R Project - Baltimore Crime Data
Ryan O’Keefe
2026-02-23
Introduction
For my R Project, I elected to examine Baltimore Crime Data. I was interested in looking at such a relevant dataset to our city, and looking at real-world metrics to increase my own level of knowledge of the criminal activity that occurs here. I created 5 different visualizations in order to help identify trends and patterns that could be of assistance to local police departments in order to combat the level of crime that occurs.
Dataset
Prior to working with the data, I first had to ensure that the dataset was first downloaded, and then cleaned up. The code to do so is as follows.
## [1] "RowID" "CCNumber" "CrimeDateTime" "CrimeCode"
## [5] "Description" "Inside_Outside" "Weapon" "Post"
## [9] "Gender" "Age" "Race" "Ethnicity"
## [13] "Location" "Old_District" "New_District" "Neighborhood"
## [17] "Latitude" "Longitude" "GeoLocation" "PremiseType"
## [21] "Total_Incidents" "x" "y"## RowID CCNumber CrimeDateTime CrimeCode Description Inside_Outside
## <int> <char> <char> <char> <char> <char>
## 1: 1 14I10336 8/29/2014 12:00:00 PM 5A BURGLARY <NA>
## 2: 2 14H13075 8/29/2014 11:45:00 PM 4E COMMON ASSAULT <NA>
## 3: 3 14H13066 8/29/2014 11:10:00 PM 6G LARCENY <NA>
## 4: 4 14I00593 8/29/2014 7:00:00 AM 6J LARCENY <NA>
## 5: 5 14H13360 8/29/2014 11:30:00 PM 6G LARCENY <NA>
## 6: 6 14H12804 8/29/2014 11:25:00 AM 4E COMMON ASSAULT <NA>
## Weapon Post Gender Age Race Ethnicity
## <char> <char> <char> <int> <char> <char>
## 1: <NA> 835 M 52 UNKNOWN <NA>
## 2: <NA> 515 F 24 BLACK_OR_AFRICAN_AMERICAN <NA>
## 3: <NA> 114 F 44 BLACK_OR_AFRICAN_AMERICAN <NA>
## 4: <NA> 732 <NA> NA UNKNOWN <NA>
## 5: <NA> 213 F 26 WHITE <NA>
## 6: <NA> 412 F 30 BLACK_OR_AFRICAN_AMERICAN <NA>
## Location Old_District New_District Neighborhood Latitude
## <char> <char> <char> <char> <num>
## 1: 2700 W FAIRMOUNT AVE SOUTHWEST N/A SHIPLEY HILL 39.28835
## 2: 500 E 38TH ST NORTHERN N/A WAVERLY 39.33551
## 3: 200 PARK AVE CENTRAL N/A DOWNTOWN 39.29255
## 4: 1700 GWYNNS FALLS PKWY WESTERN N/A PARKVIEW/WOODBROOK 39.31681
## 5: 900 S BOND ST SOUTHEAST N/A FELLS POINT 39.28080
## 6: 1600 ROUNDHILL RD NORTHEAST N/A HILLEN 39.33810
## Longitude GeoLocation PremiseType
## <num> <char> <char>
## 1: -76.66073 (39.288352111548775,-76.660731200606335) ROW/TOWNHOUSE-VAC
## 2: -76.60790 (39.335514488267016,-76.60789683728612) ROW/TOWNHOUSE-OCC
## 3: -76.61825 (39.292552601970897,-76.618254674552134) BAR
## 4: -76.64989 (39.316813840390076,-76.649889876952656) STREET
## 5: -76.59475 (39.28080478810265,-76.594749640946659) BAR
## 6: -76.59101 (39.338103415204927,-76.591011276689429) APT/CONDO - OCCUPIED
## Total_Incidents x y
## <int> <num> <num>
## 1: 1 -76.66073 39.28835
## 2: 1 -76.60790 39.33551
## 3: 1 -76.61825 39.29255
## 4: 1 -76.64989 39.31681
## 5: 1 -76.59475 39.28080
## 6: 1 -76.59101 39.33810## [1] 492759 23## Classes 'data.table' and 'data.frame': 492759 obs. of 23 variables:
## $ RowID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CCNumber : chr "14I10336" "14H13075" "14H13066" "14I00593" ...
