Complaint CodedMonth DateOccur FlagCrime FlagUnfounded
1: 19-000184 2018-12 12/31/2018 15:30 Y NA
2: 18-061560 2018-12 12/31/2018 15:30 Y NA
3: 18-061560 2018-12 12/31/2018 15:30 Y NA
4: 18-061554 2018-12 12/31/2018 15:00 Y NA
5: 18-061561 2018-12 12/31/2018 14:45 Y NA
6: 19-000083 2018-12 12/31/2018 14:30 Y NA
FlagAdministrative Count FlagCleanup Crime District
1: NA 1 NA 64601 3
2: NA 1 NA 91123 1
3: NA 1 NA 91113 1
4: NA 1 NA 265321 6
5: NA 1 NA 64701 5
6: NA 1 NA 64701 1
Description ILEADSAddress
1: LARCENY-FROM MTR VEH $500 - $24,999 0
2: SIMPLE ASSAULT-CHILD/NO INJURY 4117
3: SIMPLE ASSAULT-ADULT/NO INJURY 4117
4: LEAVING SCENE OF ACCIDENT 0
5: LARCENY-FROM MTR VEH UNDER $500 0
6: LARCENY-FROM MTR VEH UNDER $500 2920
ILEADSStreet Neighborhood LocationName LocationComment
1: S 3RD ST / GEYER AVE 20
2: GERMANIA ST 4
3: GERMANIA ST 4
4: N KINGSHIGHWAY BLVD / NATURAL 55
5: ANNIE MALONE DR / W SAINT FERD 57
6: MERAMEC ST 17 US POST OFFICE
CADAddress CADStreet XCoord YCoord
1: NA 906024.9 1009980.0
2: 4117 GERMANIA 882747.7 992739.9
3: 4117 GERMANIA 882747.7 992739.9
4: 4223 ENRIGHT 891049.0 1035207.0
5: NA 895101.4 1028721.0
6: 5410 IDAHO 895897.0 1000157.0
The STL Metropolitan Police produces a monthly crime update.
Stored in a csv format and can be downloaded.
Located at https://www.slmpd.org/Crimereports.shtml.
The file provides all crime details collected from the preceding month.
Contains locations, neighborhoods, precincts, map coordinates and times of crimes in the St Louis Metropolitan Area.
Complaint CodedMonth DateOccur FlagCrime
Length:95604 Length:95604 Length:95604 Length:95604
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
FlagUnfounded FlagAdministrative Count FlagCleanup
Mode:logical Mode:logical Min. :-1.0000 Mode:logical
NA's:95604 NA's:95604 1st Qu.: 1.0000 NA's:95604
Median : 1.0000
Mean : 0.9746
3rd Qu.: 1.0000
Max. : 1.0000
Crime District Description ILEADSAddress
Min. : 10000 Min. :0.000 Length:95604 Min. : 0
1st Qu.: 64701 1st Qu.:2.000 Class :character 1st Qu.: 900
Median : 71030 Median :4.000 Mode :character Median : 3307
Mean :122365 Mean :3.625 Mean : 3009
3rd Qu.:182260 3rd Qu.:5.000 3rd Qu.: 4612
Max. :266999 Max. :6.000 Max. :12361
NA's :145
ILEADSStreet Neighborhood LocationName LocationComment
Length:95604 Min. : 0.00 Length:95604 Length:95604
Class :character 1st Qu.:17.00 Class :character Class :character
Mode :character Median :36.00 Mode :character Mode :character
Mean :38.82
3rd Qu.:59.00
Max. :88.00
CADAddress CADStreet XCoord YCoord
Min. : 0 Length:95604 Min. : 0 Min. : 0
1st Qu.: 1711 Class :character 1st Qu.:887680 1st Qu.:1004962
Median : 3766 Mode :character Median :893512 Median :1018439
Mean : 3557 Mean :870517 Mean : 991756
3rd Qu.: 4929 3rd Qu.:900001 3rd Qu.:1029498
Max. :41740 Max. :911342 Max. :1093729
NA's :22860
Again, some fields are irrelevant to our analysis.
We will remove these elements using a tidyverse library called dplyr.
We will also have to restructure certain date/time variables.
Flags are not needed.
Don’t see how count field is significant in the analysis.
Observations: 369
Variables: 15
$ Complaint <chr> "18-061082", "18-060906", "18-060607", "18-060518",...
$ CodedMonth <chr> "2018-12", "2018-12", "2018-12", "2018-12", "2018-1...
$ DateOccur <chr> "12/28/2018 1:15", "12/26/2018 21:56", "12/24/2018 ...
$ Crime <int> 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10...
$ District <int> 6, 3, 6, 1, 6, 5, 6, 4, 3, 4, 5, 6, 6, 3, 2, 6, 6, ...
$ Description <chr> "HOMICIDE", "HOMICIDE", "HOMICIDE", "HOMICIDE", "HO...
$ ILEADSAddress <int> 5300, 2651, 4042, 5913, 4476, 1224, 3921, 0, 1051, ...
$ ILEADSStreet <chr> "GERALDINE AVE", "HICKORY ST", "CARTER AVE", "PENNS...
$ Neighborhood <int> 71, 31, 68, 1, 56, 53, 67, 63, 21, 60, 55, 84, 76, ...
$ LocationName <chr> "", "CAROLINE PLACE APARTMENTS", "", "", "", "", ""...
$ LocationComment <chr> "", "APARTMENT COMPLEX", "", "", "", "", "", "", ""...
$ CADAddress <int> 5304, 2654, NA, 5913, NA, 1200, 3927, NA, 1051, 193...
$ CADStreet <chr> "GERALDINE", "RUTGER", "", "PENNSYLVANIA", "", "AUB...
$ XCoord <dbl> 893346.5, 900117.9, 899795.6, 892982.7, 894410.2, 8...
$ YCoord <dbl> 1040599.0, 1015110.0, 1033658.0, 992870.4, 1032898....
I wanted to select a specific crime. In this case we will look at Homicides.
Some data fields are not relevant to the analysis so I’ve limited the data to the following 6 elements.
Homicides are UCR coded as 10000.
Although the STLMPD website states rows are unique, they are NOT.
During this phase I also wanted to determine data types.
The mix is a combination of characters string and integers.
I will have to re-charactize some elements to more easily manipulate later.
“CodedMonth” and “DateOccur” are not date/time elements, so they need to be changed.
'data.frame': 369 obs. of 15 variables:
$ Complaint : chr "18-061082" "18-060906" "18-060607" "18-060518" ...
$ CodedMonth : Date, format: "2018-12-28" "2018-12-28" ...
$ DateOccur : POSIXct, format: "2018-12-28 01:15:00" "2018-12-26 21:56:00" ...
$ Crime : int 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 ...
$ District : int 6 3 6 1 6 5 6 4 3 4 ...
$ Description : chr "HOMICIDE" "HOMICIDE" "HOMICIDE" "HOMICIDE" ...
$ ILEADSAddress : int 5300 2651 4042 5913 4476 1224 3921 0 1051 1933 ...
$ ILEADSStreet : chr "GERALDINE AVE" "HICKORY ST" "CARTER AVE" "PENNSYLVANIA AVE" ...
$ Neighborhood : int 71 31 68 1 56 53 67 63 21 60 ...
$ LocationName : chr "" "CAROLINE PLACE APARTMENTS" "" "" ...
$ LocationComment: chr "" "APARTMENT COMPLEX" "" "" ...
$ CADAddress : int 5304 2654 NA 5913 NA 1200 3927 NA 1051 1933 ...
$ CADStreet : chr "GERALDINE" "RUTGER" "" "PENNSYLVANIA" ...
$ XCoord : num 893347 900118 899796 892983 894410 ...
$ YCoord : num 1040599 1015110 1033658 992870 1032898 ...
Need to use some R libraries to convert data types.
Used stringr and lubridate libraries to change data types.
Changed “CodedMonth” to a string value closer to one resembling a year/month/day field.
Used 28 days as the day value so I do not have to constantly worry about the changing days/month values.
Since the data is collected as of the last day of the month, it will not affect the monthly crime perspective.
Next I created a concatonated string group and convert that field into a “POSIX” day/month/day variable.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 749 days 0.0 0.0 SHREVE 4257
2 483 days 0.0 0.0 NA
3 371 days 997824.9 890012.2 NA
4 291 days 0.0 0.0 CHURCH 7943
5 286 days 0.0 0.0 MAFFITT 5322
6 184 days 0.0 0.0 LABADIE 4446
7 179 days 0.0 0.0 EUCLID 1202
8 124 days 0.0 0.0 TEXAS 3709
9 42 days 996991.1 894592.8 ITASKA 3111
10 31 days 0.0 0.0 SAINT LOUIS 4753
11 31 days 992687.3 890685.3 NA
12 29 days 1028778.0 888118.1 NA
13 27 days 1034026.0 898534.9 GREEN LEA 4136
14 27 days 1023634.0 907592.0 14TH 1908
15 27 days 1030898.0 901867.4 NA
16 27 days 1048605.0 896231.8 GIMBLIN 1020
17 27 days 1028286.0 893354.1 ALDINE 4349
18 27 days 1034113.0 885395.3 NA
19 27 days 1021943.0 899509.6 DELMAR 3114
20 27 days 1027916.0 905727.4 KNAPP 3245
21 27 days 1028080.0 894410.9 COTE BRILLIANTE 4229
22 27 days 1000447.0 877220.9 KINSEY 6272
23 27 days 1036161.0 882481.3 KENNERLY 6101
24 27 days 1033304.0 887147.7 NA
25 27 days 1029233.0 890945.9 NA
26 27 days 1034806.0 882698.1 NA
27 27 days 1029923.0 880264.4 HAMILTON 1021
28 27 days 1019440.0 905891.9 15TH 710
29 26 days 1032130.0 896900.4 NATURAL BRIDGE 4231
30 26 days 998275.6 894535.1 MINNESOTA 4457
31 26 days 1047905.0 897340.6 NA
32 26 days 0.0 0.0 NA
33 26 days 1004111.0 893520.9 GRAND 3630
34 26 days 1007221.0 884519.4 HEREFORD 3322
35 26 days 1003295.0 895121.2 NA
36 26 days 1030650.0 887444.4 DR MARTIN LUTHER KING 5100
37 26 days 995444.5 892927.1 VIRGINIA 5301
38 26 days 1032146.0 883637.7 NA
39 26 days 1008027.0 901258.3 CONGRESS 1909
40 26 days 1032246.0 900444.8 LEE 3856
41 26 days 1028281.0 894047.3 COTE BRILLIANTE 4200
42 25 days 1034757.0 904588.8 BROADWAY 4828
43 25 days 1028137.0 893625.3 BILLUPS 1705
44 25 days 1053153.0 898033.6 NA
45 25 days 1031750.0 883103.6 MINERVA 5634
46 25 days 998801.4 894874.6 MICHIGAN 4414
47 25 days 1027393.0 904794.8 RAUSCHENBACH 3117
48 25 days 1039733.0 885195.7 MCARTHUR 5910
49 25 days 1025430.0 897494.3 CASS 3731
50 24 days 1051995.0 895308.4 RIVERVIEW 1124
51 24 days 1005529.0 890871.6 POTOMAC 3954
52 24 days 1000978.0 886650.3 WALLACE 4341
53 24 days 1034920.0 886010.0 BURD 2728
54 24 days 1033370.0 887518.1 NA
55 24 days 1028707.0 884870.6 NA
56 24 days 1031967.0 895056.4 NA
57 24 days 1022360.0 900798.4 NA
58 24 days 1046675.0 889809.7 MIMIKA 5531
59 24 days 1010480.0 895630.6 LOUISIANA 2328
60 23 days 1007382.0 893365.3 CONNECTICUT 3634
61 23 days 1036961.0 883830.0 SAINT LOUIS 5920
62 23 days 1042272.0 893522.1 NA
63 23 days 1014127.0 903976.8 DILLON 1124
64 23 days 1032130.0 896900.4 NATURAL BRIDGE 4231
65 23 days 1036605.0 897450.3 CARRIE 4531
66 23 days 1022548.0 907625.4 NA
67 23 days 1038155.0 895308.8 FLORISSANT 4700
68 23 days 1003278.0 895156.4 COMPTON 3720
69 23 days 1037063.0 886918.4 PALM 5550
70 23 days 1022299.0 906762.9 COCHRAN 1461
71 23 days 1026766.0 897107.0 GARFIELD 4000
72 23 days 1043213.0 892050.1 THRUSH 5400
73 23 days 1000926.0 892725.6 MERAMEC 3711
74 22 days 1047342.0 889848.5 FLOY 5594
75 22 days 1044996.0 892586.6 NA
76 22 days 988016.4 886631.2 DAVIS 547
77 22 days 1046716.0 896542.6 SWITZER 951
78 22 days 0.0 0.0 NA
79 22 days 1029418.0 891030.9 4949
80 22 days 1002487.0 898351.1 NA
81 22 days 1030139.0 904277.8 NA
82 22 days 1034151.0 902558.0 NA
83 22 days 1048015.0 896324.3 FREDERICK 8216
84 22 days 1021607.0 898042.0 NA
85 22 days 1034699.0 884938.9 HIGHLAND 5627
86 22 days 992407.4 890121.1 IDAHO 6602
87 22 days 1031273.0 885087.4 SEMPLE 1400
88 21 days 1052128.0 895835.8 NA
89 21 days 1044850.0 892558.1 FLORISSANT 5728
90 21 days 1048122.0 897219.8 BROADWAY 8220
