Analytics Edge: Unit 1 - An Analytical Detective
Sulman Khan
October 24, 2018
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 occured. 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.
mvt = read.csv("mvtWeek1.csv")How many rows of data (observations) are in this dataset?
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 ...Explanation: 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?
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 ...Explanation: 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”?
max(mvt$ID)
## [1] 9181151Explanation: You can compute the maximum value of the ID variable with max(mvt$ID).
What is the minimum value of the variable “Beat”?
summary(mvt)
## ID Date LocationDescription Arrest Domestic Beat District
## Min. :1310022 5/16/08 0:00 : 11 STREET :156564 Mode :logical Mode :logical Min. : 111 Min. : 1.00
## 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
## Median :4762956 4/13/04 21:00 : 10 OTHER : 4573 TRUE :15536 TRUE :415 Median :1121 Median :10.00
## Mean :4968629 9/17/05 22:00 : 10 ALLEY : 2308 Mean :1259 Mean :11.82
## 3rd Qu.:7201878 10/12/01 22:00: 9 GAS STATION : 2111 3rd Qu.:1733 3rd Qu.:17.00
## Max. :9181151 10/13/01 22:00: 9 DRIVEWAY - RESIDENTIAL : 1675 Max. :2535 Max. :31.00
## (Other) :191582 (Other) : 9558 NA's :43056
## CommunityArea Year Latitude Longitude
## Min. : 0 Min. :2001 Min. :41.64 Min. :-87.93
## 1st Qu.:22 1st Qu.:2003 1st Qu.:41.77 1st Qu.:-87.72
## Median :32 Median :2006 Median :41.85 Median :-87.68
## Mean :38 Mean :2006 Mean :41.84 Mean :-87.68
## 3rd Qu.:60 3rd Qu.:2009 3rd Qu.:41.92 3rd Qu.:-87.64
## Max. :77 Max. :2012 Max. :42.02 Max. :-87.52
## NA's :24616 NA's :2276 NA's :2276
min(mvt$Beat)
## [1] 111Explanation: If you type summary(mvt) in your R console, you can see the summary statistics for each variable. This shows that the minimum value of Beat is 111. Alternatively, you could use the min function by typing min(mvt$Beat).
How many observations have value TRUE in the Arrest variable (this is the number of crimes for which an arrest was made)?
summary(mvt)