## $ CrimeDateTime : chr "8/29/2014 12:00:00 PM" "8/29/2014 11:45:00 PM" "8/29/2014 11:10:00 PM" "8/29/2014 7:00:00 AM" ...
## $ CrimeCode : chr "5A" "4E" "6G" "6J" ...
## $ Description : chr "BURGLARY" "COMMON ASSAULT" "LARCENY" "LARCENY" ...
## $ Inside_Outside : chr NA NA NA NA ...
## $ Weapon : chr NA NA NA NA ...
## $ Post : chr "835" "515" "114" "732" ...
## $ Gender : chr "M" "F" "F" NA ...
## $ Age : int 52 24 44 NA 26 30 61 72 27 61 ...
## $ Race : chr "UNKNOWN" "BLACK_OR_AFRICAN_AMERICAN" "BLACK_OR_AFRICAN_AMERICAN" "UNKNOWN" ...
## $ Ethnicity : chr NA NA NA NA ...
## $ Location : chr "2700 W FAIRMOUNT AVE" "500 E 38TH ST" "200 PARK AVE" "1700 GWYNNS FALLS PKWY" ...
## $ Old_District : chr "SOUTHWEST" "NORTHERN" "CENTRAL" "WESTERN" ...
## $ New_District : chr "N/A" "N/A" "N/A" "N/A" ...
## $ Neighborhood : chr "SHIPLEY HILL" "WAVERLY" "DOWNTOWN" "PARKVIEW/WOODBROOK" ...
## $ Latitude : num 39.3 39.3 39.3 39.3 39.3 ...
## $ Longitude : num -76.7 -76.6 -76.6 -76.6 -76.6 ...
## $ GeoLocation : chr "(39.288352111548775,-76.660731200606335)" "(39.335514488267016,-76.60789683728612)" "(39.292552601970897,-76.618254674552134)" "(39.316813840390076,-76.649889876952656)" ...
## $ PremiseType : chr "ROW/TOWNHOUSE-VAC" "ROW/TOWNHOUSE-OCC" "BAR" "STREET" ...
## $ Total_Incidents: int 1 1 1 1 1 1 1 1 1 1 ...
## $ x : num -76.7 -76.6 -76.6 -76.6 -76.6 ...
## $ y : num 39.3 39.3 39.3 39.3 39.3 ...
## - attr(*, ".internal.selfref")=<externalptr>## RowID CCNumber CrimeDateTime CrimeCode
## Min. : 1 Length:492759 Length:492759 Length:492759
## 1st Qu.:123190 Class :character Class :character Class :character
## Median :246379 Mode :character Mode :character Mode :character
## Mean :246379
## 3rd Qu.:369569
## Max. :492758
##
## Description Inside_Outside Weapon Post
## Length:492759 Length:492759 Length:492759 Length:492759
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Gender Age Race Ethnicity
## Length:492759 Min. :-7979.00 Length:492759 Length:492759
## Class :character 1st Qu.: 26.00 Class :character Class :character
## Mode :character Median : 35.00 Mode :character Mode :character
## Mean : 37.91
## 3rd Qu.: 49.00
## Max. : 8251.00
## NA's :91339
## Location Old_District New_District Neighborhood
## Length:492759 Length:492759 Length:492759 Length:492759
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Latitude Longitude GeoLocation PremiseType
## Min. :-76.66 Min. :-76.73 Length:492759 Length:492759
## 1st Qu.: 39.29 1st Qu.:-76.65 Class :character Class :character
## Median : 39.30 Median :-76.61 Mode :character Mode :character
## Mean : 38.95 Mean :-75.92
## 3rd Qu.: 39.33 3rd Qu.:-76.59
## Max. : 39.38 Max. : 39.29
## NA's :1245 NA's :1245
## Total_Incidents x y
## Min. :1 Min. :-76.73 Min. :-76.66
## 1st Qu.:1 1st Qu.:-76.65 1st Qu.: 39.29
## Median :1 Median :-76.61 Median : 39.30
## Mean :1 Mean :-75.92 Mean : 38.95
## 3rd Qu.:1 3rd Qu.:-76.59 3rd Qu.: 39.33
## Max. :1 Max. : 39.29 Max. : 39.38
## NA's :1 NA's :1245 NA's :1245## RowID CCNumber