91 21 days 1004800.0 894538.1 NA
92 21 days 1035792.0 892505.1 NA
93 21 days 1042225.0 890033.2 PLOVER 4936
94 21 days 1034573.0 896427.9 NEWSTEAD 4140
95 21 days 1026560.0 887259.6 ENRIGHT 5048
96 20 days 1031415.0 894194.6 NEWSTEAD 2931
97 20 days 1028621.0 898253.1 NA
98 20 days 997279.0 894676.9 MICHIGAN 4626
99 20 days 1031778.0 890118.8 HIGHLAND 4900
100 20 days 1002564.0 891249.9 NA
101 20 days 1030531.0 898549.0 LEXINGTON 3900
102 20 days 1035777.0 893192.8 NA
103 20 days 1032928.0 897204.1 NA
104 20 days 1005006.0 892615.9 GILES 3521
105 20 days 1034773.0 881698.6 NA
106 20 days 1031673.0 884752.1 BELT 1439
107 20 days 1032225.0 882995.8 BLACKSTONE 1387
108 19 days 1000824.0 894103.1 LOUISIANA 4114
109 19 days 1030386.0 901979.7 PECK 4012
110 19 days 1025417.0 892611.9 PENDLETON 926
111 19 days 1013903.0 904245.2 NA
112 19 days 1025707.0 896608.4 NA
113 19 days 1044225.0 891667.2 ROBIN 5434
114 19 days 1038048.0 900410.6 GRAND 1325
115 19 days 1026655.0 891146.6 NA
116 19 days 1001651.0 899383.5 WISCONSIN 3853
117 19 days 1030024.0 900011.5 NATURAL BRIDGE 3836
118 19 days 997345.6 894541.8 MICHIGAN 4600
119 19 days 1044387.0 887882.4 NA
120 19 days 1022813.0 905078.3 MURPHY PARK 1851
121 19 days 1026907.0 899641.4 NA
122 19 days 1028866.0 877725.8 NA
123 19 days 1028459.0 902545.9 PALM 2522
124 19 days 1001554.0 895565.8 NA
125 18 days 1030515.0 885139.5 MINERVA 5363
126 18 days 998619.1 894877.5 MICHIGAN 4340
127 18 days 1000689.0 896718.0 NA
128 18 days 1000348.0 894739.9 MERAMEC 3147
129 18 days 1002881.0 895726.6 NA
130 17 days 995729.4 891902.7 IDAHO 5417
131 17 days 990205.4 888646.9 GRAND 1325
132 17 days 1028346.0 893245.7 ALDINE 4349
133 17 days 1030457.0 898946.1 PALM 3921
134 17 days 1021285.0 887849.5 PARKVIEW 4921
135 17 days 1027106.0 887992.3 KINGSHIGHWAY 900
136 17 days 1030385.0 891428.5 MARKET 4639
137 17 days 1026678.0 898056.9 NA
138 17 days 1005252.0 894945.9 VIRGINIA 3429
139 17 days 1027150.0 889813.4 MCMILLAN 4700
140 17 days 1032050.0 894045.3 LABADIE 4446
141 17 days 1028243.0 904858.9 NA
142 17 days 1044511.0 887701.6 GOODFELLOW 5003
143 17 days 1000773.0 894063.4 NA
144 16 days 1044511.0 887701.6 NA
145 16 days 1033378.0 891132.4 NA
146 16 days 1034206.0 903869.1 JOHN 1449
147 16 days 1048634.0 890708.3 NORTH POINTE 6145
148 16 days 1004299.0 891279.4 NA
149 16 days 1000305.0 887120.0 DELOR 5254
150 16 days 1025296.0 893849.6 C D BANKS 4155
151 16 days 1029258.0 906530.4 11TH 3505
152 16 days 1010280.0 895225.3 TENNESSEE 2602
153 16 days 994702.8 892471.4 NA
154 15 days 1035297.0 895854.2 PENROSE 4481
155 15 days 1033212.0 884433.3 THEODOSIA 5601
156 15 days 1035636.0 893450.8 SHREVE 4049
157 15 days 1027261.0 896310.5 GARFIELD 4000
158 15 days 1021049.0 900148.2 SAMUEL SHEPARD 2946
159 15 days 1017651.0 909633.8 NA
160 14 days 1038855.0 892836.5 NA
161 14 days 1036218.0 884340.4 ROOSEVELT 5816
162 14 days 1002831.0 896061.4 CHIPPEWA 3116
163 14 days 0.0 0.0 TENNESSEE 5226
164 14 days 1042247.0 890065.3 PLOVER 4938
165 13 days 1035671.0 897440.9 CLARENCE 4401
166 13 days 1029887.0 889834.6 DR MARTIN LUTHER KING 4821
167 13 days 1035834.0 884252.7 NA
168 13 days 999847.4 885892.1 NA
169 13 days 0.0 0.0 4949
170 13 days 1036000.0 888884.8 UNION 3431
171 13 days 0.0 0.0 NA
172 13 days 1037099.0 895579.4 RICHARD 4625
173 13 days 998998.5 896676.4 NA
174 13 days 1046874.0 889008.8 GOODFELLOW 5517
175 13 days 1027291.0 882262.8 ENRIGHT 5616
176 12 days 1031341.0 891609.6 MARCUS 2613
177 12 days 1044484.0 888564.8 NA
178 12 days 1030127.0 886695.8 RIDGE 5138
179 12 days 1017204.0 908242.7 NA
180 12 days 1032972.0 903934.3 19TH 4406
181 12 days 1029537.0 893621.6 NA
182 12 days 1025357.0 907701.4 NA
183 12 days 1033897.0 889429.4 ST LOUIS 5109
184 12 days 1022022.0 907595.8 13TH 1430
185 11 days 1017799.0 907619.9 9TH 205
186 11 days 1020329.0 899238.6 OLIVE 3037
187 11 days 1031357.0 905076.9 NA
188 11 days 997764.3 895610.9 NEBRASKA 4529
189 11 days 1026735.0 891120.7 NA
190 11 days 1007405.0 894425.6 NA
191 11 days 1035772.0 882305.7 WABADA 5969
192 11 days 1026184.0 890301.9 ENRIGHT 4550
193 11 days 1051786.0 894677.4 HOWELL 1181
194 11 days 1027321.0 888019.9 NA
195 11 days 1051778.0 894566.9 NA
196 11 days 1032520.0 880346.8 NA
197 11 days 1051838.0 895354.6 HOWELL 1115
198 10 days 1028775.0 900428.8 NA
199 10 days 1027075.0 896370.4 GARFIELD 4000
200 10 days 1022154.0 898673.6 NA
201 10 days 1004925.0 897760.9 CHEROKEE 2720
202 10 days 1026933.0 888149.1 AUBERT 773
203 10 days 1027279.0 891495.9 NA
204 10 days 1022136.0 905133.4 HOGAN 1800
205 10 days 998003.9 893209.4 VIRGINIA 4518
206 10 days 1030180.0 888420.4 KINGSHIGHWAY 1408
207 9 days 1010572.0 903703.8 ALLEN 1051
208 9 days 1025124.0 905021.8 BENTON 1933
209 9 days 1045249.0 887714.6 LALITE 6336
210 9 days 1037168.0 894947.6 NA
211 9 days 990701.9 888591.6 NA
212 9 days 1028552.0 881199.8 GOODFELLOW 853
213 9 days 993755.2 878508.2 GRAVOIS 7422
214 9 days 1046545.0 891148.8 NA
215 9 days 1068605.0 910845.4 EB 270 NA
216 9 days 1017616.0 876377.6 PARK 6763
217 9 days 1028716.0 890091.6 PAGE 4711
218 9 days 1013953.0 904086.3 NA
219 9 days 1031748.0 885691.9 DR MARTIN LUTHER KING 5378
220 9 days 1030986.0 885172.3 NA
221 9 days 1022057.0 901215.1 STODDARD 2800
222 8 days 1022134.0 909573.1 NA
223 8 days 1006156.0 896621.1 PENNSYLVANIA 3244
224 8 days 1030986.0 885172.3 NA
225 8 days 1040130.0 894837.9 NA
226 8 days 1040705.0 891528.7 ARLINGTON 4941
227 8 days 1036066.0 893029.3 KOSSUTH 4863
228 8 days 1024730.0 907940.6 NA
229 7 days 1018977.0 905530.4 NA
230 7 days 1030451.0 896704.6 WHITTIER 3047
231 7 days 1018061.0 895739.8 SCOTT 3560
232 7 days 1040680.0 890932.3 EMERSON 4921
233 7 days 1032299.0 886623.5 PATTON 5331
234 7 days 1034664.0 896014.3 LEE 4438
235 7 days 1033549.0 887857.3 UNION 2700
236 7 days 1026696.0 899404.5 MONTGOMERY 3461
237 7 days 1035119.0 883649.6 HIGHLAND 5807
238 7 days 1026065.0 881536.7 KINGSBURY 5708
239 7 days 1022984.0 900603.5 JAMES COOL PAPA BELL 2900
240 6 days 1032339.0 900019.9 SHERMAN 3927
241 6 days 1027313.0 897869.5 COTTAGE 3834
242 6 days 1034257.0 901754.4 NA
243 6 days 1044313.0 891175.7 GILMORE 5276
244 6 days 1002248.0 887263.9 MORGANFORD 4522
245 6 days 0.0 0.0 NA
246 6 days 1007882.0 895751.5 HARTFORD 3230
247 6 days 1033378.0 891132.4 EUCLID 2944
248 6 days 1031231.0 903749.4 PENROSE 2106
249 6 days 1032953.0 887081.5 WABADA 5330
250 6 days 1032241.0 900648.8 LEE 3833
251 6 days 1035883.0 883594.1 MAFFITT 5800
252 6 days 1028851.0 891679.1 DR MARTIN LUTHER KING 4582
253 6 days 1033520.0 889914.2 ST LOUIS 5025
254 5 days 992870.4 892982.7 PENNSYLVANIA 5913
255 5 days 1032898.0 894410.2 NA
256 5 days 1028603.0 888564.7 AUBERT 1200
257 5 days 1051408.0 896489.1 CANAAN 907
258 5 days 1030932.0 893627.2 NA
259 5 days 1032937.0 892505.8 ASHLAND 4710
260 5 days 1026577.0 894318.1 EVANS 4200
261 5 days 1037449.0 894152.3 NA
262 5 days 1002881.0 895726.6 NA
263 5 days 1029269.0 898082.8 LABADIE 3945
264 5 days 1026084.0 899342.4 NA
265 5 days 1028563.0 898133.8 VANDEVENTER 2816
266 5 days 1043141.0 890387.4 WREN 5015
267 5 days 1017271.0 903906.1 NA
268 5 days 1022183.0 899505.3 NA
269 5 days 989533.2 887239.5 SCHIRMER 800
270 5 days 1002406.0 891194.5 ALBERTA 3921
271 5 days 989042.8 889513.5 PENNSYLVANIA 7403
272 5 days 997804.3 891252.8 DELOR 3659
273 5 days 1033277.0 904660.4 BISSELL 1121
274 5 days 1011331.0 881563.1 DALTON 0
275 5 days 1049783.0 891947.9 MORA 8561
276 5 days 1028461.0 885269.8 NA
277 5 days 1031326.0 890200.3 HAMMETT 4851
278 5 days 1049854.0 895810.5 HALLS FERRY 8612
279 5 days 1033725.0 882188.8 NA
280 5 days 1046626.0 890521.4 SHULTE 6035
281 4 days 1033658.0 899795.6 NA
282 4 days 1027078.0 888592.4 EUCLID 785
283 4 days 1031732.0 902337.5 GRAND 4206
284 4 days 990479.7 888796.9 NA
285 4 days 1051754.0 896324.3 ELIAS 933
286 4 days 1047754.0 889373.8 NA
287 4 days 1048493.0 896371.1 CHURCH 8309
288 4 days 1033060.0 891237.8 LABADIE 4843
289 4 days 1043363.0 890705.5 WREN 5055
290 4 days 1020209.0 903370.6 21ST 715
291 4 days 996624.4 891625.1 LOUISIANA 5211
292 3 days 1024271.0 906633.1 NA
293 3 days 1041538.0 892153.7 ALCOTT 5200
294 3 days 1027247.0 903401.7 DODIER NA
295 3 days 1022198.0 907641.5 14TH 1430
296 3 days 1035428.0 893986.3 4949
297 3 days 1024089.0 908269.7 NA
298 3 days 1033423.0 897476.8 KOSSUTH 4235
299 3 days 1034813.0 903047.6 DE SOTO 1409
300 3 days 995171.7 893913.2 WALSH 308
301 3 days 1002376.0 893358.7 NA
302 3 days 0.0 0.0 NA
303 3 days 1031432.0 891251.6 NA
304 3 days 990000.4 888462.2 NA
305 3 days 1003994.0 894935.3 VIRGINIA 3620
306 2 days 1015110.0 900117.9 RUTGER 2654
307 2 days 1051640.0 897259.3 ELIAS 835
308 2 days 1051479.0 895927.5 CANAAN 971
309 2 days 1040904.0 886178.6 NA
310 2 days 1035696.0 893689.0 KOSSUTH 4727
311 2 days 1016725.0 890017.6 NORFOLK 4250
312 2 days 1030720.0 896366.2 ASHLAND 4279
313 1 days 1010894.0 893626.0 SHENANDOAH 3658
314 1 days 1004511.0 898021.4 OHIO 3452
315 1 days 1004375.0 884540.4 CHIPPEWA 4939
316 1 days 1027553.0 893493.7 DR MARTIN LUTHER KING 4308
317 1 days 1001617.0 895896.4 PENNSYLVANIA 3942
318 1 days 1004029.0 888199.9 MORGANFORD 4254
319 1 days 1037577.0 885193.6 SELBER 5830
320 1 days 1043688.0 891512.5 WREN 5270
321 1 days 1003043.0 890577.3 DUNNICA 3946
322 1 days 1023898.0 906643.6 NA
323 1 days 1019893.0 910547.7 2ND 999
324 1 days 1049007.0 890159.1 GOODFELLOW 5961
325 1 days 1034124.0 895794.9 FARLIN 4447
326 1 days 1052972.0 896665.4 RIVERVIEW 911
327 0 days 1040599.0 893346.5 GERALDINE 5304
328 0 days 1035764.0 882107.0 WABADA 5962
329 0 days 1033922.0 888254.7 TERRY 5252
330 0 days 1036786.0 886544.7 CLARA 3340
331 0 days 1042473.0 892352.3 DAVISON 5271
332 0 days 995260.5 893647.8 EICHELBERGER 411
333 0 days 995670.8 891021.7 NA
334 0 days 1031520.0 885578.9 ARLINGTON 1460
335 0 days 1038456.0 900558.8 NA
336 -1 days 1031068.0 885126.1 ARLINGTON 1401
