other factors at play, and this trend should be investigated further. However, since we don’t know when the arrests were actually made, our detective work in this area has reached a dead end.

Popular Locations of Interest

We want to find the top five locations where motor vehicle thefts occur. If you create a table of the LocationDescription variable, it is unfortunately very hard to read since there are 78 different locations in the data set. By using the sort function, we can view this same table, but sorted by the number of observations in each category. In your R console, type:

# Sorts the tabulated data of the mvts in each location description
z = sort(table(mvt$LocationDescription))
kable(z)

Var1	Freq
AIRPORT BUILDING NON-TERMINAL - SECURE AREA	1
AIRPORT EXTERIOR - SECURE AREA	1
ANIMAL HOSPITAL	1
APPLIANCE STORE	1
CTA TRAIN	1
JAIL / LOCK-UP FACILITY	1
NEWSSTAND	1
BRIDGE	2
COLLEGE/UNIVERSITY RESIDENCE HALL	2
CURRENCY EXCHANGE	2
BOWLING ALLEY	3
CLEANING STORE	3
MEDICAL/DENTAL OFFICE	3
ABANDONED BUILDING	4
AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA	4
BARBERSHOP	4
LAKEFRONT/WATERFRONT/RIVERBANK	4
LIBRARY	4
SAVINGS AND LOAN	4
AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA	5
CHA APARTMENT	5
DAY CARE CENTER	5
FIRE STATION	5
FOREST PRESERVE	6
BANK	7
CONVENIENCE STORE	7
DRUG STORE	8
OTHER COMMERCIAL TRANSPORTATION	8
ATHLETIC CLUB	9
AIRPORT VENDING ESTABLISHMENT	10
AIRPORT PARKING LOT	11
SCHOOL, PRIVATE, BUILDING	14
TAVERN/LIQUOR STORE	14
FACTORY/MANUFACTURING BUILDING	16
BAR OR TAVERN	17
WAREHOUSE	17
MOVIE HOUSE/THEATER	18
RESIDENCE PORCH/HALLWAY	18
NURSING HOME/RETIREMENT HOME	21
TAXICAB	21
DEPARTMENT STORE	22
HIGHWAY/EXPRESSWAY	22
SCHOOL, PRIVATE, GROUNDS	23
VEHICLE-COMMERCIAL	23
AIRPORT EXTERIOR - NON-SECURE AREA	24
OTHER RAILROAD PROP / TRAIN DEPOT	28
SMALL RETAIL STORE	33
CONSTRUCTION SITE	35
CAR WASH	44
COLLEGE/UNIVERSITY GROUNDS	47
GOVERNMENT BUILDING/PROPERTY	48
RESTAURANT	49
CHURCH/SYNAGOGUE/PLACE OF WORSHIP	56
GROCERY FOOD STORE	80
HOSPITAL BUILDING/GROUNDS	101
SCHOOL, PUBLIC, BUILDING	114
HOTEL/MOTEL	124
COMMERCIAL / BUSINESS OFFICE	126
CTA GARAGE / OTHER PROPERTY	148
SPORTS ARENA/STADIUM	166
APARTMENT	184
SCHOOL, PUBLIC, GROUNDS	206
PARK PROPERTY	255
POLICE FACILITY/VEH PARKING LOT	266
AIRPORT/AIRCRAFT	363
CHA PARKING LOT/GROUNDS	405
SIDEWALK	462
VEHICLE NON-COMMERCIAL	817
VACANT LOT/LAND	985
RESIDENCE-GARAGE	1176
RESIDENCE	1302
RESIDENTIAL YARD (FRONT/BACK)	1536
DRIVEWAY - RESIDENTIAL	1675
GAS STATION	2111
ALLEY	2308
OTHER	4573
PARKING LOT/GARAGE(NON.RESID.)	14852
STREET	156564

Which locations are the top five locations for motor vehicle thefts, excluding the “Other” category?

These are Street, Parking Lot/Garage (Non. Resid.), Alley, Gas Station, and Driveway - Residential.

How many observations are in Top5?

Create a subset of the Top5 locations.

# Create a Top5 subset
Top5 = subset(mvt, LocationDescription=="STREET" | LocationDescription=="PARKING LOT/GARAGE(NON.RESID.)" | LocationDescription=="ALLEY" | LocationDescription=="GAS STATION" | LocationDescription=="DRIVEWAY - RESIDENTIAL")

One of the locations has a much higher arrest rate than the other locations. Which is it?

To make our tables a bit nicer to read, we can refresh this factor variable. In your R console, type:

# Create factor variabel
Top5$LocationDescription = factor(Top5$LocationDescription)
# Calculate the proportion
m = table(Top5$LocationDescription, Top5$Arrest)

z = prop.table(m,1)
kable(z)

	FALSE	TRUE
ALLEY	0.8921144	0.1078856
DRIVEWAY - RESIDENTIAL	0.9211940	0.0788060
GAS STATION	0.7920417	0.2079583
PARKING LOT/GARAGE(NON.RESID.)	0.8920684	0.1079316
STREET	0.9259408	0.0740592

Gas Station has by far the highest percentage of arrests, with over 20% of motor vehicle thefts resulting in an arrest.

On which day of the week do the most motor vehicle thefts at gas stations happen?

# Calculate the proportion
m = table(Top5$LocationDescription, Top5$Weekday)

z = prop.table(m,1)
kable(z)

	Friday	Monday	Saturday	Sunday	Thursday	Tuesday	Wednesday
ALLEY	0.1668111	0.1386482	0.1477470	0.1330156	0.1364818	0.1399480	0.1373484
DRIVEWAY - RESIDENTIAL	0.1534328	0.1522388	0.1205970	0.1319403	0.1570149	0.1450746	0.1397015
GAS STATION	0.1572714	0.1326386	0.1601137	0.1591663	0.1335860	0.1279015	0.1293226
PARKING LOT/GARAGE(NON.RESID.)	0.1569486	0.1432804	0.1480609	0.1303528	0.1401831	0.1395772	0.1415971
STREET	0.1518421	0.1424657	0.1416354	0.1389591	0.1424082	0.1398023	0.1428873

Saturday is the day where most motor vehicle thefts at gas stations occur.

On which day of the week do the fewest motor vehicle thefts in residential driveways happen?

# Calculate the proprotion
m = table(Top5$LocationDescription, Top5$Weekday)

z = prop.table(m,1)
kable(z)

	Friday	Monday	Saturday	Sunday	Thursday	Tuesday	Wednesday
ALLEY	0.1668111	0.1386482	0.1477470	0.1330156	0.1364818	0.1399480	0.1373484
DRIVEWAY - RESIDENTIAL	0.1534328	0.1522388	0.1205970	0.1319403	0.1570149	0.1450746	0.1397015
GAS STATION	0.1572714	0.1326386	0.1601137	0.1591663	0.1335860	0.1279015	0.1293226
PARKING LOT/GARAGE(NON.RESID.)	0.1569486	0.1432804	0.1480609	0.1303528	0.1401831	0.1395772	0.1415971
STREET	0.1518421	0.1424657	0.1416354	0.1389591	0.1424082	0.1398023	0.1428873

Saturday is the day where most motor vehicle thefts at residential driveways occur.