CrimeDateTime CrimeCode Description
## 0 1 1 1 1
## Inside_Outside Weapon Post Gender Age
## 448348 354081 5756 77229 91339
## Race Ethnicity Location Old_District New_District
## 32544 382168 2839 5560 111
## Neighborhood Latitude Longitude GeoLocation PremiseType
## 6527 1245 1245 1 40198
## Total_Incidents x y
## 1 1245 1245## RowID CCNumber CrimeDateTime CrimeCode Description
## 0 0 0 0 0
## Inside_Outside Weapon Post Gender Age
## 0 0 0 0 0
## Race Ethnicity Location Old_District New_District
## 0 0 0 0 0
## Neighborhood Latitude Longitude GeoLocation PremiseType
## 0 0 0 0 0
## Total_Incidents x y
## 0 0 0## RowID CCNumber CrimeDateTime CrimeCode Description
## <int> <char> <char> <char> <char>
## 1: 138203 17G12975 5/1/2011 12:00:00 AM 2A RAPE
## 2: 143811 23J07748 9/22/1984 12:00:00 AM 2A RAPE
## 3: 170189 24L06195 12/21/2024 5:00:00 AM 4E COMMON ASSAULT
## 4: 170252 24L06198 12/21/2024 5:31:00 AM 4E COMMON ASSAULT
## 5: 170257 24L05168 12/17/2024 8:00:00 PM 4A AGG. ASSAULT
## 6: 170259 24L05170 12/17/2024 11:30:00 PM 4A AGG. ASSAULT
## Inside_Outside Weapon Post Gender Age
## <char> <char> <char> <char> <int>
## 1: I Y 333 F 26
## 2: I Y 713 F 3
## 3: I PERSONAL_WEAPONS 815 F 66
## 4: I PERSONAL_WEAPONS 821 F 49
## 5: I OTHER 721 F 10
## 6: I KNIFE_CUTTING_INSTRUMENT 224 M 39
## Race Ethnicity Location
## <char> <char> <char>
## 1: BLACK_OR_AFRICAN_AMERICAN NOT_HISPANIC_OR_LATINO 2700 E CHASE ST
## 2: BLACK_OR_AFRICAN_AMERICAN NOT_HISPANIC_OR_LATINO 1500 SHIELDS PL
## 3: BLACK_OR_AFRICAN_AMERICAN UNKNOWN 4300 PARKTON ST
## 4: BLACK_OR_AFRICAN_AMERICAN NOT_HISPANIC_OR_LATINO 400 N EDGEWOOD ST
## 5: BLACK_OR_AFRICAN_AMERICAN UNKNOWN 2400 WINCHESTER ST
## 6: BLACK_OR_AFRICAN_AMERICAN NOT_HISPANIC_OR_LATINO 500 N GLOVER ST
## Old_District New_District Neighborhood Latitude Longitude
## <char> <char> <char> <num> <num>
## 1: EASTERN N/A BIDDLE STREET 39.30376 -76.57921
## 2: WESTERN N/A UPTON 39.30150 -76.63468
## 3: N/A SOUTHWEST YALE HEIGHTS 39.27768 -76.68637
## 4: N/A SOUTHWEST ALLENDALE 39.29200 -76.67550
## 5: N/A WESTERN BRIDGEVIEW/GREENLAWN 39.30163 -76.65664
## 6: N/A SOUTHEAST MCELDERRY PARK 39.29761 -76.58001
## GeoLocation PremiseType Total_Incidents x y
## <char> <char> <int> <num> <num>
## 1: (39.303764,-76.579207) OTHER/RESIDENTIAL 1 -76.57921 39.30376
## 2: (39.301503,-76.634683) OTHER/RESIDENTIAL 1 -76.63468 39.30150
## 3: (39.277684,-76.686368) OTHER/RESIDENTIAL 1 -76.68637 39.27768
## 4: (39.291997,-76.675504) OTHER/RESIDENTIAL 1 -76.67550 39.29200
## 5: (39.301633,-76.656637) OTHER/RESIDENTIAL 1 -76.65664 39.30163
## 6: (39.297611,-76.580009) OTHER/RESIDENTIAL 1 -76.58001 39.29761## [1] 17784 23## Classes 'data.table' and 'data.frame': 17784 obs. of 23 variables:
## $ RowID : int 138203 143811 170189 170252 170257 170259 170261 170262 170265 170328 ...
## $ CCNumber : chr "17G12975" "23J07748" "24L06195" "24L06198" ...
## $ CrimeDateTime : chr "5/1/2011 12:00:00 AM" "9/22/1984 12:00:00 AM" "12/21/2024 5:00:00 AM" "12/21/2024 5:31:00 AM" ...