337 -1 days 1028747.0 898586.3 LABADIE 3800
338 -1 days 1010081.0 903858.6 NA
339 -1 days 1016086.0 908223.8 CLARK 601
340 -1 days 1032564.0 881482.7 NA
341 -1 days 1017361.0 890066.3 MANCHESTER 4238
342 -1 days 1035157.0 902483.6 CONDE 5220
343 -1 days 1036030.0 882441.9 HIGHLAND 5900
344 -1 days 1043154.0 890135.3 NA
345 -1 days 1036149.0 891990.5 FARLIN 4950
346 -1 days 1022299.0 906762.9 CASS 1415
347 -1 days 989055.9 889596.9 UPTON 225
348 -1 days 1000754.0 895093.4 MICHIGAN 4100
349 -1 days 1008747.0 887198.5 LACKLAND 3139
350 -1 days 1048551.0 893434.8 NA
351 -1 days 1047618.0 897453.9 BITTNER 732
352 -2 days 997429.1 891964.9 TENNESSEE 4758
353 -2 days 1037384.0 885274.5 GOODFELLOW 5713
354 -2 days 1003002.0 895591.7 NA
355 -2 days 1029828.0 885989.0 MINERVA 5232
356 -2 days 1035240.0 898489.6 ATHLONE 4430
357 -2 days 1051865.0 896562.4 HARLAN 917
358 -2 days 1041712.0 893530.7 CLAXTON 5357
359 -2 days 1039648.0 885117.3 FERRIS 5911
360 -2 days 1028291.0 891706.2 NA
361 -2 days 1028254.0 890506.0 PAGE 4634
362 -2 days 1035149.0 882181.7 NA
363 -2 days 1013424.0 904108.4 NA
364 -2 days 1035148.0 901348.9 NA
365 -3 days 1003486.0 896527.1 NEBRASKA 3646
366 -3 days 1032899.0 894125.9 LEXINGTON 4534
367 -3 days 1033526.0 899422.0 CLAY 4221
368 -3 days 1034569.0 886330.5 NA
369 -3 days 1028958.0 887970.3 MAPLE 5009
LocationComment LocationName
1 SAMS ST. LOUIS PACKING CO
2
3 REAR
4
5
6
7
8
9
10
11
12 AUTO ZONE
13
14
15 DOLLAR GENERAL
16
17
18
19
20
21
22
23 ON STREET ON STREET
24
25 NBC LOUNGE
26
27
28
29 WEST PARKING LOT M&A LIQUOR STORE
30 MOUNT PLEASANT PARK
31
32
33 PAPA JOHNS PIZZA
34 APARTMENT BUILDING
35 STREET
36 THE OTHER PLACE LOUNGE
37
38
39
40
41 VACANT LOT
42 HOTEL- FIRST WESTERN INN
43
44 RESIDENCE RESIDENCE
45
46
47
48
49
50
51
52 ALLEY ADJACENT TO SIDE WINDOW
53 LONDON S BOARDING HOUSE
54
55 REAR ALLEY
56
57
58
59 1ST FLOOR
60
61
62
63 CLINTON-PEABODY HOUSING COMPLEX
64 M & A MARKET PARKING LOT
65 JUST INSIDE OF TREE LINE IN REAR OF
66
67 @AMEREN - 158
68
69
70
71
72
73 SECOND FLOOR
74
75
76
77
78
79
80
81
82 REAR YARD OF VACANT RESIDENCE
83
84
85
86
87
88
89
90
91 GRAVOIS PARK
92
93
94
95 REAR
96
97 MIDWEST PETROLEUM
98
99
100 SOUTH SIDE OF AMBERG PARK
101
102 APT A
103
104
105
106 STREET
107
108
109
110
111
112 CAR WASH AND AUTOMOBILE DETAILING BU BIG P. S CAR WASH AND DETAILING
113
114
115
116 APT B
117
118
119 ST. LOUIS FISH & CHICKEN
120 IN STREET MURPHY PARK APARTMENTS
121 UPPER LEVEL CLUB
122 VACANT LOT
123
124
125
126
127
128
129
130
131
132 STREET
133
134 14TH FLOOR
135
136
137
138
139
140
141
142
143
144 @BP GAS STATION PARKING LOT
145
146
147
148
149
150
151
152
153
154
155
156
157 VACANT LOT
158
159 MANSION HOUSE
160
161
162 IN STREET
163 VICTIM S RESPONDED TO THE 5200 BLOCK
164
165
166 BEST AUTO PLEX
167 GROCERY STORE PRICE CHOPPER
168 5401 MISSOURI ROUTE 30 LUCKY DUCK RESTAURANT
169
170 @SCHNUCKS - CITY PLAZA ON UNION
171
172 TWO-FAMILY APARTMENT BLDG
173 CONOCO GAS STATION
174 REAR ALLEY
175 ST. LUKE S PLAZA APARTMENTS
176 EAST SIDEWALK
177
178
179
180
181
182
183 NORTH ALLEY
184 CASS BANK
185
186 INTERIOR/SIDEWALK/PARKING LOT/STREET OLIVE BAR
187 WINDSOR PARK
188 STREET
189
190
191 VACANT RESIDENCE
192
193
194 CROWN FOOD MART
195
196 JULIAN AT HODIAMONT
197
198 BING LAU
199
200
201
202
203
204 REAR
205
206
207
208 APT A
209 REAR
210
211
212
213 APARTMENT COMPLEX GRAVOIS PLACE APARTMENTS
214 REAR ALLEY
215
216
217
218 ST. LOUIS HOUSING AUTHORITY CLINTON PEABODY PUBLIC HOUSING COMP
219
220
221 REAR
222
223
224
225
226
227
228
229
230
231
232
233
234 REAR GARAGE
235
236
237
238 WINTER GARDEN APARTMENTS
239
240
241
242
243
244 THE ELLENWOOD BUILDING
245 FAIRGROUNDS PARK FAIRGROUNDS PARK
246 @ROOSEVELT HIGH SCHOOL
247
248 ALLEY
249
250
251
252
253
254
255
256
257
258 EAST SIDE OF STREET IN FRONT OF A VA
259
260
261
262
263 2ND FLOOR
264
265 MIDWEST PETROLEUM
266
267 STREET
268
269
270
271
272
273
274 DUPLEX ATTACHED TO 2641 DALTON
275
276 HAROLD S CHOP SUEY
277
278 APARTMENT RIVERVIEW APARTMENTS
279
280
281
282
283 @HOTEL- ECONOMY INN
284
285
286
287 ALLEY
288
289
290 APT 412
291
292 NUMERICAL ADDRESS OF THE PRIMARY SC
293 REAR ALLEY
294
295 @DOMINOS PIZZA - N 13TH
296
297
298 ALLEY/DUMPSTER
299 INTERSECTION OF E DE SOTO AVENUE AND
300 APT C
301
302
303
304 VACANT LOT
305
306 APARTMENT COMPLEX CAROLINE PLACE APARTMENTS
307
308
309
310 REAR ALLEY
311
312
313
314
315 APT 3E
316 PARKING LOT MARTIN LUTHER KING PLAZA
317
318
319
320
321
322
323 STREET
324
325
326
327
328
329
330
331
332 REAR SCHMIDT EQUIPMENT AND SUPPLY
333
334
335 LOVES TRUCK STOP
336 HANK S PACKAGE LIQUOR
337
338 @BAR-BASTILLE BASTILLE
339 BALL PARK VILLAGE BALLPARK VILLAGE / BUDWEISER BREW H
340
341
342
343
344
345
346 ON STREET
347 1ST FL
348
349
350
351
352
353 GOODFELLOW PLACE APARTMENTS
354
355
356
357
358
359
360
361
362
363 PARKING LOT CLINTON PEABODY
364
365 REAR EAST ALLEY
366
367
368 REAR
369 REAR DETACHED GARAGE- VACANT RESIDEN
Neighborhood ILEADSStreet ILEADSAddress Description
1 69 SHREVE 4257 HOMICIDE
2 62 9TH 1418 HOMICIDE
3 3 S 38TH ST 5215 HOMICIDE
4 74 CHURCH 7944 HOMICIDE
5 50 5322 HOMICIDE
6 56 LABADIE 4446 HOMICIDE
7 53 EUCLID 1202 HOMICIDE
8 19 TEXAS 3709 HOMICIDE
9 17 ITASKA ST 3111 HOMICIDE
10 56 SAINT LOUIS 4751 HOMICIDE
11 1 HOLLY HILLS AVE 629 HOMICIDE
12 53 N KINGSHIGHWAY BLVD 1225 HOMICIDE
13 68 W GREEN LEA PL 4132 HOMICIDE
14 63 N 14TH ST 1908 HOMICIDE
15 67 N GRAND BLVD 4038 HOMICIDE
16 74 GIMBLIN ST 1018 HOMICIDE
17 57 ALDINE AVE 4337 HOMICIDE
18 50 BURD AVE 2524 HOMICIDE
19 59 FRANKLIN AVE 3114 HOMICIDE
20 65 KNAPP ST 3245 HOMICIDE
21 57 W COTE BRILLIANTE AVE 4229 HOMICIDE
22 8 KINSEY PL 6272 HOMICIDE
23 50 KENNERLY AVE 5972 HOMICIDE
24 50 GROVER ST 2521 HOMICIDE
25 56 DR MARTIN LUTHER KING DR 4621 HOMICIDE
26 50 COTE BRILLIANTE AVE 5895 HOMICIDE
27 48 HAMILTON AVE 1021 HOMICIDE
28 36 N 15TH ST 709 HOMICIDE
29 68 NATURAL BRIDGE AVE 4231 HOMICIDE
30 17 MICHIGAN AVE 4461 HOMICIDE
31 74 BADEN AVE 724 HOMICIDE
32 39 I 64 WESTBOUND AT CLAYTON AVE 0 HOMICIDE
33 19 S GRAND BLVD 3630 HOMICIDE
34 14 HEREFORD ST 3322 HOMICIDE
35 19 S COMPTON AVE 3751 HOMICIDE
36 51 DR MARTIN LUTHER KING DR 5084 HOMICIDE
37 1 VIRGINIA AVE 5301 HOMICIDE
38 78 CLARA AVE 1401 HOMICIDE
39 22 CONGRESS ST 1909 HOMICIDE
40 67 LEE AVE 3836 HOMICIDE
41 57 W COTE BRILLIANTE AVE 4267 HOMICIDE
42 64 N BROADWAY 4828 HOMICIDE
43 57 BILLUPS AVE 1707 HOMICIDE
44 74 LOWELL ST 8861 HOMICIDE
45 78 MINERVA AVE 5634 HOMICIDE
46 17 OSCEOLA ST 3110 HOMICIDE
47 60 RAUSCHENBACH AVE 3117 HOMICIDE
48 70 MCARTHUR AVE 5910 HOMICIDE
49 59 DR MARTIN LUTHER KING DR 3731 HOMICIDE
50 74 RIVERVIEW BLVD 1124 HOMICIDE
51 15 POTOMAC ST 3949 HOMICIDE
52 5 WALLACE AVE 4314 HOMICIDE
53 50 BURD AVE 2728 HOMICIDE
54 50 UNION BLVD 2611 HOMICIDE
55 49 VERNON AVE 5326 HOMICIDE
56 56 N NEWSTEAD AVE 3224 HOMICIDE
57 59 GAMBLE ST 2900 HOMICIDE
58 76 ERA AVE 5530 HOMICIDE
59 25 LOUISIANA AVE 2328 HOMICIDE
60 15 CONNECTICUT ST 3634 HOMICIDE
61 50 SAINT LOUIS AVE 5920 HOMICIDE
62 71 EMERSON AVE 5416 HOMICIDE
63 33 DILLON DR 1124 HOMICIDE
64 68 NATURAL BRIDGE AVE 4231 HOMICIDE
65 69 CARRIE AVE 4539 HOMICIDE
66 60 N 13TH ST 1513 HOMICIDE
67 71 SHREVE AVE 5318 HOMICIDE
68 19 S COMPTON AVE 3720 HOMICIDE
69 50 PALM ST 5556 HOMICIDE
70 61 COCHRAN PL 1459 HOMICIDE
71 56 GARFIELD AVE 3915 HOMICIDE
72 72 THRUSH AVE 5400 HOMICIDE
73 16 MERAMEC ST 3711 HOMICIDE
74 76 FLOY AVE 5592 HOMICIDE
75 72 ROBIN AVE 5598 HOMICIDE
76 2 W DAVIS ST 547 HOMICIDE
77 74 SWITZER AVE 951 HOMICIDE
78 67 FAIRGROUNDS PL 4100 HOMICIDE
79 56 DICK GREGORY PL 1524 HOMICIDE
80 18 CHIPPEWA ST 2605 HOMICIDE
81 65 N 21ST ST 3915 HOMICIDE
82 66 E PRAIRIE AVE 2009 HOMICIDE
83 74 FREDERICK ST 8216 HOMICIDE
84 77 JOSEPHINE BAKER AVE 700 HOMICIDE
85 50 HIGHLAND AVE 5627 HOMICIDE
86 1 IDAHO AVE 6440 HOMICIDE
87 78 SEMPLE AVE 1416 HOMICIDE
88 74 RIVERVIEW BLVD 1052 HOMICIDE
89 72 W FLORISSANT AVE 5728 HOMICIDE
90 74 N BROADWAY 8216 HOMICIDE
91 19 LOUISIANA AVE 3513 HOMICIDE
92 69 FARLIN AVE 4892 HOMICIDE
93 72 PLOVER AVE 4936 HOMICIDE
94 69 N NEWSTEAD AVE 4149 HOMICIDE
95 51 ENRIGHT AVE 5048 HOMICIDE
96 56 LABADIE AVE 4435 HOMICIDE
97 59 N VANDEVENTER 2815 HOMICIDE
98 17 MINNESOTA AVE 4629 HOMICIDE
99 55 HIGHLAND AVE 4900 HOMICIDE
100 16 KEOKUK ST 3836 HOMICIDE
101 56 LEXINGTON AVE 3963 HOMICIDE
102 69 KOSSUTH AVE 4834 HOMICIDE
103 68 RED BUD AVE 3980 HOMICIDE
104 15 GILES AVE 3521 HOMICIDE
105 50 DR MARTIN LUTHER KING DR 5971 HOMICIDE
106 78 BELT AVE 1431 HOMICIDE
107 78 BLACKSTONE AVE 1387 HOMICIDE
108 16 LOUISIANA AVE 4114 HOMICIDE
109 67 BAILEY AVE 3215 HOMICIDE
110 58 PENDLETON AVE 924 HOMICIDE
111 33 HICKORY LN 1435 HOMICIDE
112 77 N VANDEVENTER AVE 1420 HOMICIDE
113 72 ROBIN AVE 5428 HOMICIDE
114 79 E CARRIE AVE / I 70 WESTBOUND 0 HOMICIDE
115 54 MCMILLAN AVE 4561 HOMICIDE
116 18 WISCONSIN AVE 3853 HOMICIDE
117 59 PRAIRIE AVE 3614 HOMICIDE
118 17 MICHIGAN AVE 4622 HOMICIDE
119 76 GOODFELLOW BLVD 5000 HOMICIDE
120 61 MURPHY PARK DR 1847 HOMICIDE
121 59 N GRAND BLVD 2546 HOMICIDE
122 48 N SKINKER BLVD 882 HOMICIDE
123 60 W PALM ST 2522 HOMICIDE
124 16 OSAGE ST 3019 HOMICIDE
125 78 MINERVA AVE 5363 HOMICIDE
126 17 MICHIGAN AVE 4404 HOMICIDE
127 16 CALIFORNIA AVE 4056 HOMICIDE
128 16 MERAMEC ST 3145 HOMICIDE
129 16 MINNESOTA AVE 3754 HOMICIDE
130 1 IDAHO AVE 5417 HOMICIDE
131 2 VERMONT AVE 7315 HOMICIDE
132 57 ALDINE AVE 4349 HOMICIDE
133 56 PALM ST 3926 HOMICIDE
134 38 PARKVIEW PL 4921 HOMICIDE
135 53 N KINGSHIGHWAY BLVD 900 HOMICIDE
136 56 N MARKET ST 4647 HOMICIDE
137 59 N MARKET ST 3800 HOMICIDE
138 30 VIRGINIA AVE 3429 HOMICIDE
139 54 MCMILLAN AVE 4735 HOMICIDE
140 56 N TAYLOR AVE 3120 HOMICIDE
141 65 N FLORISSANT AVE 3330 HOMICIDE
142 76 GOODFELLOW BLVD 5003 HOMICIDE
143 16 KLOCKE ST 3400 HOMICIDE
144 76 GOODFELLOW BLVD 5003 HOMICIDE
145 55 N EUCLID AVE 2944 HOMICIDE
146 66 E JOHN AVE 1439 HOMICIDE
147 73 NORTH POINTE BLVD 6139 HOMICIDE
148 15 GUSTINE AVE 3619 HOMICIDE