## ID Date LocationDescription Arrest Domestic Beat District
## Min. :1310022 5/16/08 0:00 : 11 STREET :156564 Mode :logical Mode :logical Min. : 111 Min. : 1.00
## 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
## Median :4762956 4/13/04 21:00 : 10 OTHER : 4573 TRUE :15536 TRUE :415 Median :1121 Median :10.00
## Mean :4968629 9/17/05 22:00 : 10 ALLEY : 2308 Mean :1259 Mean :11.82
## 3rd Qu.:7201878 10/12/01 22:00: 9 GAS STATION : 2111 3rd Qu.:1733 3rd Qu.:17.00
## Max. :9181151 10/13/01 22:00: 9 DRIVEWAY - RESIDENTIAL : 1675 Max. :2535 Max. :31.00
## (Other) :191582 (Other) : 9558 NA's :43056
## CommunityArea Year Latitude Longitude
## Min. : 0 Min. :2001 Min. :41.64 Min. :-87.93
## 1st Qu.:22 1st Qu.:2003 1st Qu.:41.77 1st Qu.:-87.72
## Median :32 Median :2006 Median :41.85 Median :-87.68
## Mean :38 Mean :2006 Mean :41.84 Mean :-87.68
## 3rd Qu.:60 3rd Qu.:2009 3rd Qu.:41.92 3rd Qu.:-87.64
## Max. :77 Max. :2012 Max. :42.02 Max. :-87.52
## NA's :24616 NA's :2276 NA's :2276Explanation: 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?
summary(mvt)
## ID Date LocationDescription Arrest Domestic Beat District
## Min. :1310022 5/16/08 0:00 : 11 STREET :156564 Mode :logical Mode :logical Min. : 111 Min. : 1.00
## 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
## Median :4762956 4/13/04 21:00 : 10 OTHER : 4573 TRUE :15536 TRUE :415 Median :1121 Median :10.00
## Mean :4968629 9/17/05 22:00 : 10 ALLEY : 2308 Mean :1259 Mean :11.82
## 3rd Qu.:7201878 10/12/01 22:00: 9 GAS STATION : 2111 3rd Qu.:1733 3rd Qu.:17.00
## Max. :9181151 10/13/01 22:00: 9 DRIVEWAY - RESIDENTIAL : 1675 Max. :2535 Max. :31.00
## (Other) :191582 (Other) : 9558 NA's :43056
## CommunityArea Year Latitude Longitude
## Min. : 0 Min. :2001 Min. :41.64 Min. :-87.93
## 1st Qu.:22 1st Qu.:2003 1st Qu.:41.77 1st Qu.:-87.72
## Median :32 Median :2006 Median :41.85 Median :-87.68
## Mean :38 Mean :2006 Mean :41.84 Mean :-87.68
## 3rd Qu.:60 3rd Qu.:2009 3rd Qu.:41.92 3rd Qu.:-87.64
## Max. :77 Max. :2012 Max. :42.02 Max. :-87.52
## NA's :24616 NA's :2276 NA's :2276
table(mvt$LocationDescription)
##
## ABANDONED BUILDING AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA AIRPORT BUILDING NON-TERMINAL - SECURE AREA
## 4 4 1
## AIRPORT EXTERIOR - NON-SECURE AREA AIRPORT EXTERIOR - SECURE AREA AIRPORT PARKING LOT
## 24 1 11
## AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA AIRPORT VENDING ESTABLISHMENT AIRPORT/AIRCRAFT
## 5 10 363
## ALLEY ANIMAL HOSPITAL APARTMENT
## 2308 1 184
## APPLIANCE STORE ATHLETIC CLUB BANK
## 1 9 7
## BAR OR TAVERN BARBERSHOP BOWLING ALLEY
## 17 4 3
## BRIDGE CAR WASH CHA APARTMENT
## 2 44 5
## CHA PARKING LOT/GROUNDS CHURCH/SYNAGOGUE/PLACE OF WORSHIP CLEANING STORE
## 405 56 3
## COLLEGE/UNIVERSITY GROUNDS COLLEGE/UNIVERSITY RESIDENCE HALL COMMERCIAL / BUSINESS OFFICE
## 47 2 126
## CONSTRUCTION SITE CONVENIENCE STORE CTA GARAGE / OTHER PROPERTY
## 35 7 148
## CTA TRAIN CURRENCY EXCHANGE DAY CARE CENTER
## 1 2 5
## DEPARTMENT STORE DRIVEWAY - RESIDENTIAL DRUG STORE
## 22 1675 8
## FACTORY/MANUFACTURING BUILDING FIRE STATION FOREST PRESERVE
## 16 5 6
## GAS STATION GOVERNMENT BUILDING/PROPERTY GROCERY FOOD STORE
## 2111 48 80
## HIGHWAY/EXPRESSWAY HOSPITAL BUILDING/GROUNDS HOTEL/MOTEL
## 22 101 124
## JAIL / LOCK-UP FACILITY LAKEFRONT/WATERFRONT/RIVERBANK LIBRARY
## 1 4 4
## MEDICAL/DENTAL OFFICE MOVIE HOUSE/THEATER NEWSSTAND
## 3 18 1
## NURSING HOME/RETIREMENT HOME OTHER OTHER COMMERCIAL TRANSPORTATION
## 21 4573 8
## OTHER RAILROAD PROP / TRAIN DEPOT PARK PROPERTY PARKING LOT/GARAGE(NON.RESID.)
## 28 255 14852
## POLICE FACILITY/VEH PARKING LOT RESIDENCE RESIDENCE-GARAGE
## 266 1302 1176
## RESIDENCE PORCH/HALLWAY RESIDENTIAL YARD (FRONT/BACK) RESTAURANT
## 18 1536 49
## SAVINGS AND LOAN SCHOOL, PRIVATE, BUILDING SCHOOL, PRIVATE, GROUNDS
## 4 14 23
## SCHOOL, PUBLIC, BUILDING SCHOOL, PUBLIC, GROUNDS SIDEWALK
## 114 206 462
## SMALL RETAIL STORE SPORTS ARENA/STADIUM STREET
## 33 166 156564
## TAVERN/LIQUOR STORE TAXICAB VACANT LOT/LAND
## 14 21 985
## VEHICLE-COMMERCIAL VEHICLE NON-COMMERCIAL WAREHOUSE
## 23 817 17Explanation: 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?
mvt$Date[1]
## [1] 12/31/12 23:15
## 131680 Levels: 1/1/01 0:01 1/1/01 0:05 1/1/01 0:30 1/1/01 1:17 1/1/01 1:50 1/1/01 10:00 1/1/01 10:12 1/1/01 11:00 1/1/01 12:00 1/1/01 13:00 ... 9/9/12 9:50Explanation: If you type mvt$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
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))This converts the variable “Date” into a Date object in R. Take a look at the variable DateConvert using the summary function.