## $ CrimeCode : chr "2A" "2A" "4E" "4E" ...
## $ Description : chr "RAPE" "RAPE" "COMMON ASSAULT" "COMMON ASSAULT" ...
## $ Inside_Outside : chr "I" "I" "I" "I" ...
## $ Weapon : chr "Y" "Y" "PERSONAL_WEAPONS" "PERSONAL_WEAPONS" ...
## $ Post : chr "333" "713" "815" "821" ...
## $ Gender : chr "F" "F" "F" "F" ...
## $ Age : int 26 3 66 49 10 39 12 21 33 37 ...
## $ Race : chr "BLACK_OR_AFRICAN_AMERICAN" "BLACK_OR_AFRICAN_AMERICAN" "BLACK_OR_AFRICAN_AMERICAN" "BLACK_OR_AFRICAN_AMERICAN" ...
## $ Ethnicity : chr "NOT_HISPANIC_OR_LATINO" "NOT_HISPANIC_OR_LATINO" "UNKNOWN" "NOT_HISPANIC_OR_LATINO" ...
## $ Location : chr "2700 E CHASE ST" "1500 SHIELDS PL" "4300 PARKTON ST" "400 N EDGEWOOD ST" ...
## $ Old_District : chr "EASTERN" "WESTERN" "N/A" "N/A" ...
## $ New_District : chr "N/A" "N/A" "SOUTHWEST" "SOUTHWEST" ...
## $ Neighborhood : chr "BIDDLE STREET" "UPTON" "YALE HEIGHTS" "ALLENDALE" ...
## $ Latitude : num 39.3 39.3 39.3 39.3 39.3 ...
## $ Longitude : num -76.6 -76.6 -76.7 -76.7 -76.7 ...
## $ GeoLocation : chr "(39.303764,-76.579207)" "(39.301503,-76.634683)" "(39.277684,-76.686368)" "(39.291997,-76.675504)" ...