149 5 DELOR ST 4254 HOMICIDE
150 58 C D BANKS AVE 4158 HOMICIDE
151 65 N 14TH ST 3504 HOMICIDE
152 25 TENNESSEE AVE 2601 HOMICIDE
153 1 BATES ST 535 HOMICIDE
154 69 PENROSE ST 4481 HOMICIDE
155 50 THEODOSIA AVE 5606 HOMICIDE
156 69 SHREVE AVE 4049 HOMICIDE
157 56 GARFIELD AVE 4040 HOMICIDE
158 37 SAMUEL SHEPARD DR 2946 HOMICIDE
159 35 N 4TH ST 300 HOMICIDE
160 84 I 70 WESTBOUND / N KINGSHIGHWA 0 HOMICIDE
161 50 ROOSEVELT PL 5816 HOMICIDE
162 16 CHIPPEWA ST 3013 HOMICIDE
163 0 UNKNOWN 0 HOMICIDE
164 72 PLOVER AVE 4938 HOMICIDE
165 68 CLARENCE AVE 4415 HOMICIDE
166 55 DR MARTIN LUTHER KING DR 4815 HOMICIDE
167 50 GOODFELLOW BLVD 2747 HOMICIDE
168 5 GRAVOIS AVE 5401 HOMICIDE
169 0 UNKNOWN 0 HOMICIDE
170 50 UNION BLVD 3431 HOMICIDE
171 72 W FLORISSANT AVE 5500 HOMICIDE
172 69 RICHARD PL 4625 HOMICIDE
173 17 S BROADWAY 4355 HOMICIDE
174 76 EMMA AVE 6307 HOMICIDE
175 48 ENRIGHT AVE 5616 HOMICIDE
176 55 MARCUS AVE 2613 HOMICIDE
177 76 SHERRY AVE 6120 HOMICIDE
178 51 RIDGE AVE 5140 HOMICIDE
179 35 CHESTNUT ST 714 HOMICIDE
180 66 N 19TH ST 4406 HOMICIDE
181 57 SAINT FERDINAND AVE 4370 HOMICIDE
182 63 N MARKET PL 1101 HOMICIDE
183 52 ST LOUIS AVE 5105 HOMICIDE
184 62 N 13TH ST 1420 HOMICIDE
185 35 N 9TH ST 205 HOMICIDE
186 37 OLIVE ST 3037 HOMICIDE
187 65 BLAIR AVE 4109 HOMICIDE
188 17 NEBRASKA AVE 4528 HOMICIDE
189 54 MCMILLAN AVE 4503 HOMICIDE
190 25 ARKANSAS AVE 3170 HOMICIDE
191 50 WABADA AVE 5969 HOMICIDE
192 54 ENRIGHT AVE 4550 HOMICIDE
193 74 HOWELL ST 1181 HOMICIDE
194 53 N KINGSHIGHWAY BLVD 930 HOMICIDE
195 74 HALLS FERRY RD 9006 HOMICIDE
196 48 JULIAN AVE 5985 HOMICIDE
197 74 HOWELL ST 1115 HOMICIDE
198 59 N GRAND BLVD 3101 HOMICIDE
199 56 GARFIELD AVE 4012 HOMICIDE
200 77 FRANKLIN AVE 3311 HOMICIDE
201 19 IOWA AVE 3420 HOMICIDE
202 53 AUBERT AVE 773 HOMICIDE
203 54 NEWBERRY TER 4502 HOMICIDE
204 61 HOGAN ST 1320 HOMICIDE
205 16 ALASKA AVE 4630 HOMICIDE
206 53 N KINGSHIGHWAY BLVD 1408 HOMICIDE
207 21 ALLEN AVE 1051 HOMICIDE
208 60 BENTON ST 1933 HOMICIDE
209 76 SHERRY AVE 6341 HOMICIDE
210 69 SEXAUER AVE 4420 HOMICIDE
211 1 IDAHO AVE 7138 HOMICIDE
212 48 GOODFELLOW BLVD 853 HOMICIDE
213 4 GRAVOIS AVE 7422 HOMICIDE
214 76 SUMMIT AVE 5629 HOMICIDE
215 75 RIVERVIEW DR / WB 270 0 HOMICIDE
216 44 W PARK AVE 6763 HOMICIDE
217 54 PAGE BLVD 4711 HOMICIDE
218 33 HICKORY LN 1468 HOMICIDE
219 78 DR MARTIN LUTHER KING DR 5390 HOMICIDE
220 78 RIDGE AVE 5368 HOMICIDE
221 59 STODDARD ST 2800 HOMICIDE
222 63 N 7TH ST / I 70 WESTBOUND 0 HOMICIDE
223 30 PENNSYLVANIA AVE 3244 HOMICIDE
224 78 RIDGE AVE 5401 HOMICIDE
225 71 N KINGSHIGHWAY BLVD 5406 HOMICIDE
226 71 ARLINGTON AVE 4943 HOMICIDE
227 69 KOSSUTH AVE 4863 HOMICIDE
228 63 CLINTON ST 1200 HOMICIDE
229 36 LOCUST ST 1527 HOMICIDE
230 56 WHITTIER ST 3047 HOMICIDE
231 37 S GRAND BLVD 715 HOMICIDE
232 72 EMERSON AVE 4921 HOMICIDE
233 50 PATTON AVE 5331 HOMICIDE
234 69 LEE AVE 4440 HOMICIDE
235 52 UNION BLVD 2700 HOMICIDE
236 59 MONTGOMERY ST 3561 HOMICIDE
237 50 HIGHLAND AVE 5824 HOMICIDE
238 46 KINGSBURY PL 5708 HOMICIDE
239 59 JAMES COOL PAPA BELL AVE 2920 HOMICIDE
240 67 SHERMAN PL 3921 HOMICIDE
241 59 COTTAGE AVE 3842 HOMICIDE
242 66 E LINTON AVE 2157 HOMICIDE
243 72 GILMORE AVE 5276 HOMICIDE
244 5 MORGANFORD RD 4528 HOMICIDE
245 83 FAIRGROUNDS PARK DR 3900 HOMICIDE
246 25 HARTFORD ST 3230 HOMICIDE
247 55 N EUCLID AVE 2944 HOMICIDE
248 65 PENROSE ST 2115 HOMICIDE
249 50 WABADA AVE 5324 HOMICIDE
250 67 LEE AVE 3838 HOMICIDE
251 50 MAFFITT AVE 5870 HOMICIDE
252 56 DR MARTIN LUTHER KING DR 4557 HOMICIDE
253 52 SAINT LOUIS AVE 5027 HOMICIDE
254 1 PENNSYLVANIA AVE 5913 HOMICIDE
255 56 LEXINGTON AVE 4476 HOMICIDE
256 53 AUBERT AVE 1224 HOMICIDE
257 74 CANAAN AVE 915 HOMICIDE
258 57 N TAYLOR AVE 2814 HOMICIDE
259 55 MARCUS AVE 3061 HOMICIDE
260 58 EVANS AVE 4220 HOMICIDE
261 69 CARTER AVE 4836 HOMICIDE
262 16 MINNESOTA AVE 3754 HOMICIDE
263 56 LABADIE AVE 3945 HOMICIDE
264 59 BACON ST 2409 HOMICIDE
265 56 N VANDEVENTER AVE 2821 HOMICIDE
266 72 WREN AVE 5019 HOMICIDE
267 36 S 18TH ST / CLARK AVE 0 HOMICIDE
268 59 BELL AVE 3100 HOMICIDE
269 1 SCHIRMER ST 802 HOMICIDE
270 16 ALBERTA ST 3921 HOMICIDE
271 2 PENNSYLVANIA AVE 7403 HOMICIDE
272 16 DELOR ST 3659 HOMICIDE
273 66 RANDALL PL 4426 HOMICIDE
274 13 DALTON AVE 2639 HOMICIDE
275 73 MORA LN 8561 HOMICIDE
276 51 UNION BLVD 1122 HOMICIDE
277 55 HAMMETT PL 4851 HOMICIDE
278 74 HALLS FERRY RD 8612 HOMICIDE
279 78 HAMILTON AVE 1452 HOMICIDE
280 76 SHULTE AVE 6035 HOMICIDE
281 68 CARTER AVE 4042 HOMICIDE
282 53 N EUCLID AVE 785 HOMICIDE
283 67 N GRAND BLVD 4206 HOMICIDE
284 1 ALABAMA AVE 7146 HOMICIDE
285 74 ELIAS AVE 933 HOMICIDE
286 76 ACME AVE 5636 HOMICIDE
287 74 CHURCH RD 8309 HOMICIDE
288 55 LABADIE AVE 4843 HOMICIDE
289 72 WREN AVE 5055 HOMICIDE
290 36 N 21ST ST 715 HOMICIDE
291 1 LOUISIANA AVE 5205 HOMICIDE
292 63 CLINTON ST 1455 HOMICIDE
293 72 EMERSON AVE 5201 HOMICIDE
294 60 E DODIER ST 2511 HOMICIDE
295 62 N 13TH ST 1430 HOMICIDE
296 69 MARCUS AVE 4100 HOMICIDE
297 63 CHAMBERS ST 1120 HOMICIDE
298 68 E KOSSUTH AVE 4235 HOMICIDE
299 66 E DE SOTO AVE 1401 HOMICIDE
300 17 WALSH ST 308 HOMICIDE
301 16 S GRAND BLVD 3900 HOMICIDE
302 0 UNKNOWN 0 HOMICIDE
303 55 CUPPLES PL 4741 HOMICIDE
304 2 VERMONT AVE 7343 HOMICIDE
305 19 VIRGINIA AVE 3620 HOMICIDE
306 31 HICKORY ST 2651 HOMICIDE
307 74 ELIAS AVE 827 HOMICIDE
308 74 CANAAN AVE 971 HOMICIDE
309 70 GOODFELLOW BLVD 4301 HOMICIDE
310 69 SHREVE AVE 4106 HOMICIDE
311 39 NORFOLK AVE 4247 HOMICIDE
312 56 NEW ASHLAND PL 3100 HOMICIDE
313 27 SHENANDOAH AVE 3658 HOMICIDE
314 19 OHIO AVE 3454 HOMICIDE
315 14 CHIPPEWA ST 4939 HOMICIDE
316 58 DR MARTIN LUTHER KING DR 4308 HOMICIDE
317 16 PENNSYLVANIA AVE 3942 HOMICIDE
318 15 MERAMEC ST 4255 HOMICIDE
319 50 GOODFELLOW BLVD 3351 HOMICIDE
320 72 WREN AVE 5270 HOMICIDE
321 16 DUNNICA AVE 3946 HOMICIDE
322 60 MADISON ST 1501 HOMICIDE
323 35 CARR ST 202 HOMICIDE
324 73 GOODFELLOW BLVD 5961 HOMICIDE
325 69 FARLIN AVE 4435 HOMICIDE
326 74 RIVERVIEW BLVD 907 HOMICIDE
327 71 GERALDINE AVE 5300 HOMICIDE
328 50 WABADA AVE 5962 HOMICIDE
329 52 TERRY AVE 5252 HOMICIDE
330 50 CLARA AVE 3340 HOMICIDE
331 72 DAVISON AVE 5271 HOMICIDE
332 17 EICHELBERGER ST 411 HOMICIDE
333 1 BATES ST 1005 HOMICIDE
334 78 ARLINGTON AVE 1460 HOMICIDE
335 79 N BROADWAY 6124 HOMICIDE
336 78 ARLINGTON AVE 1401 HOMICIDE
337 59 LABADIE AVE 3850 HOMICIDE
338 21 RUSSELL AVE 1027 HOMICIDE
339 35 CLARK AVE 601 HOMICIDE
340 78 HAMILTON TER 5946 HOMICIDE
341 39 MANCHESTER AVE 4229 HOMICIDE
342 66 CONDE ST 5220 HOMICIDE
343 50 HIGHLAND AVE 5971 HOMICIDE
344 72 ROBIN AVE 5016 HOMICIDE
345 69 FARLIN AVE 4950 HOMICIDE
346 61 COCHRAN PL 1457 HOMICIDE
347 2 UPTON ST 225 HOMICIDE
348 16 MICHIGAN AVE 4108 HOMICIDE
349 15 LACKLAND AVE 3144 HOMICIDE
350 74 ETON LN 1545 HOMICIDE
351 74 BITTNER ST 732 HOMICIDE
352 16 TENNESSEE AVE 4758 HOMICIDE
353 50 GOODFELLOW PL 5713 HOMICIDE
354 19 MINNESOTA AVE 3741 HOMICIDE
355 51 MINERVA AVE 5228 HOMICIDE
356 68 ATHLONE AVE 4448 HOMICIDE
357 74 HARLAN AVE 916 HOMICIDE
358 71 CLAXTON AVE 5381 HOMICIDE
359 70 FERRIS AVE 5911 HOMICIDE
360 54 EVANS AVE 4527 HOMICIDE
361 54 PAGE BLVD 4630 HOMICIDE
362 50 COTE BRILLIANTE AVE 5951 HOMICIDE
363 33 RUTGER LN 1417 HOMICIDE
364 68 E ALICE AVE 2011 HOMICIDE
365 19 NEBRASKA AVE 3646 HOMICIDE
366 56 LEXINGTON AVE 4520 HOMICIDE
367 68 CLAY AVE 4221 HOMICIDE
368 50 BELT AVE 2738 HOMICIDE
369 51 MAPLE AVE 5009 HOMICIDE
District Crime DateOccur CodedMonth Complaint
1 6 10000 2016-06-09 08:28:00 2018-06-28 16-027833
2 4 10000 2017-09-01 13:44:00 2018-12-28 17-043306
3 1 10000 2017-02-22 23:45:00 2018-02-28 18-008475
4 6 10000 2018-06-10 00:01:00 2019-03-28 18-026258
5 5 10000 2019-03-17 13:03:00 2019-12-28 19-011818
6 6 10000 2018-05-28 00:01:00 2018-11-28 18-023972
7 5 10000 2018-05-02 17:05:00 2018-10-28 18-019567
8 3 10000 2018-07-27 20:15:00 2018-11-28 18-034672
9 1 10000 2018-03-17 23:00:00 2018-04-28 18-012176
10 5 10000 2018-03-28 18:15:00 2018-04-28 18-013752
11 1 10000 2019-06-27 01:48:00 2019-07-28 19-030335
12 5 10000 2019-10-30 21:07:00 2019-11-28 19-054615
13 6 10000 2018-12-01 17:37:00 2018-12-28 18-056958
14 4 10000 2018-11-01 23:00:00 2018-11-28 18-052272
15 4 10000 2018-11-01 13:07:00 2018-11-28 18-052152
16 6 10000 2018-08-01 23:03:00 2018-08-28 18-035664
17 5 10000 2018-05-01 21:20:00 2018-05-28 18-019411
18 5 10000 2018-05-01 12:47:00 2018-05-28 18-019311
19 4 10000 2018-05-01 11:00:00 2018-05-28 18-020625
20 4 10000 2018-03-01 18:30:00 2018-03-28 18-009700
21 5 10000 2018-03-01 08:50:00 2018-03-28 18-009431
22 2 10000 2018-02-01 16:30:00 2018-02-28 18-005350
23 5 10000 2018-01-01 17:00:00 2018-01-28 18-000142
24 5 10000 2019-04-01 15:29:00 2019-04-28 19-014217
25 5 10000 2019-06-01 01:03:00 2019-06-28 19-025297
26 5 10000 2019-06-01 02:48:00 2019-06-28 19-025312
27 5 10000 2019-09-01 04:15:00 2019-09-28 19-043120
28 4 10000 2019-11-01 02:22:00 2019-11-28 19-054833
29 6 10000 2018-12-02 15:16:00 2018-12-28 18-057082
30 1 10000 2018-08-02 14:02:00 2018-08-28 18-035796
31 6 10000 2018-07-02 22:00:00 2018-07-28 18-030399
32 2 10000 2018-06-02 14:37:00 2018-06-28 18-024858
33 3 10000 2018-06-02 01:34:00 2018-06-28 18-024776
34 2 10000 2018-05-02 19:00:00 2018-05-28 18-019792
35 3 10000 2018-05-02 15:01:00 2018-05-28 18-019544
36 5 10000 2018-02-02 23:25:00 2018-02-28 18-005382
37 1 10000 2019-06-02 08:52:00 2019-06-28 19-025531
38 5 10000 2019-06-02 10:05:00 2019-06-28 19-025525
39 3 10000 2018-10-02 17:45:00 2018-10-28 18-046912
40 6 10000 2018-10-02 20:35:00 2018-10-28 18-046933
41 5 10000 2018-10-02 23:40:00 2018-10-28 18-046944
42 6 10000 2018-12-03 01:24:00 2018-12-28 18-057161
43 5 10000 2018-07-03 17:34:00 2018-07-28 18-030526
44 6 10000 2018-04-03 18:00:00 2018-04-28 18-014712
45 5 10000 2019-01-03 13:36:00 2019-01-28 19-000429
46 1 10000 2019-02-03 19:30:00 2019-02-28 19-005180
47 4 10000 2019-06-03 06:03:00 2019-06-28 19-025632
48 6 10000 2019-07-03 08:31:00 2019-07-28 19-031517