What is the month and year of the median date in our dataset?
summary(DateConvert)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2001-01-01" "2003-07-10" "2006-05-21" "2006-08-23" "2009-10-24" "2012-12-31"Explanation: If you type summary(DateConvert), you can see that the median date is 2006-05-21.
Now, let’s extract the month and the day of the week, and add these variables to our data frame mvt. We can do this with two simple functions. Type the following commands in R:
mvt$Month = months(DateConvert)
mvt$Weekday = weekdays(DateConvert)This creates two new variables in our data frame, Month and Weekday, and sets them equal to the month and weekday values that we can extract from the Date object. Lastly, replace the old Date variable with DateConvert by typing:
mvt$Date = DateConvertIn which month did the fewest motor vehicle thefts occur?
table(mvt$Month)
##
## April August December February January July June March May November October September
## 15280 16572 16426 13511 16047 16801 16002 15758 16035 16063 17086 16060Explanation: If you type table(mvt$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?
table(mvt$Weekday)
##
## Friday Monday Saturday Sunday Thursday Tuesday Wednesday
## 29284 27397 27118 26316 27319 26791 27416Explanation: If you type table(mvt$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?
table(mvt$Arrest,mvt$Month)
##
## 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 1248Explanation: 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
hist(mvt$Date, breaks=100)
In general, does it look like crime increases or decreases from 2002 - 2012?
While there is not a clear trend, it looks like crime generally decreases.
In general, does it look like crime increases or decreases from 2005 - 2008?
In this time period, there is a clear downward trend in crime.
In general, does it look like crime increases or decreases from 2009 - 2011?
In this time period, there is a clear upward trend in crime.
Now, lets see how arrests have changed over time by creating a boxplot
boxplot(mvt$Date ~ mvt$Arrest)
Does it look like there were more crimes for which arrests were made in the first half of the time period or the second half of the time period?
If you look at the boxplot, the one for Arrest=TRUE is definitely skewed towards the bottom of the plot, meaning that there were more crimes for which arrests were made in the first half of the time period
For what proportion of motor vehicle thefts in 2001 was an arrest made?
table(mvt$Arrest, mvt$Year)
##
## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## FALSE 18517 16638 14859 15169 14956 14796 13068 13425 11327 14796 15012 13542
## TRUE 2152 2115 1798 1693 1528 1302 1212 1020 840 701 625 550Explanation: If you create a table using the command table(mvt$Arrest, mvt$Year), the column for 2001 has 2152 observations with Arrest=TRUE and 18517 observations with Arrest=FALSE. The fraction of motor vehicle thefts in 2001 for which an arrest was made is thus 2152/(2152+18517) = 0.1041173.
For what proportion of motor vehicle thefts in 2007 was an arrest made?
table(mvt$Arrest, mvt$Year)
##
## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## FALSE 18517 16638 14859 15169 14956 14796 13068 13425 11327 14796 15012 13542
## TRUE 2152 2115 1798 1693 1528 1302 1212 1020 840 701 625 550Explanation: If you create a table using the command table(mvt$Arrest, mvt$Year), the column for 2007 has 1212 observations with Arrest=TRUE and 13068 observations with Arrest=FALSE. The fraction of motor vehicle thefts in 2007 for which an arrest was made is thus 1212/(1212+13068) = 0.08487395.
For what proportion of motor vehicle thefts in 2012 was an arrest made?