## $ PremiseType : chr "OTHER/RESIDENTIAL" "OTHER/RESIDENTIAL" "OTHER/RESIDENTIAL" "OTHER/RESIDENTIAL" ...
## $ Total_Incidents: int 1 1 1 1 1 1 1 1 1 1 ...
## $ x : num -76.6 -76.6 -76.7 -76.7 -76.7 ...
## $ y : num 39.3 39.3 39.3 39.3 39.3 ...
## - attr(*, ".internal.selfref")=<externalptr>## RowID CCNumber CrimeDateTime CrimeCode
## Min. :138203 Length:17784 Length:17784 Length:17784
## 1st Qu.:183630 Class :character Class :character Class :character
## Median :195518 Mode :character Mode :character Mode :character
## Mean :195874
## 3rd Qu.:206340
## Max. :444317
## Description Inside_Outside Weapon Post
## Length:17784 Length:17784 Length:17784 Length:17784
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Gender Age Race Ethnicity
## Length:17784 Min. : 0.00 Length:17784 Length:17784
## Class :character 1st Qu.:24.00 Class :character Class :character
## Mode :character Median :32.00 Mode :character Mode :character
## Mean :34.71
## 3rd Qu.:44.00
## Max. :95.00
## Location Old_District New_District Neighborhood
## Length:17784 Length:17784 Length:17784 Length:17784
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Latitude Longitude GeoLocation PremiseType
## Min. :39.20 Min. :-76.71 Length:17784 Length:17784
## 1st Qu.:39.29 1st Qu.:-76.65 Class :character Class :character
## Median :39.30 Median :-76.62 Mode :character Mode :character
## Mean :39.31 Mean :-76.62
## 3rd Qu.:39.32 3rd Qu.:-76.59
## Max. :39.37 Max. :-76.53
## Total_Incidents x y
## Min. :1 Min. :-76.71 Min. :39.20
## 1st Qu.:1 1st Qu.:-76.65 1st Qu.:39.29
## Median :1 Median :-76.62 Median :39.30
## Mean :1 Mean :-76.62 Mean :39.31
## 3rd Qu.:1 3rd Qu.:-76.59 3rd Qu.:39.32
## Max. :1 Max. :-76.53 Max. :39.37Conclusions
Based on the charts below, the following actionable steps can be taken based on these observations. It is clear that the downtown area, as well as the Belair-Edison, Sandtown-Winchester, and Frankford neighborhoods are in the most dire need of increased policing and potential preventative action measures. These neighborhoods far and away have higher crime reports. When it comes to demographics, we see that the most populous age range of those committing crimes is in the 20-35 range - by seeking out individuals who commit crimes in this demographic and finding root causes, we may be able to take preventative action and work with local governing bodies to create change. With regard to identifying trends, there are two line graphs that show crime reports by both time of day, and month of the year. It appears that crime peaks in the summer months, and with respect to time of day, crime is quiet from 5:00 AM to 1:00 PM, but spikes after 3:00 PM and continues to be prevalent until approximately 3:00 AM. Finally, based on the donut chart that examines frequency of certain types of crime by description, it appears that violent crimes unfortunately take up the majority of the reports: Common assault is the most frequent, followed by aggrevated assault, then robbery followed by rape. In conclusion, based on the trends observed here, Baltimore’s police departments should increase staffing according to the times of day and month where crime spikes, as well as increasing presence in the neighborhoods listed above to ensure safety of civilians.