49 4 10000 2019-10-03 15:02:00 2019-10-28 19-049602
50 6 10000 2018-11-04 21:05:00 2018-11-28 18-052745
51 2 10000 2018-07-04 23:45:00 2018-07-28 18-030719
52 1 10000 2018-06-04 21:43:00 2018-06-28 18-025251
53 5 10000 2019-03-04 20:03:00 2019-03-28 19-009857
54 5 10000 2019-05-04 03:13:00 2019-05-28 19-020139
55 5 10000 2019-05-04 10:40:00 2019-05-28 19-020183
56 6 10000 2019-06-04 01:40:00 2019-06-28 19-025830
57 4 10000 2019-07-04 21:55:00 2019-07-28 19-031835
58 6 10000 2019-10-04 23:45:00 2019-10-28 19-049868
59 3 10000 2018-10-04 09:00:00 2018-10-28 18-047230
60 2 10000 2018-12-05 16:45:00 2018-12-28 18-057634
61 5 10000 2018-09-05 08:18:00 2018-09-28 18-041936
62 6 10000 2018-08-05 21:10:00 2018-08-28 18-036365
63 3 10000 2018-07-05 21:26:00 2018-07-28 18-030894
64 6 10000 2018-07-05 19:44:00 2018-07-28 18-030877
65 6 10000 2018-07-05 11:18:00 2018-07-28 18-030812
66 4 10000 2018-07-05 00:20:00 2018-07-28 18-030729
67 6 10000 2018-03-05 00:45:00 2018-03-28 18-009971
68 3 10000 2018-01-05 21:45:00 2018-01-28 18-000847
69 5 10000 2019-03-05 13:00:00 2019-03-28 19-009999
70 4 10000 2019-08-05 01:18:00 2019-08-28 19-037801
71 5 10000 2019-09-05 00:51:00 2019-09-28 19-043868
72 6 10000 2019-09-05 13:25:00 2019-09-28 19-043969
73 1 10000 2019-11-05 09:26:00 2019-11-28 19-055608
74 6 10000 2018-09-06 19:09:00 2018-09-28 18-042231
75 6 10000 2018-07-06 21:42:00 2018-07-28 18-031078
76 1 10000 2018-06-06 05:40:00 2018-06-28 18-025503
77 6 10000 2018-05-06 16:25:00 2018-05-28 18-020241
78 6 10000 2018-05-06 13:22:00 2018-05-28 18-020208
79 5 10000 2018-04-06 20:10:00 2018-04-28 18-015241
80 3 10000 2019-01-06 15:30:00 2019-01-28 19-000915
81 4 10000 2019-04-06 15:35:00 2019-04-28 19-015072
82 6 10000 2019-04-06 21:50:00 2019-04-28 19-015262
83 6 10000 2019-05-06 23:15:00 2019-05-28 19-020657
84 4 10000 2019-06-06 11:58:00 2019-06-28 19-026300
85 5 10000 2019-11-06 23:00:00 2019-11-28 19-055927
86 1 10000 2018-10-06 09:46:00 2018-10-28 18-047573
87 5 10000 2019-12-06 21:15:00 2019-12-28 19-061107
88 6 10000 2018-08-07 22:30:00 2018-08-28 18-036817
89 6 10000 2018-06-07 04:04:00 2018-06-28 18-025671
90 6 10000 2019-01-07 22:09:00 2019-01-28 19-001140
91 3 10000 2019-07-07 01:45:00 2019-07-28 19-032275
92 6 10000 2019-07-07 13:56:00 2019-07-28 19-032327
93 6 10000 2019-08-07 16:26:00 2019-08-28 19-038332
94 6 10000 2019-09-07 00:45:00 2019-09-28 19-044294
95 5 10000 2019-10-07 10:01:00 2019-10-28 19-050231
96 6 10000 2018-09-08 20:39:00 2018-09-28 18-042567
97 4 10000 2018-08-08 22:35:00 2018-08-28 18-037014
98 1 10000 2018-07-08 19:20:00 2018-07-28 18-031390
99 5 10000 2019-02-08 11:55:00 2019-02-28 19-005986
100 1 10000 2019-04-08 18:30:00 2019-04-28 19-015480
101 6 10000 2019-05-08 21:28:00 2019-05-28 19-021058
102 6 10000 2019-05-08 21:35:00 2019-05-28 19-021064
103 6 10000 2019-06-08 22:30:00 2019-06-28 19-026743
104 2 10000 2019-10-08 21:23:00 2019-10-28 19-050574
105 5 10000 2018-10-08 17:14:00 2018-10-28 18-047961
106 5 10000 2018-10-08 18:41:00 2018-10-28 18-047980
107 5 10000 2018-10-08 22:00:00 2018-10-28 18-048002
108 3 10000 2018-12-09 00:01:00 2018-12-28 18-058563
109 4 10000 2018-07-09 21:12:00 2018-07-28 18-031594
110 5 10000 2018-03-09 15:00:00 2018-03-28 18-010716
111 3 10000 2018-02-09 21:09:00 2018-02-28 18-006392
112 4 10000 2018-01-09 17:31:00 2018-01-28 18-001431
113 6 10000 2019-01-09 00:10:00 2019-01-28 19-001348
114 6 10000 2019-01-09 22:00:00 2019-01-28 19-001524
115 5 10000 2019-04-09 17:58:00 2019-04-28 19-015697
116 3 10000 2019-04-09 22:45:00 2019-04-28 19-015726
117 4 10000 2019-06-09 15:56:00 2019-06-28 19-026862
118 1 10000 2019-06-09 20:32:00 2019-06-28 19-026905
119 6 10000 2019-08-09 13:54:00 2019-08-28 19-038644
120 4 10000 2019-08-09 22:30:00 2019-08-28 19-038720
121 4 10000 2019-11-09 01:50:00 2019-11-28 19-056263
122 5 10000 2018-10-09 20:00:00 2018-10-28 18-048475
123 4 10000 2019-12-09 12:05:00 2019-12-28 19-061480
124 3 10000 2019-12-09 12:50:00 2019-12-28 19-061497
125 5 10000 2018-09-10 21:49:00 2018-09-28 18-042941
126 1 10000 2018-05-10 20:10:00 2018-05-28 18-020963
127 3 10000 2018-03-10 01:04:00 2018-03-28 18-010757
128 3 10000 2019-07-10 11:28:00 2019-07-28 19-032846
129 3 10000 2018-10-10 09:48:00 2018-10-28 18-048273
130 1 10000 2018-09-11 22:20:00 2018-09-28 18-043140
131 1 10000 2018-07-11 17:40:00 2018-07-28 18-031935
132 5 10000 2018-06-11 14:41:00 2018-06-28 18-026460
133 6 10000 2018-05-11 16:47:00 2018-05-28 18-021101
134 5 10000 2018-01-11 10:28:00 2018-01-28 18-001737
135 5 10000 2019-02-11 16:00:00 2019-02-28 19-007995
136 5 10000 2019-02-11 20:50:00 2019-02-28 19-006513
137 4 10000 2019-02-11 22:00:00 2019-02-28 19-006512
138 3 10000 2019-04-11 19:40:00 2019-04-28 19-016075
139 5 10000 2019-07-11 00:42:00 2019-07-28 19-032961
140 6 10000 2019-07-11 18:00:00 2019-07-28 19-033107
141 4 10000 2019-08-11 02:00:00 2019-08-28 19-038947
142 6 10000 2019-09-11 23:27:00 2019-09-28 19-045265
143 3 10000 2019-11-11 15:54:00 2019-11-28 19-056681
144 6 10000 2018-12-12 12:53:00 2018-12-28 18-058750
145 6 10000 2018-05-12 21:15:00 2018-05-28 18-021274
146 6 10000 2018-02-12 19:39:00 2018-02-28 18-006829
147 6 10000 2018-01-12 22:47:00 2018-01-28 18-001995
148 2 10000 2018-01-12 10:00:00 2018-01-28 18-001895
149 1 10000 2019-01-12 22:22:00 2019-01-28 19-001896
150 5 10000 2019-03-12 00:45:00 2019-03-28 19-010953
151 4 10000 2019-08-12 17:06:00 2019-08-28 19-039284
152 3 10000 2019-12-12 18:20:00 2019-12-28 19-062140
153 1 10000 2019-12-12 21:22:00 2019-12-28 19-062167
154 6 10000 2018-08-13 23:55:00 2018-08-28 18-037897
155 5 10000 2018-06-13 22:34:00 2018-06-28 18-026927
156 6 10000 2019-05-13 20:52:00 2019-05-28 19-021920
157 5 10000 2019-09-13 18:43:00 2019-09-28 19-045605
158 4 10000 2019-10-13 18:46:00 2019-10-28 19-051389
159 4 10000 2018-10-13 00:18:00 2018-10-28 18-048718
160 6 10000 2018-12-14 03:46:00 2018-12-28 18-058996
161 5 10000 2018-08-14 09:15:00 2018-08-28 18-037957
162 3 10000 2019-06-14 23:50:00 2019-06-28 19-027970
163 0 10000 2019-09-14 03:50:00 2019-09-28 19-045679
164 6 10000 2018-10-14 00:27:00 2018-10-28 18-048841
165 6 10000 2018-11-15 19:18:00 2018-11-28 18-054501
166 5 10000 2018-09-15 06:30:00 2018-09-28 18-044019
167 5 10000 2018-07-15 13:32:00 2018-07-28 18-032477
168 1 10000 2018-04-15 01:11:00 2018-04-28 18-016585
169 0 10000 2019-01-15 13:24:00 2019-01-28 19-002283
170 5 10000 2019-01-15 15:12:00 2019-01-28 19-002278
171 6 10000 2019-06-15 09:37:00 2019-06-28 19-028025
172 6 10000 2019-06-15 11:10:00 2019-06-28 19-028033
173 1 10000 2019-07-15 02:54:00 2019-07-28 19-033672
174 6 10000 2018-10-15 22:32:00 2018-10-28 18-049140
175 5 10000 2019-12-15 00:10:00 2019-12-28 19-062475
176 5 10000 2018-12-16 07:38:00 2018-12-28 18-059333
177 6 10000 2018-09-16 16:10:00 2018-09-28 18-044013
178 5 10000 2018-08-16 04:00:00 2018-08-28 18-038296
179 4 10000 2018-06-16 12:34:00 2018-06-28 18-027359
180 4 10000 2018-06-16 04:30:00 2018-06-28 18-027307
181 5 10000 2019-01-16 23:48:00 2019-01-28 19-002489
182 4 10000 2019-08-16 17:13:00 2019-08-28 19-040096
183 5 10000 2019-08-16 22:21:00 2019-08-28 19-040121
184 4 10000 2019-10-16 21:01:00 2019-10-28 19-052008
185 4 10000 2018-08-17 10:48:00 2018-08-28 18-038533
186 4 10000 2018-06-17 23:55:00 2018-06-28 18-027605
187 4 10000 2018-06-17 00:05:00 2018-06-28 18-027449
188 1 10000 2018-03-17 23:19:00 2018-03-28 18-012030
189 5 10000 2018-03-17 16:10:00 2018-03-28 18-011972
190 3 10000 2018-01-17 23:45:00 2018-01-28 18-002737
191 5 10000 2019-01-17 16:31:00 2019-01-28 19-002622
192 5 10000 2019-03-17 21:40:00 2019-03-28 19-011872
193 6 10000 2019-06-17 03:10:00 2019-06-28 19-028344
194 5 10000 2019-07-17 00:06:00 2019-07-28 19-034095
195 6 10000 2019-07-17 23:00:00 2019-07-28 19-034271
196 5 10000 2019-07-17 23:10:00 2019-07-28 19-034276
197 6 10000 2019-10-17 01:16:00 2019-10-28 19-052025
198 4 10000 2018-08-18 12:58:00 2018-08-28 18-038739
199 5 10000 2018-03-18 09:19:00 2018-03-28 18-012080
200 4 10000 2018-01-18 13:41:00 2018-01-28 18-002826
201 3 10000 2019-03-18 21:08:00 2019-03-28 19-012060
202 5 10000 2019-03-18 23:39:00 2019-03-28 19-012105
203 5 10000 2019-07-18 10:10:00 2019-07-28 19-034345
204 4 10000 2019-08-18 02:15:00 2019-08-28 19-040288
205 1 10000 2019-11-18 00:50:00 2019-11-28 19-057833
206 5 10000 2018-10-18 15:10:00 2018-10-28 18-049640
207 3 10000 2018-12-19 23:35:00 2018-12-28 18-059926
208 4 10000 2018-12-19 23:30:00 2018-12-28 18-059924
209 6 10000 2018-07-19 18:30:00 2018-07-28 18-033260
210 6 10000 2018-05-19 21:45:00 2018-05-28 18-022532
211 1 10000 2018-05-19 02:40:00 2018-05-28 18-022391
212 5 10000 2018-03-19 10:26:00 2018-03-28 18-012231
213 1 10000 2018-01-19 17:25:00 2018-01-28 18-003034
214 6 10000 2019-03-19 11:07:00 2019-03-28 19-012127
215 6 10000 2019-05-19 20:06:00 2019-05-28 19-023050
216 2 10000 2019-07-19 12:50:00 2019-07-28 19-034583
217 5 10000 2019-07-19 20:31:00 2019-07-28 19-034640
218 3 10000 2019-09-19 20:51:00 2019-09-28 19-046892
219 5 10000 2019-11-19 07:08:00 2019-11-28 19-058060
220 5 10000 2019-11-19 10:41:00 2019-11-28 19-058099
221 4 10000 2019-11-19 19:34:00 2019-11-28 19-058200
222 4 10000 2018-12-20 23:39:00 2018-12-28 18-060116
223 3 10000 2018-07-20 05:02:00 2018-07-28 18-033301
224 5 10000 2018-03-20 19:37:00 2018-03-28 18-012492
225 6 10000 2018-01-20 22:53:00 2018-01-28 18-003218
226 6 10000 2019-05-20 21:38:00 2019-05-28 19-023244
227 6 10000 2019-07-20 15:00:00 2019-07-28 19-034765
228 4 10000 2019-10-20 05:10:00 2019-10-28 19-052582
229 4 10000 2018-11-21 15:24:00 2018-11-28 18-055391
230 6 10000 2018-09-21 09:53:00 2018-09-28 18-044906
231 4 10000 2018-08-21 18:38:00 2018-08-28 18-039325
232 6 10000 2019-01-21 07:00:00 2019-01-28 19-003132
233 5 10000 2019-06-21 13:55:00 2019-06-28 19-029263
234 6 10000 2019-06-21 17:00:00 2019-06-28 19-029441
235 5 10000 2019-07-21 05:09:00 2019-07-28 19-034864