table(mvt$Arrest, mvt$Year)
##
## 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 550Explanation: 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:
sort(table(mvt$LocationDescription))
##
## AIRPORT BUILDING NON-TERMINAL - SECURE AREA AIRPORT EXTERIOR - SECURE AREA ANIMAL HOSPITAL
## 1 1 1
## APPLIANCE STORE CTA TRAIN JAIL / LOCK-UP FACILITY
## 1 1 1
## NEWSSTAND BRIDGE COLLEGE/UNIVERSITY RESIDENCE HALL
## 1 2 2
## CURRENCY EXCHANGE BOWLING ALLEY CLEANING STORE
## 2 3 3
## MEDICAL/DENTAL OFFICE ABANDONED BUILDING AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA
## 3 4 4
## BARBERSHOP LAKEFRONT/WATERFRONT/RIVERBANK LIBRARY
## 4 4 4
## SAVINGS AND LOAN AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA CHA APARTMENT
## 4 5 5
## DAY CARE CENTER FIRE STATION FOREST PRESERVE
## 5 5 6
## BANK CONVENIENCE STORE DRUG STORE
## 7 7 8
## OTHER COMMERCIAL TRANSPORTATION ATHLETIC CLUB AIRPORT VENDING ESTABLISHMENT
## 8 9 10
## AIRPORT PARKING LOT SCHOOL, PRIVATE, BUILDING TAVERN/LIQUOR STORE
## 11 14 14
## FACTORY/MANUFACTURING BUILDING BAR OR TAVERN WAREHOUSE
## 16 17 17
## MOVIE HOUSE/THEATER RESIDENCE PORCH/HALLWAY NURSING HOME/RETIREMENT HOME
## 18 18 21
## TAXICAB DEPARTMENT STORE HIGHWAY/EXPRESSWAY
## 21 22 22
## SCHOOL, PRIVATE, GROUNDS VEHICLE-COMMERCIAL AIRPORT EXTERIOR - NON-SECURE AREA
## 23 23 24
## OTHER RAILROAD PROP / TRAIN DEPOT SMALL RETAIL STORE CONSTRUCTION SITE
## 28 33 35
## CAR WASH COLLEGE/UNIVERSITY GROUNDS GOVERNMENT BUILDING/PROPERTY
## 44 47 48
## RESTAURANT CHURCH/SYNAGOGUE/PLACE OF WORSHIP GROCERY FOOD STORE
## 49 56 80
## HOSPITAL BUILDING/GROUNDS SCHOOL, PUBLIC, BUILDING HOTEL/MOTEL
## 101 114 124
## COMMERCIAL / BUSINESS OFFICE CTA GARAGE / OTHER PROPERTY SPORTS ARENA/STADIUM
## 126 148 166
## APARTMENT SCHOOL, PUBLIC, GROUNDS PARK PROPERTY
## 184 206 255
## POLICE FACILITY/VEH PARKING LOT AIRPORT/AIRCRAFT CHA PARKING LOT/GROUNDS
## 266 363 405
## SIDEWALK VEHICLE NON-COMMERCIAL VACANT LOT/LAND
## 462 817 985
## RESIDENCE-GARAGE RESIDENCE RESIDENTIAL YARD (FRONT/BACK)
## 1176 1302 1536
## DRIVEWAY - RESIDENTIAL GAS STATION ALLEY
## 1675 2111 2308
## OTHER PARKING LOT/GARAGE(NON.RESID.) STREET
## 4573 14852 156564Which 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.
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:
Top5$LocationDescription = factor(Top5$LocationDescription)
m = table(Top5$LocationDescription, Top5$Arrest)
prop.table(m,1)
##
## FALSE TRUE
## ALLEY 0.89211438 0.10788562
## DRIVEWAY - RESIDENTIAL 0.92119403 0.07880597
## GAS STATION 0.79204169 0.20795831
## PARKING LOT/GARAGE(NON.RESID.) 0.89206841 0.10793159
## STREET 0.92594083 0.07405917Gas Station has by far the highest percentage of arrests, with over 20% of motor vehicle thefts resulting in an arrest.
On which day of the week do the most motor vehicle thefts at gas stations happen?
m = table(Top5$LocationDescription, Top5$Weekday)
prop.table(m,1)
##
## Friday Monday Saturday Sunday Thursday Tuesday Wednesday
## ALLEY 0.1668111 0.1386482 0.1477470 0.1330156 0.1364818 0.1399480 0.1373484
## DRIVEWAY - RESIDENTIAL 0.1534328 0.1522388 0.1205970 0.1319403 0.1570149 0.1450746 0.1397015
## GAS STATION 0.1572714 0.1326386 0.1601137 0.1591663 0.1335860 0.1279015 0.1293226
## PARKING LOT/GARAGE(NON.RESID.) 0.1569486 0.1432804 0.1480609 0.1303528 0.1401831 0.1395772 0.1415971
## STREET 0.1518421 0.1424657 0.1416354 0.1389591 0.1424082 0.1398023 0.1428873Saturday is the day where most motor vehicle thefts at gas stations occur.
On which day of the week do the fewest motor vehicle thefts in residential driveways happen?
m = table(Top5$LocationDescription, Top5$Weekday)
prop.table(m,1)
##
## 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 0.1428873Saturday is the day where most motor vehicle thefts at residential driveways occur.