Chart #1: Crime Reports By Neighborhood
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Neighborhood n
## <char> <int>
## 1: ABELL 50
## 2: ALLENDALE 117
## 3: ARCADIA 23
## 4: ARLINGTON 98
## 5: ARMISTEAD GARDENS 74
## ---
## 280: WRENLANE 5
## 281: WYMAN PARK 13
## 282: WYNDHURST 6
## 283: YALE HEIGHTS 53
## 284: YORK-HOMELAND 15## Neighborhood n
## <char> <int>
## 1: ABELL 50
## 2: ALLENDALE 117
## 3: ARCADIA 23
## 4: ARLINGTON 98
## 5: ARMISTEAD GARDENS 74
## ---
## 280: WRENLANE 5
## 281: WYMAN PARK 13
## 282: WYNDHURST 6
## 283: YALE HEIGHTS 53
## 284: YORK-HOMELAND 15## Neighborhood n
## 63 DOWNTOWN 862
## 14 BELAIR-EDISON 440
## 88 FRANKFORD 437
## 236 SANDTOWN-WINCHESTER 374
## 256 UPTON 347
## 27 BROOKLYN 283
## 165 MONDAWMIN 274
## 84 FELLS POINT 245
## 47 CHERRY HILL 237
## 44 CENTRAL PARK HEIGHTS 226
## 127 INNER HARBOR 219
## 188 OLIVER 198
## 52 COLDSTREAM HOMESTEAD MONTEBELLO 194
## 69 EAST BALTIMORE MIDWAY 192
## 155 MCELDERRY PARK 192
## 263 WASHINGTON VILLAGE/PIGTOWN 190
## 187 OLDTOWN 188
## 39 CARROLLTON RIDGE 186
## 205 PENN NORTH 174
## 107 HAMILTON HILLS 170
## 174 MOUNT VERNON 169
## 25 BROADWAY EAST 167
## 67 DUNBAR-BROADWAY 164
## 207 PENROSE/FAYETTE STREET OUTREACH 154
## 93 GAY STREET 151
## 108 HAMPDEN 150
## 112 HARLEM PARK 150
## 213 POPPLETON 145
## 89 FRANKLIN SQUARE 140
## 221 RESERVOIR HILL 137
## 8 BALTIMORE HIGHLANDS 133
## 75 ELLWOOD PARK/MONUMENT 133
## 45 CHARLES NORTH 129
## 215 PULASKI INDUSTRIAL AREA 129
## 57 CURTIS BAY 128
## 145 LOCH RAVEN 125
## 202 PATTERSON PARK NEIGHBORHOOD 125
## 83 FEDERAL HILL 124
## 139 LAKELAND 123
## 128 IRVINGTON 121
## 46 CHARLES VILLAGE 119
## 163 MILLHILL 119
## 2 ALLENDALE 117
## 159 MID-TOWN BELVEDERE 116
## 130 JOHNSTON SQUARE 114
## 18 BETTER WAVERLY 113
## 170 MORRELL PARK 112
## 219 REISTERSTOWN STATION 112
## 17 BEREA 111
## 228 ROSEMONT 109
## 124 HOWARD PARK 103
## 10 BARCLAY 101
## 33 CANTON 101
## 54 COPPIN HEIGHTS/ASH-CO-EAST 100
## 65 DRUID HEIGHTS 100
## 4 ARLINGTON 98
## 94 GLEN 93
## 204 PEN LUCY 92
## 98 GREEKTOWN 90
## 279 WOODMERE 88
## 152 MADISON PARK 86
## 178 NEW NORTHWOOD 86
## 101 GREENSPRING 85
## 36 CARROLL - CAMDEN INDUSTRIAL AREA 82
## 74 EDNOR GARDENS-LAKESIDE 82
## 109 HANLON-LONGWOOD 80
## 153 MADISON-EASTEND 80
## 180 NORTH HARFORD ROAD 80
## 24 BRIDGEVIEW/GREENLAWN 79
## 82 FALLSTAFF 79
## 196 PARK CIRCLE 79
## 73 EDMONDSON VILLAGE 77
## 100 GREENMOUNT WEST 77
## 264 WAVERLY 76
## 161 MIDDLE EAST 75
## 5 ARMISTEAD GARDENS 74
## 91 FRANKLINTOWN ROAD 74
## 253 UNIVERSITY OF MARYLAND 72
## 261 WALTHERSON 72
## 121 HOLLINS MARKET 71
## 262 WASHINGTON HILL 71
## 70 EASTERWOOD 70
## 96 GLENHAM-BELHAR 70
## 162 MIDTOWN-EDMONDSON 70
## 238 SETON HILL 70
## 270 WESTPORT 70