236 4 10000 2019-07-21 12:59:00 2019-07-28 19-034925
237 5 10000 2019-09-21 11:13:00 2019-09-28 19-047170
238 5 10000 2019-12-21 01:00:00 2019-12-28 19-063436
239 4 10000 2019-12-21 23:53:00 2019-12-28 19-063479
240 6 10000 2018-12-22 19:35:00 2018-12-28 18-060407
241 4 10000 2018-07-22 19:34:00 2018-07-28 18-033754
242 6 10000 2018-05-22 13:12:00 2018-05-28 18-022949
243 6 10000 2018-04-22 06:00:00 2018-04-28 18-017730
244 1 10000 2018-02-22 23:21:00 2018-02-28 18-008449
245 6 10000 2019-05-22 18:16:00 2019-05-28 19-023607
246 3 10000 2019-05-22 23:00:00 2019-05-28 19-023675
247 6 10000 2019-06-22 18:32:00 2019-06-28 19-029491
248 4 10000 2019-08-22 07:36:00 2019-08-28 19-041089
249 5 10000 2019-09-22 03:35:00 2019-09-28 19-047288
250 6 10000 2019-09-22 14:51:00 2019-09-28 19-047376
251 5 10000 2019-09-22 23:31:00 2019-09-28 19-047468
252 5 10000 2019-11-22 10:35:00 2019-11-28 19-058671
253 5 10000 2019-12-22 03:15:00 2019-12-28 19-063500
254 1 10000 2018-12-23 16:45:00 2018-12-28 18-060518
255 6 10000 2018-12-23 12:32:00 2018-12-28 18-060494
256 5 10000 2018-12-23 04:00:00 2018-12-28 18-060452
257 6 10000 2018-09-23 20:38:00 2018-09-28 18-045308
258 5 10000 2018-09-23 12:23:00 2018-09-28 18-045238
259 6 10000 2018-09-23 11:47:00 2018-09-28 18-045235
260 5 10000 2018-09-23 07:22:00 2018-09-28 18-045200
261 6 10000 2018-08-23 10:15:00 2018-08-28 18-039664
262 3 10000 2018-07-23 17:30:00 2018-07-28 18-033958
263 6 10000 2018-07-23 03:01:00 2018-07-28 18-033795
264 4 10000 2018-07-23 00:40:00 2018-07-28 18-033782
265 5 10000 2018-03-23 20:10:00 2018-03-28 18-012955
266 6 10000 2018-01-23 22:40:00 2018-01-28 18-003685
267 4 10000 2018-01-23 13:29:00 2018-01-28 18-003611
268 4 10000 2019-01-23 07:48:00 2019-01-28 19-003372
269 1 10000 2019-01-23 21:12:00 2019-01-28 19-003512
270 1 10000 2019-01-23 22:56:00 2019-01-28 19-003515
271 1 10000 2019-04-23 13:00:00 2019-04-28 19-018137
272 1 10000 2019-06-23 00:20:00 2019-06-28 19-029522
273 4 10000 2019-06-23 15:00:00 2019-06-28 19-029774
274 2 10000 2019-07-23 21:00:00 2019-07-28 19-035553
275 6 10000 2019-07-23 21:55:00 2019-07-28 19-035400
276 5 10000 2019-08-23 20:06:00 2019-08-28 19-041462
277 5 10000 2019-09-23 19:40:00 2019-09-28 19-047675
278 6 10000 2019-09-23 20:45:00 2019-09-28 19-047678
279 5 10000 2019-09-23 21:49:00 2019-09-28 19-047681
280 6 10000 2019-10-23 20:30:00 2019-10-28 19-053304
281 6 10000 2018-12-24 12:22:00 2018-12-28 18-060607
282 5 10000 2018-09-24 04:00:00 2018-09-28 18-045348
283 4 10000 2018-04-24 03:14:00 2018-04-28 18-018038
284 1 10000 2019-03-24 21:15:00 2019-03-28 19-012978
285 6 10000 2019-04-24 03:08:00 2019-04-28 19-018259
286 6 10000 2019-05-24 21:47:00 2019-05-28 19-024031
287 6 10000 2019-06-24 12:00:00 2019-06-28 19-030268
288 6 10000 2019-06-24 22:58:00 2019-06-28 19-029879
289 6 10000 2019-08-24 14:25:00 2019-08-28 19-041580
290 4 10000 2019-08-24 14:46:00 2019-08-28 19-041611
291 1 10000 2019-11-24 23:23:00 2019-11-28 19-059088
292 4 10000 2018-09-25 22:03:00 2018-09-28 18-045713
293 6 10000 2018-09-25 12:29:00 2018-09-28 18-045634
294 4 10000 2018-06-25 21:36:00 2018-06-28 18-029092
295 4 10000 2018-06-25 17:15:00 2018-06-28 18-029066
296 6 10000 2018-06-25 10:39:00 2018-06-28 18-029029
297 4 10000 2018-05-25 01:15:00 2018-05-28 18-023389
298 6 10000 2018-01-25 10:00:00 2018-01-28 18-004168
299 6 10000 2019-01-25 22:07:00 2019-01-28 19-003800
300 1 10000 2019-03-25 23:32:00 2019-03-28 19-013127
301 3 10000 2019-04-25 05:00:00 2019-04-28 19-018478
302 0 10000 2019-05-25 09:20:00 2019-05-28 19-024111
303 5 10000 2019-06-25 03:13:00 2019-06-28 19-029897
304 1 10000 2019-08-25 05:56:00 2019-08-28 19-041690
305 3 10000 2019-11-25 18:38:00 2019-11-28 19-059224
306 3 10000 2018-12-26 21:56:00 2018-12-28 18-060906
307 6 10000 2018-09-26 19:35:00 2018-09-28 18-045926
308 6 10000 2018-09-26 10:30:00 2018-09-28 18-045789
309 6 10000 2018-04-26 11:45:00 2018-04-28 18-018445
310 6 10000 2018-02-26 00:25:00 2018-02-28 18-008902
311 2 10000 2019-09-26 19:02:00 2019-09-28 19-048273
312 6 10000 2019-11-26 10:32:00 2019-11-28 19-059309
313 2 10000 2018-11-27 22:00:00 2018-11-28 18-056435
314 3 10000 2018-07-27 22:36:00 2018-07-28 18-034695
315 2 10000 2018-04-27 23:00:00 2018-04-28 18-018711
316 5 10000 2018-02-27 16:27:00 2018-02-28 18-009211
317 3 10000 2018-01-27 14:12:00 2018-01-28 18-004367
318 2 10000 2018-01-27 13:00:00 2018-01-28 18-004387
319 5 10000 2018-01-27 01:22:00 2018-01-28 18-004265
320 6 10000 2019-01-27 15:01:00 2019-01-28 19-004046
321 1 10000 2019-01-27 21:00:00 2019-01-28 19-004099
322 4 10000 2019-04-27 05:17:00 2019-04-28 19-018891
323 4 10000 2019-05-27 01:08:00 2019-05-28 19-024375
324 6 10000 2019-05-27 23:15:00 2019-05-28 19-024517
325 6 10000 2019-09-27 18:50:00 2019-09-28 19-048489
326 6 10000 2019-11-27 16:00:00 2019-11-28 19-059586
327 6 10000 2018-12-28 01:15:00 2018-12-28 18-061082
328 5 10000 2018-09-28 11:30:00 2018-09-28 18-046177
329 5 10000 2018-07-28 22:15:00 2018-07-28 18-034867
330 5 10000 2018-04-28 03:20:00 2018-04-28 18-018735
331 6 10000 2019-01-28 12:40:00 2019-01-28 19-004172
332 1 10000 2019-09-28 16:20:00 2019-09-28 19-048627
333 1 10000 2019-10-28 14:51:00 2019-10-28 19-054188
334 5 10000 2019-11-28 00:25:00 2019-11-28 19-059621
335 6 10000 2019-11-28 13:09:00 2019-11-28 19-059669
336 5 10000 2018-11-29 20:49:00 2018-11-28 18-056624
337 4 10000 2018-07-29 00:56:00 2018-07-28 18-034886
338 3 10000 2018-05-29 00:11:00 2018-05-28 18-023978
339 4 10000 2018-04-29 20:00:00 2018-04-28 18-018976
340 5 10000 2018-04-29 17:00:00 2018-04-28 18-018954
341 2 10000 2018-01-29 00:55:00 2018-01-28 18-004573
342 6 10000 2019-04-29 07:05:00 2019-04-28 19-019206
343 5 10000 2019-04-29 16:10:00 2019-04-28 19-019314
344 6 10000 2019-05-29 06:10:00 2019-05-28 19-024763
345 6 10000 2019-05-29 12:00:00 2019-05-28 19-025192
346 4 10000 2019-05-29 12:17:00 2019-05-28 19-024821
347 1 10000 2019-06-29 21:00:00 2019-06-28 19-030913
348 3 10000 2019-08-29 11:58:00 2019-08-28 19-042610
349 2 10000 2018-10-29 07:30:00 2018-10-28 18-051495
350 6 10000 2018-10-29 10:40:00 2018-10-28 18-051536
351 6 10000 2018-10-29 15:48:00 2018-10-28 18-051586
352 1 10000 2018-09-30 20:27:00 2018-09-28 18-046560
353 5 10000 2018-09-30 15:33:00 2018-09-28 18-046536
354 3 10000 2018-09-30 12:09:00 2018-09-28 18-046508
355 5 10000 2018-07-30 20:48:00 2018-07-28 18-035236
356 6 10000 2018-07-30 05:20:00 2018-07-28 18-035065
357 6 10000 2018-06-30 18:44:00 2018-06-28 18-029981
358 6 10000 2018-01-30 10:40:00 2018-01-28 18-004812
359 6 10000 2019-04-30 23:45:00 2019-04-28 19-019547
360 5 10000 2019-05-30 12:47:00 2019-05-28 19-025009
361 5 10000 2019-05-30 20:56:00 2019-05-28 19-025076
362 5 10000 2019-05-30 21:58:00 2019-05-28 19-025102
363 3 10000 2019-07-30 00:01:00 2019-07-28 19-036579
364 6 10000 2019-12-30 18:10:00 2019-12-28 19-064753
365 3 10000 2018-05-31 01:30:00 2018-05-28 18-024433
366 6 10000 2018-01-31 11:15:00 2018-01-28 18-004969
367 6 10000 2019-05-31 12:59:00 2019-05-28 19-025205
368 5 10000 2019-08-31 04:34:00 2019-08-28 19-042948
369 5 10000 2018-10-31 16:23:00 2018-10-28 18-051996
OGR data source with driver: ESRI Shapefile
Source: "E:\NEW-stl-louis-crime\St Louis Shape files\nbrhds_wards\BND_Nhd88_cw.shp", layer: "BND_Nhd88_cw"
with 88 features
It has 6 fields
Integer64 fields read as strings: NHD_NUM
Collected US Census data to bring in geospatial polygons that represent St Louis Neighborhoods.
Transformed mapview data into WGS84 structure.
Check to make sure data is a geospatial object.
Use census geospatial data to generate a map.
Observations: 88
Variables: 6
$ NHD_NUM <fct> 43, 29, 28, 40, 41, 42, 39, 44, 36, 37, 62, 61, 45, 77, ...
$ NHD_NAME <fct> Franz Park, Tiffany, Botanical Heights, Kings Oak, Chelt...
$ ANGLE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ NHD_NUMTXT <fct> 43 Franz Park, 29 Tiffany, 28 Botanical Heights, 40 King...
$ SHAPE_area <dbl> 11012014, 5887342, 11586012, 4706723, 9245751, 9771242, ...
$ SHAPE_len <dbl> 14740.430, 10467.847, 14700.023, 9239.956, 12357.106, 12...
We have 88 neighborhoods and their name and number are factor types in R.
The polygon shapes are included in this data frame.
# A tibble: 24 x 3
# Groups: CodedMonth [24]
CodedMonth Crime n
<date> <int> <int>
1 2018-07-28 10000 23
2 2019-06-28 10000 23
3 2018-09-28 10000 21
4 2019-05-28 10000 21
5 2019-07-28 10000 21
6 2018-01-28 10000 19
7 2018-10-28 10000 19
8 2018-12-28 10000 19
9 2019-01-28 10000 18
10 2019-09-28 10000 18
# ... with 14 more rows
Group data by coded month.
Count the number of homicides per month.
Data presented in a bar graph with totals displayed above the bar.
I added a smoothing line to get a better view of the crime movement.
Note that October 2018 was the peak.
It was when Channel 5 reported the sever increase in carjackings. Looks like homicids too.
It was also the timeframe when they reported establishing atask force.
# A tibble: 62 x 6
NHD_NAME Crime n cumulative total cumul.percent
<fct> <int> <int> <int> <int> <dbl>
1 Wells Goodfellow 10000 27 27 369 7.32
2 Baden 10000 22 49 369 13.3
3 Greater Ville 10000 20 69 369 18.7
4 Dutchtown 10000 18 87 369 23.6
5 Penrose 10000 16 103 369 27.9
6 Jeff Vanderlou 10000 15 118 369 32.0
7 Walnut Park East 10000 15 133 369 36.0
8 Hamilton Heights 10000 13 146 369 39.6
9 Carondelet 10000 11 157 369 42.5
10 Walnut Park West 10000 11 168 369 45.5
# ... with 52 more rows
Had to adjust the factor variables (NHD_NAME) and to account for missing variables (NA).
Count by crime and put in decending order.
This is a display of the highest crime neighborhoods.
70% of the homicides are committed in the top 21 neighborhoods (23%)
Group by Neighborhood Name.
Chart puts data in a descending order and presents greater than 5.
Create and mutate an hour of day field using lubridate.
This adds a new field to crimeA data frame to categorize a day into 6 hour blocks.