## 64 DOWNTOWN WEST 69
## 240 SHIPLEY HILL 69
## 260 WALBROOK 68
## 34 CANTON INDUSTRIAL AREA 65
## 117 HIGHLANDTOWN 64
## 132 JONESTOWN 63
## 258 VIOLETVILLE 63
## 182 NORTHWEST COMMUNITY ACTION 62
## 21 BOLTON HILL 61
## 12 BAYVIEW 60
## 72 EDGEWOOD 59
## 164 MILTON-MONTFORD 59
## 268 WESTFIELD 59
## 38 CARROLL-SOUTH HILTON 58
## 95 GLEN OAKS 57
## 143 LIBERTY SQUARE 56
## 197 PARKLANE 56
## 234 SAINT JOSEPHS 56
## 61 DOLFIELD 55
## 230 ROSEMONT HOMEOWNERS/TENANTS 55
## 179 NEW SOUTHWEST/MOUNT CLARE 54
## 31 CALLAWAY-GARRISON 53
## 35 CARE 53
## 224 RIVERSIDE 53
## 242 SOUTH CLIFTON PARK 53
## 283 YALE HEIGHTS 53
## 26 BROENING MANOR 52
## 1 ABELL 50
## 13 BEECHFIELD 50
## 171 MOSHER 50
## 212 PLEASANT VIEW GARDENS 50
## 255 UPPER FELLS POINT 50
## 85 FOREST PARK 49
## 97 GRACELAND PARK 49
## 53 CONCERNED CITIZENS OF FOREST PARK 47
## 62 DORCHESTER 47
## 158 MID-GOVANS 47
## 199 PARKVIEW/WOODBROOK 47
## 41 CEDMONT 45
## 141 LAURAVILLE 45
## 278 WOODBOURNE-MCCABE 45
## 273 WINCHESTER 44
## 32 CAMERON VILLAGE 43
## 58 CYLBURN 43
## 87 FOUR BY FOUR 43
## 173 MOUNT HOLLY 43
## 115 HERITAGE CROSSING 42
## 186 OLD GOUCHER 42
## 48 CHESWOLDE 41
## 59 DARLEY PARK 40
## 68 EAST ARLINGTON 40
## 113 HARWOOD 40
## 118 HILLEN 40
## 183 O'DONNELL HEIGHTS 40
## 249 TOWANDA-GRANTLEY 40
## 266 WEST FOREST PARK 40
## 225 ROGNEL HEIGHTS 39
## 42 CEDONIA 38
## 206 PENN-FALLSWAY 38
## 239 SHARP-LEADENHALL 38
## 210 PERRING LOCH 37
## 20 BIDDLE STREET 36
## 104 GWYNNS FALLS 36
## 232 SAINT AGNES 36
## 136 KRESSON 35
## 142 LEVINDALE 35
## 144 LITTLE ITALY 35
## 198 PARKSIDE 35
## 265 WEST ARLINGTON 35
## 193 OTTERBEIN 34
## 252 UNION SQUARE 33
## 160 MIDDLE BRANCH/REEDBIRD PARKS 32
## 43 CENTRAL FOREST PARK 31
## 90 FRANKLINTOWN 31
## 102 GROVE PARK 31
## 140 LANGSTON HUGHES 31
## 195 PANWAY/BRADDISH AVENUE 31
## 269 WESTGATE 31
## 55 CROSS COUNTRY 30
## 220 REMINGTON 30
## 241 SOUTH BALTIMORE 30
## 6 ASHBURTON 29
## 211 PIMLICO GOOD NEIGHBORS 29
## 277 WOODBOURNE HEIGHTS 29
## 77 EVERGREEN LAWN 28
## 156 MEDFIELD 28
## 23 BREWERS HILL 27
## 157 MEDFORD 27
## 191 ORCHARD RIDGE 27
## 218 RAMBLEWOOD 27
## 126 IDLEWOOD 26
## 133 KENILWORTH PARK 26
## 203 PATTERSON PLACE 26
## 229 ROSEMONT EAST 26
## 254 UPLANDS 26
## 274 WINDSOR HILLS 26
## 122 HOMELAND 24
## 246 STONEWOOD-PENTWOOD-WINSTON 24
## 3 ARCADIA 23
## 22 BOYD-BOOTH 23
## 49 CHINQUAPIN PARK 23
## 50 CLIFTON PARK 23
## 172 MOUNT CLARE 23
## 79 FAIRFIELD AREA 22
## 125 HUNTING RIDGE 22
## 166 MONTEBELLO 22
## 276 WOODBERRY 22
## 66 DRUID HILL PARK 21
## 106 HAMILTON 21
## 185 OAKLEE 21
## 272 WILSON PARK 21
## 29 BUTCHER'S HILL 20
## 56 CROSS KEYS 20
## 92 GARWYN OAKS 20
## 146 LOCUST POINT 20
## 189 ORANGEVILLE 20
## 184 OAKENSHAWE 19
## 16 BELVEDERE 18
## 176 MOUNT WINANS 18
## 217 RADNOR-WINSTON 18
## 222 RICHNOR SPRINGS 18
## 226 ROLAND PARK 18
## 248 TEN HILLS 18
## 119 HOES HEIGHTS 16
## 175 MOUNT WASHINGTON 15
## 259 WAKEFIELD 15
## 275 WINSTON-GOVANS 15
## 284 YORK-HOMELAND 15
## 138 LAKE WALKER 14
## 181 NORTH