Used a logic functions to segment day categories
Reporting.diff YCoord XCoord CADStreet
Length:369 Min. : 0 Min. : 0 Length:369
Class :difftime 1st Qu.:1010480 1st Qu.:888200 Class :character
Mode :numeric Median :1028747 Median :893435 Mode :character
Mean : 983939 Mean :857475
3rd Qu.:1035119 3rd Qu.:897454
Max. :1068605 Max. :910845
CADAddress LocationComment LocationName Neighborhood
Min. : 0 Length:369 Length:369 Length:369
1st Qu.:2804 Class :character Class :character Class :character
Median :4225 Mode :character Mode :character Mode :character
Mean :3868
3rd Qu.:5042
Max. :8612
NA's :115
ILEADSStreet ILEADSAddress Description District
Length:369 Min. : 0 Length:369 Min. :0.000
Class :character 1st Qu.:2157 Class :character 1st Qu.:3.000
Mode :character Median :3942 Mode :character Median :5.000
Mean :3665 Mean :4.366
3rd Qu.:5000 3rd Qu.:6.000
Max. :9006 Max. :6.000
Crime DateOccur CodedMonth
Min. :10000 Min. :2016-06-09 08:28:00 Min. :2018-01-28
1st Qu.:10000 1st Qu.:2018-07-05 21:26:00 1st Qu.:2018-07-28
Median :10000 Median :2018-12-26 21:56:00 Median :2018-12-28
Mean :10000 Mean :2018-12-29 15:53:31 Mean :2019-01-17
3rd Qu.:10000 3rd Qu.:2019-06-27 01:48:00 3rd Qu.:2019-07-28
Max. :10000 Max. :2019-12-30 18:10:00 Max. :2019-12-28
Complaint NHD_NAME
Length:369 Wells Goodfellow: 27
Class :character Baden : 22
Mode :character Greater Ville : 20
Dutchtown : 18
Penrose : 16
(Other) :263
NA's : 3
We will use the data we restructed earlier in the analysis.
We will use the crime D file.
Check the structure of the file we selected.
XCoord and YCoord coordinates are based on the State Plane North American Datum 1983 (NAD83) format.
This data will have to be converted to lat/long values.
Some of the XCoords and YCoords have values of O. This will need to be accounted for later in the analysis.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 749 days 0 0 SHREVE 4257
2 483 days 0 0 NA
3 291 days 0 0 CHURCH 7943
4 286 days 0 0 MAFFITT 5322
5 184 days 0 0 LABADIE 4446
6 179 days 0 0 EUCLID 1202
7 124 days 0 0 TEXAS 3709
8 31 days 0 0 SAINT LOUIS 4753
9 26 days 0 0 NA
10 22 days 0 0 NA
11 14 days 0 0 TENNESSEE 5226
12 13 days 0 0 4949
13 13 days 0 0 NA
14 6 days 0 0 NA
15 3 days 0 0 NA
LocationComment LocationName Neighborhood
1 SAMS ST. LOUIS PACKING CO 69
2 62
3 74
4 50
5 56
6 53
7 19
8 56
9 39
10 67
11 VICTIM S RESPONDED TO THE 5200 BLOCK 0
12 0
13 72
14 FAIRGROUNDS PARK FAIRGROUNDS PARK 83
15 0
ILEADSStreet ILEADSAddress Description District Crime
1 SHREVE 4257 HOMICIDE 6 10000
2 9TH 1418 HOMICIDE 4 10000
3 CHURCH 7944 HOMICIDE 6 10000
4 5322 HOMICIDE 5 10000
5 LABADIE 4446 HOMICIDE 6 10000
6 EUCLID 1202 HOMICIDE 5 10000
7 TEXAS 3709 HOMICIDE 3 10000
8 SAINT LOUIS 4751 HOMICIDE 5 10000
9 I 64 WESTBOUND AT CLAYTON AVE 0 HOMICIDE 2 10000
10 FAIRGROUNDS PL 4100 HOMICIDE 6 10000
11 UNKNOWN 0 HOMICIDE 0 10000
12 UNKNOWN 0 HOMICIDE 0 10000
13 W FLORISSANT AVE 5500 HOMICIDE 6 10000
14 FAIRGROUNDS PARK DR 3900 HOMICIDE 6 10000
15 UNKNOWN 0 HOMICIDE 0 10000
DateOccur CodedMonth Complaint NHD_NAME
1 2016-06-09 08:28:00 2018-06-28 16-027833 Penrose
2 2017-09-01 13:44:00 2018-12-28 17-043306 Columbus Square
3 2018-06-10 00:01:00 2019-03-28 18-026258 Baden
4 2019-03-17 13:03:00 2019-12-28 19-011818 Wells Goodfellow
5 2018-05-28 00:01:00 2018-11-28 18-023972 Greater Ville
6 2018-05-02 17:05:00 2018-10-28 18-019567 Fountain Park
7 2018-07-27 20:15:00 2018-11-28 18-034672 Gravois Park
8 2018-03-28 18:15:00 2018-04-28 18-013752 Greater Ville
9 2018-06-02 14:37:00 2018-06-28 18-024858 Forest Park South East
10 2018-05-06 13:22:00 2018-05-28 18-020208 Fairground Neighborhood
11 2019-09-14 03:50:00 2019-09-28 19-045679 <NA>
12 2019-01-15 13:24:00 2019-01-28 19-002283 <NA>
13 2019-06-15 09:37:00 2019-06-28 19-028025 Walnut Park East
14 2019-05-22 18:16:00 2019-05-28 19-023607 Fairground Park
15 2019-05-25 09:20:00 2019-05-28 19-024111 <NA>
Collect those records whose X/Y values are zeros.
These records will need a different type of processing.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 371 days 997824.9 890012.2 NA
2 42 days 996991.1 894592.8 ITASKA 3111
3 31 days 992687.3 890685.3 NA
4 29 days 1028778.0 888118.1 NA
5 27 days 1034026.0 898534.9 GREEN LEA 4136
6 27 days 1023634.0 907592.0 14TH 1908
7 27 days 1030898.0 901867.4 NA
8 27 days 1048605.0 896231.8 GIMBLIN 1020
9 27 days 1028286.0 893354.1 ALDINE 4349
10 27 days 1034113.0 885395.3 NA
11 27 days 1021943.0 899509.6 DELMAR 3114
12 27 days 1027916.0 905727.4 KNAPP 3245
13 27 days 1028080.0 894410.9 COTE BRILLIANTE 4229
14 27 days 1000447.0 877220.9 KINSEY 6272
15 27 days 1036161.0 882481.3 KENNERLY 6101
16 27 days 1033304.0 887147.7 NA
17 27 days 1029233.0 890945.9 NA
18 27 days 1034806.0 882698.1 NA
19 27 days 1029923.0 880264.4 HAMILTON 1021
20 27 days 1019440.0 905891.9 15TH 710
21 26 days 1032130.0 896900.4 NATURAL BRIDGE 4231
22 26 days 998275.6 894535.1 MINNESOTA 4457
23 26 days 1047905.0 897340.6 NA
24 26 days 1004111.0 893520.9 GRAND 3630
25 26 days 1007221.0 884519.4 HEREFORD 3322
26 26 days 1003295.0 895121.2 NA
27 26 days 1030650.0 887444.4 DR MARTIN LUTHER KING 5100
28 26 days 995444.5 892927.1 VIRGINIA 5301
29 26 days 1032146.0 883637.7 NA
30 26 days 1008027.0 901258.3 CONGRESS 1909
31 26 days 1032246.0 900444.8 LEE 3856
32 26 days 1028281.0 894047.3 COTE BRILLIANTE 4200
33 25 days 1034757.0 904588.8 BROADWAY 4828
34 25 days 1028137.0 893625.3 BILLUPS 1705
35 25 days 1053153.0 898033.6 NA
36 25 days 1031750.0 883103.6 MINERVA 5634
37 25 days 998801.4 894874.6 MICHIGAN 4414
38 25 days 1027393.0 904794.8 RAUSCHENBACH 3117
39 25 days 1039733.0 885195.7 MCARTHUR 5910
40 25 days 1025430.0 897494.3 CASS 3731
41 24 days 1051995.0 895308.4 RIVERVIEW 1124
42 24 days 1005529.0 890871.6 POTOMAC 3954
43 24 days 1000978.0 886650.3 WALLACE 4341
44 24 days 1034920.0 886010.0 BURD 2728
45 24 days 1033370.0 887518.1 NA
46 24 days 1028707.0 884870.6 NA
47 24 days 1031967.0 895056.4 NA
48 24 days 1022360.0 900798.4 NA
49 24 days 1046675.0 889809.7 MIMIKA 5531
50 24 days 1010480.0 895630.6 LOUISIANA 2328
51 23 days 1007382.0 893365.3 CONNECTICUT 3634
52 23 days 1036961.0 883830.0 SAINT LOUIS 5920
53 23 days 1042272.0 893522.1 NA
54 23 days 1014127.0 903976.8 DILLON 1124
55 23 days 1032130.0 896900.4 NATURAL BRIDGE 4231
56 23 days 1036605.0 897450.3 CARRIE 4531
57 23 days 1022548.0 907625.4 NA
58 23 days 1038155.0 895308.8 FLORISSANT 4700
59 23 days 1003278.0 895156.4 COMPTON 3720
60 23 days 1037063.0 886918.4 PALM 5550
61 23 days 1022299.0 906762.9 COCHRAN 1461
62 23 days 1026766.0 897107.0 GARFIELD 4000
63 23 days 1043213.0 892050.1 THRUSH 5400
64 23 days 1000926.0 892725.6 MERAMEC 3711
65 22 days 1047342.0 889848.5 FLOY 5594
66 22 days 1044996.0 892586.6 NA
67 22 days 988016.4 886631.2 DAVIS 547
68 22 days 1046716.0 896542.6 SWITZER 951
69 22 days 1029418.0 891030.9 4949
70 22 days 1002487.0 898351.1 NA
71 22 days 1030139.0 904277.8 NA
72 22 days 1034151.0 902558.0 NA
73 22 days 1048015.0 896324.3 FREDERICK 8216
74 22 days 1021607.0 898042.0 NA
75 22 days 1034699.0 884938.9 HIGHLAND 5627
76 22 days 992407.4 890121.1 IDAHO 6602
77 22 days 1031273.0 885087.4 SEMPLE 1400
78 21 days 1052128.0 895835.8 NA
79 21 days 1044850.0 892558.1 FLORISSANT 5728
80 21 days 1048122.0 897219.8 BROADWAY 8220
81 21 days 1004800.0 894538.1 NA
82 21 days 1035792.0 892505.1 NA
83 21 days 1042225.0 890033.2 PLOVER 4936
84 21 days 1034573.0 896427.9 NEWSTEAD 4140
85 21 days 1026560.0 887259.6 ENRIGHT 5048
86 20 days 1031415.0 894194.6 NEWSTEAD 2931
87 20 days 1028621.0 898253.1 NA
88 20 days 997279.0 894676.9 MICHIGAN 4626
89 20 days 1031778.0 890118.8 HIGHLAND 4900
90 20 days 1002564.0 891249.9 NA
91 20 days 1030531.0 898549.0 LEXINGTON 3900
92 20 days 1035777.0 893192.8 NA
93 20 days 1032928.0 897204.1 NA
94 20 days 1005006.0 892615.9 GILES 3521
95 20 days 1034773.0 881698.6 NA
96 20 days 1031673.0 884752.1 BELT 1439
97 20 days 1032225.0 882995.8 BLACKSTONE 1387
98 19 days 1000824.0 894103.1 LOUISIANA 4114
99 19 days 1030386.0 901979.7 PECK 4012
100 19 days 1025417.0 892611.9 PENDLETON 926
101 19 days 1013903.0 904245.2 NA
102 19 days 1025707.0 896608.4 NA
103 19 days 1044225.0 891667.2 ROBIN 5434
104 19 days 1038048.0 900410.6 GRAND 1325
105 19 days 1026655.0 891146.6 NA
106 19 days 1001651.0 899383.5 WISCONSIN 3853
107 19 days 1030024.0 900011.5 NATURAL BRIDGE 3836
108 19 days 997345.6 894541.8 MICHIGAN 4600
109 19 days 1044387.0 887882.4 NA
110 19 days 1022813.0 905078.3 MURPHY PARK 1851
111 19 days 1026907.0 899641.4 NA
112 19 days 1028866.0 877725.8 NA
113 19 days 1028459.0 902545.9 PALM 2522
114 19 days 1001554.0 895565.8 NA
115 18 days 1030515.0 885139.5 MINERVA 5363
116 18 days 998619.1 894877.5 MICHIGAN 4340
117 18 days 1000689.0 896718.0 NA
118 18 days 1000348.0 894739.9 MERAMEC 3147
119 18 days 1002881.0 895726.6 NA
120 17 days 995729.4 891902.7 IDAHO 5417
121 17 days 990205.4 888646.9 GRAND 1325
122 17 days 1028346.0 893245.7 ALDINE 4349
123 17 days 1030457.0 898946.1 PALM 3921
124 17 days 1021285.0 887849.5 PARKVIEW 4921
125 17 days 1027106.0 887992.3 KINGSHIGHWAY 900
126 17 days 1030385.0 891428.5 MARKET 4639
127 17 days 1026678.0 898056.9 NA
128 17 days 1005252.0 894945.9 VIRGINIA 3429
129 17 days 1027150.0 889813.4 MCMILLAN 4700
130 17 days 1032050.0 894045.3 LABADIE 4446
131 17 days 1028243.0 904858.9 NA
132 17 days 1044511.0 887701.6 GOODFELLOW 5003
133 17 days 1000773.0 894063.4 NA
134 16 days 1044511.0 887701.6 NA
135 16 days 1033378.0 891132.4 NA
136 16 days 1034206.0 903869.1 JOHN 1449
137 16 days 1048634.0 890708.3 NORTH POINTE 6145
138 16 days 1004299.0 891279.4 NA
139 16 days 1000305.0 887120.0 DELOR 5254
140 16 days 1025296.0 893849.6 C D BANKS 4155
141 16 days 1029258.0 906530.4 11TH 3505
142 16 days 1010280.0 895225.3 TENNESSEE 2602
143 16 days 994702.8 892471.4 NA
144 15 days 1035297.0 895854.2 PENROSE 4481
145 15 days 1033212.0 884433.3 THEODOSIA 5601
146 15 days 1035636.0 893450.8 SHREVE 4049
147 15 days 1027261.0 896310.5 GARFIELD 4000
148 15 days 1021049.0 900148.2 SAMUEL SHEPARD 2946
149 15 days 1017651.0 909633.8 NA
150 14 days 1038855.0 892836.5 NA
151 14 days 1036218.0 884340.4 ROOSEVELT 5816
152 14 days 1002831.0 896061.4 CHIPPEWA 3116
153 14 days 1042247.0 890065.3 PLOVER 4938