ROLAND PARK/POPLAR HILL 14
## 237 SETON BUSINESS PARK 14
## 267 WEST HILLS 14
## 37 CARROLL PARK 13
## 151 LUCILLE PARK 13
## 281 WYMAN PARK 13
## 7 AUCHENTOROLY-PARKWOOD 12
## 123 HOPKINS BAYVIEW 12
## 147 LOCUST POINT INDUSTRIAL AREA 12
## 250 TREMONT 12
## 271 WILHELM PARK 12
## 51 COLDSPRING 11
## 103 GUILFORD 11
## 134 KERNEWOOD 11
## 223 RIDGELY'S DELIGHT 11
## 244 STADIUM AREA 11
## 71 EASTWOOD 10
## 245 STADIUM/ENTERTAINMENT AREA 10
## 11 BARRE CIRCLE 9
## 15 BELAIR-PARKSIDE 9
## 19 BEVERLY HILLS 9
## 78 EVESHAM PARK 9
## 148 LOWER EDMONDSON VILLAGE 9
## 154 MAYFIELD 9
## 201 PATTERSON PARK 9
## 216 PURNELL 9
## 227 ROSEBANK 9
## 233 SAINT HELENA 9
## 28 BURLEITH-LEIGHTON 8
## 129 JOHNS HOPKINS HOMEWOOD 8
## 131 JONES FALLS AREA 8
## 167 MORAVIA-WALTHER 8
## 190 ORANGEVILLE INDUSTRIAL AREA 8
## 235 SAINT PAUL 8
## 251 TUSCANY-CANTERBURY 8
## 105 GWYNNS FALLS/LEAKIN PARK 7
## 194 OVERLEA 7
## 30 BUTCHERS HILL 6
## 80 FAIRMONT 6
## 169 MORGAN STATE UNIVERSITY 6
## 282 WYNDHURST 6
## 110 HARBOR EAST 5
## 120 HOLABIRD INDUSTRIAL PARK 5
## 192 ORIGINAL NORTHWOOD 5
## 280 WRENLANE 5
## 9 BALTIMORE PENINSULA 4
## 40 CEDARCROFT 4
## 76 EVERGREEN 4
## 114 HAWKINS POINT 4
## 137 LAKE EVESHAM 4
## 168 MORGAN PARK 4
## 243 SPRING GARDEN INDUSTRIAL AREA 4
## 116 HERRING RUN PARK 3
## 208 PERKINS 3
## 231 SABINA-MATTFELDT 3
## 81 FAIRMOUNT 2
## 86 FOREST PARK GOLF COURSE 2
## 99 GREENMOUNT CEMETERY 2
## 111 HARBOR POINT 2
## 150 LOYOLA/NOTRE DAME 2
## 200 PARKWAY 2
## 209 PERKINS HOMES 2
## 214 PORT COVINGTON 2
## 247 TAYLOR HEIGHTS 2
## 60 DICKEYVILLE 1
## 135 KESWICK 1
## 149 LOWER HERRING RUN PARK 1
## 177 MT PLEASANT PARK 1
## 257 VILLAGES OF HOMELAND 1
Chart #2: Crime Reports by Age
Chart #3: Crime Reports by Time of Day
##
## Attaching package: 'lubridate'## The following objects are masked from 'package:data.table':
##
## hour, isoweek, isoyear, mday, minute, month, quarter, second, wday,
## week, yday, year## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union## hour24 n
## 1 0 1083
## 2 1 772
## 3 2 681
## 4 3 488
## 5 4 456
## 6 5 342
## 7 6 299
## 8 7 365
## 9 8 485
## 10 9 559
## 11 10 602
## 12 11 639
## 13 12 720
## 14 13 627
## 15 14 814
## 16 15 1003
## 17 16 976
## 18 17 1004
## 19 18 937
## 20 19 926
## 21 20 1025
## 22 21 1056
## 23 22 886
## 24 23 1039## 'data.frame': 24 obs. of 2 variables:
## $ hour24: int 0 1 2 3 4 5 6 7 8 9 ...
## $ n : int 1083 772 681 488 456 342 299 365 485 559 ...
Chart #4: Crime Reports by Month
## month n
## 1 1 1372
## 2 2 1356
## 3 3 1504
## 4 4 1526
## 5 5 1608
## 6 6 1557
## 7 7 1563
## 8 8 1561
## 9 9 1598
## 10 10 1569
## 11 11 1341
## 12 12 1229
Chart #5: Description of Crime Reports
##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout## [1] 12## Description n
## 1 AGG. ASSAULT 4854
## 2 ARSON 6
## 3 AUTO THEFT 11
## 4 BURGLARY 29
## 5 COMMON ASSAULT 8682
## 6 HOMICIDE 180
## 7 LARCENY 20
## 8 LARCENY FROM AUTO 2
## 9 RAPE 484
## 10 ROBBERY 2702
## 11 ROBBERY - CARJACKING 422
## 12 SHOOTING 392