154 13 days 1035671.0 897440.9 CLARENCE 4401
155 13 days 1029887.0 889834.6 DR MARTIN LUTHER KING 4821
156 13 days 1035834.0 884252.7 NA
157 13 days 999847.4 885892.1 NA
158 13 days 1036000.0 888884.8 UNION 3431
159 13 days 1037099.0 895579.4 RICHARD 4625
160 13 days 998998.5 896676.4 NA
161 13 days 1046874.0 889008.8 GOODFELLOW 5517
162 13 days 1027291.0 882262.8 ENRIGHT 5616
163 12 days 1031341.0 891609.6 MARCUS 2613
164 12 days 1044484.0 888564.8 NA
165 12 days 1030127.0 886695.8 RIDGE 5138
166 12 days 1017204.0 908242.7 NA
167 12 days 1032972.0 903934.3 19TH 4406
168 12 days 1029537.0 893621.6 NA
169 12 days 1025357.0 907701.4 NA
170 12 days 1033897.0 889429.4 ST LOUIS 5109
171 12 days 1022022.0 907595.8 13TH 1430
172 11 days 1017799.0 907619.9 9TH 205
173 11 days 1020329.0 899238.6 OLIVE 3037
174 11 days 1031357.0 905076.9 NA
175 11 days 997764.3 895610.9 NEBRASKA 4529
176 11 days 1026735.0 891120.7 NA
177 11 days 1007405.0 894425.6 NA
178 11 days 1035772.0 882305.7 WABADA 5969
179 11 days 1026184.0 890301.9 ENRIGHT 4550
180 11 days 1051786.0 894677.4 HOWELL 1181
181 11 days 1027321.0 888019.9 NA
182 11 days 1051778.0 894566.9 NA
183 11 days 1032520.0 880346.8 NA
184 11 days 1051838.0 895354.6 HOWELL 1115
185 10 days 1028775.0 900428.8 NA
186 10 days 1027075.0 896370.4 GARFIELD 4000
187 10 days 1022154.0 898673.6 NA
188 10 days 1004925.0 897760.9 CHEROKEE 2720
189 10 days 1026933.0 888149.1 AUBERT 773
190 10 days 1027279.0 891495.9 NA
191 10 days 1022136.0 905133.4 HOGAN 1800
192 10 days 998003.9 893209.4 VIRGINIA 4518
193 10 days 1030180.0 888420.4 KINGSHIGHWAY 1408
194 9 days 1010572.0 903703.8 ALLEN 1051
195 9 days 1025124.0 905021.8 BENTON 1933
196 9 days 1045249.0 887714.6 LALITE 6336
197 9 days 1037168.0 894947.6 NA
198 9 days 990701.9 888591.6 NA
199 9 days 1028552.0 881199.8 GOODFELLOW 853
200 9 days 993755.2 878508.2 GRAVOIS 7422
201 9 days 1046545.0 891148.8 NA
202 9 days 1068605.0 910845.4 EB 270 NA
203 9 days 1017616.0 876377.6 PARK 6763
204 9 days 1028716.0 890091.6 PAGE 4711
205 9 days 1013953.0 904086.3 NA
206 9 days 1031748.0 885691.9 DR MARTIN LUTHER KING 5378
207 9 days 1030986.0 885172.3 NA
208 9 days 1022057.0 901215.1 STODDARD 2800
209 8 days 1022134.0 909573.1 NA
210 8 days 1006156.0 896621.1 PENNSYLVANIA 3244
211 8 days 1030986.0 885172.3 NA
212 8 days 1040130.0 894837.9 NA
213 8 days 1040705.0 891528.7 ARLINGTON 4941
214 8 days 1036066.0 893029.3 KOSSUTH 4863
215 8 days 1024730.0 907940.6 NA
216 7 days 1018977.0 905530.4 NA
217 7 days 1030451.0 896704.6 WHITTIER 3047
218 7 days 1018061.0 895739.8 SCOTT 3560
219 7 days 1040680.0 890932.3 EMERSON 4921
220 7 days 1032299.0 886623.5 PATTON 5331
221 7 days 1034664.0 896014.3 LEE 4438
222 7 days 1033549.0 887857.3 UNION 2700
223 7 days 1026696.0 899404.5 MONTGOMERY 3461
224 7 days 1035119.0 883649.6 HIGHLAND 5807
225 7 days 1026065.0 881536.7 KINGSBURY 5708
226 7 days 1022984.0 900603.5 JAMES COOL PAPA BELL 2900
227 6 days 1032339.0 900019.9 SHERMAN 3927
228 6 days 1027313.0 897869.5 COTTAGE 3834
229 6 days 1034257.0 901754.4 NA
230 6 days 1044313.0 891175.7 GILMORE 5276
231 6 days 1002248.0 887263.9 MORGANFORD 4522
232 6 days 1007882.0 895751.5 HARTFORD 3230
233 6 days 1033378.0 891132.4 EUCLID 2944
234 6 days 1031231.0 903749.4 PENROSE 2106
235 6 days 1032953.0 887081.5 WABADA 5330
236 6 days 1032241.0 900648.8 LEE 3833
237 6 days 1035883.0 883594.1 MAFFITT 5800
238 6 days 1028851.0 891679.1 DR MARTIN LUTHER KING 4582
239 6 days 1033520.0 889914.2 ST LOUIS 5025
240 5 days 992870.4 892982.7 PENNSYLVANIA 5913
241 5 days 1032898.0 894410.2 NA
242 5 days 1028603.0 888564.7 AUBERT 1200
243 5 days 1051408.0 896489.1 CANAAN 907
244 5 days 1030932.0 893627.2 NA
245 5 days 1032937.0 892505.8 ASHLAND 4710
246 5 days 1026577.0 894318.1 EVANS 4200
247 5 days 1037449.0 894152.3 NA
248 5 days 1002881.0 895726.6 NA
249 5 days 1029269.0 898082.8 LABADIE 3945
250 5 days 1026084.0 899342.4 NA
251 5 days 1028563.0 898133.8 VANDEVENTER 2816
252 5 days 1043141.0 890387.4 WREN 5015
253 5 days 1017271.0 903906.1 NA
254 5 days 1022183.0 899505.3 NA
255 5 days 989533.2 887239.5 SCHIRMER 800
256 5 days 1002406.0 891194.5 ALBERTA 3921
257 5 days 989042.8 889513.5 PENNSYLVANIA 7403
258 5 days 997804.3 891252.8 DELOR 3659
259 5 days 1033277.0 904660.4 BISSELL 1121
260 5 days 1011331.0 881563.1 DALTON 0
261 5 days 1049783.0 891947.9 MORA 8561
262 5 days 1028461.0 885269.8 NA
263 5 days 1031326.0 890200.3 HAMMETT 4851
264 5 days 1049854.0 895810.5 HALLS FERRY 8612
265 5 days 1033725.0 882188.8 NA
266 5 days 1046626.0 890521.4 SHULTE 6035
267 4 days 1033658.0 899795.6 NA
268 4 days 1027078.0 888592.4 EUCLID 785
269 4 days 1031732.0 902337.5 GRAND 4206
270 4 days 990479.7 888796.9 NA
271 4 days 1051754.0 896324.3 ELIAS 933
272 4 days 1047754.0 889373.8 NA
273 4 days 1048493.0 896371.1 CHURCH 8309
274 4 days 1033060.0 891237.8 LABADIE 4843
275 4 days 1043363.0 890705.5 WREN 5055
276 4 days 1020209.0 903370.6 21ST 715
277 4 days 996624.4 891625.1 LOUISIANA 5211
278 3 days 1024271.0 906633.1 NA
279 3 days 1041538.0 892153.7 ALCOTT 5200
280 3 days 1027247.0 903401.7 DODIER NA
281 3 days 1022198.0 907641.5 14TH 1430
282 3 days 1035428.0 893986.3 4949
283 3 days 1024089.0 908269.7 NA
284 3 days 1033423.0 897476.8 KOSSUTH 4235
285 3 days 1034813.0 903047.6 DE SOTO 1409
286 3 days 995171.7 893913.2 WALSH 308
287 3 days 1002376.0 893358.7 NA
288 3 days 1031432.0 891251.6 NA
289 3 days 990000.4 888462.2 NA
290 3 days 1003994.0 894935.3 VIRGINIA 3620
291 2 days 1015110.0 900117.9 RUTGER 2654
292 2 days 1051640.0 897259.3 ELIAS 835
293 2 days 1051479.0 895927.5 CANAAN 971
294 2 days 1040904.0 886178.6 NA
295 2 days 1035696.0 893689.0 KOSSUTH 4727
296 2 days 1016725.0 890017.6 NORFOLK 4250
297 2 days 1030720.0 896366.2 ASHLAND 4279
298 1 days 1010894.0 893626.0 SHENANDOAH 3658
299 1 days 1004511.0 898021.4 OHIO 3452
300 1 days 1004375.0 884540.4 CHIPPEWA 4939
301 1 days 1027553.0 893493.7 DR MARTIN LUTHER KING 4308
302 1 days 1001617.0 895896.4 PENNSYLVANIA 3942
303 1 days 1004029.0 888199.9 MORGANFORD 4254
304 1 days 1037577.0 885193.6 SELBER 5830
305 1 days 1043688.0 891512.5 WREN 5270
306 1 days 1003043.0 890577.3 DUNNICA 3946
307 1 days 1023898.0 906643.6 NA
308 1 days 1019893.0 910547.7 2ND 999
309 1 days 1049007.0 890159.1 GOODFELLOW 5961
310 1 days 1034124.0 895794.9 FARLIN 4447
311 1 days 1052972.0 896665.4 RIVERVIEW 911
312 0 days 1040599.0 893346.5 GERALDINE 5304
313 0 days 1035764.0 882107.0 WABADA 5962
314 0 days 1033922.0 888254.7 TERRY 5252
315 0 days 1036786.0 886544.7 CLARA 3340
316 0 days 1042473.0 892352.3 DAVISON 5271
317 0 days 995260.5 893647.8 EICHELBERGER 411
318 0 days 995670.8 891021.7 NA
319 0 days 1031520.0 885578.9 ARLINGTON 1460
320 0 days 1038456.0 900558.8 NA
321 -1 days 1031068.0 885126.1 ARLINGTON 1401
322 -1 days 1028747.0 898586.3 LABADIE 3800
323 -1 days 1010081.0 903858.6 NA
324 -1 days 1016086.0 908223.8 CLARK 601
325 -1 days 1032564.0 881482.7 NA
326 -1 days 1017361.0 890066.3 MANCHESTER 4238
327 -1 days 1035157.0 902483.6 CONDE 5220
328 -1 days 1036030.0 882441.9 HIGHLAND 5900
329 -1 days 1043154.0 890135.3 NA
330 -1 days 1036149.0 891990.5 FARLIN 4950
331 -1 days 1022299.0 906762.9 CASS 1415
332 -1 days 989055.9 889596.9 UPTON 225
333 -1 days 1000754.0 895093.4 MICHIGAN 4100
334 -1 days 1008747.0 887198.5 LACKLAND 3139
335 -1 days 1048551.0 893434.8 NA
336 -1 days 1047618.0 897453.9 BITTNER 732
337 -2 days 997429.1 891964.9 TENNESSEE 4758
338 -2 days 1037384.0 885274.5 GOODFELLOW 5713
339 -2 days 1003002.0 895591.7 NA
340 -2 days 1029828.0 885989.0 MINERVA 5232
341 -2 days 1035240.0 898489.6 ATHLONE 4430
342 -2 days 1051865.0 896562.4 HARLAN 917
343 -2 days 1041712.0 893530.7 CLAXTON 5357
344 -2 days 1039648.0 885117.3 FERRIS 5911
345 -2 days 1028291.0 891706.2 NA
346 -2 days 1028254.0 890506.0 PAGE 4634
347 -2 days 1035149.0 882181.7 NA
348 -2 days 1013424.0 904108.4 NA
349 -2 days 1035148.0 901348.9 NA
350 -3 days 1003486.0 896527.1 NEBRASKA 3646
351 -3 days 1032899.0 894125.9 LEXINGTON 4534
352 -3 days 1033526.0 899422.0 CLAY 4221
353 -3 days 1034569.0 886330.5 NA
354 -3 days 1028958.0 887970.3 MAPLE 5009
LocationComment LocationName
1 REAR
2
3
4 AUTO ZONE
5
6
7 DOLLAR GENERAL
8
9
10
11
12
13
14
15 ON STREET ON STREET
16
17 NBC LOUNGE
18
19
20
21 WEST PARKING LOT M&A LIQUOR STORE
22 MOUNT PLEASANT PARK
23
24 PAPA JOHNS PIZZA
25 APARTMENT BUILDING
26 STREET
27 THE OTHER PLACE LOUNGE
28
29
30
31
32 VACANT LOT
33 HOTEL- FIRST WESTERN INN
34
35 RESIDENCE RESIDENCE
36
37
38
39
40
41
42
43 ALLEY ADJACENT TO SIDE WINDOW
44 LONDON S BOARDING HOUSE
45
46 REAR ALLEY
47
48
49
50 1ST FLOOR
51
52
53
54 CLINTON-PEABODY HOUSING COMPLEX
55 M & A MARKET PARKING LOT
56 JUST INSIDE OF TREE LINE IN REAR OF
57
58 @AMEREN - 158
59
60
61
62
63
64 SECOND FLOOR
65
66
67
68
69
70
71
72 REAR YARD OF VACANT RESIDENCE
73
74
75
76
77
78
79
80
81 GRAVOIS PARK
82
83
84
85 REAR
86
87 MIDWEST PETROLEUM
88
89
90 SOUTH SIDE OF AMBERG PARK
91
92 APT A
93
94
95
96 STREET
97
98
99
100
101
102 CAR WASH AND AUTOMOBILE DETAILING BU BIG P. S CAR WASH AND DETAILING
103
104
105
106 APT B
107
108
109 ST. LOUIS FISH & CHICKEN
110 IN STREET MURPHY PARK APARTMENTS
111 UPPER LEVEL CLUB
112 VACANT LOT
113
114
115
116
117
118
119
120
121
122 STREET
123
124 14TH FLOOR
125
126
127
128
129
130
131
132
133
134 @BP GAS STATION PARKING LOT
135
136
137
138
139
140
141
142
143
144
145
146
147 VACANT LOT
148
149 MANSION HOUSE
150
151
152 IN STREET
153
154
155 BEST AUTO PLEX
156 GROCERY STORE PRICE CHOPPER
157 5401 MISSOURI ROUTE 30 LUCKY DUCK RESTAURANT
158 @SCHNUCKS - CITY PLAZA ON UNION
159 TWO-FAMILY APARTMENT BLDG
160 CONOCO GAS STATION
161 REAR ALLEY
162 ST. LUKE S PLAZA APARTMENTS
163 EAST SIDEWALK
164
165
166
167
168
169
170 NORTH ALLEY
171 CASS BANK
172
173 INTERIOR/SIDEWALK/PARKING LOT/STREET OLIVE BAR
174 WINDSOR PARK
175 STREET
176
177
178 VACANT RESIDENCE
179
180
181 CROWN FOOD MART
182
183 JULIAN AT HODIAMONT
184
185 BING LAU
186
187
188
189
190
191 REAR
192
193
194
195 APT A
196 REAR
197
198
199
200 APARTMENT COMPLEX GRAVOIS PLACE APARTMENTS
201 REAR ALLEY
202
203
204
205 ST. LOUIS HOUSING AUTHORITY CLINTON PEABODY PUBLIC HOUSING COMP
206
207
208 REAR
209
210
211
212
213
214
215
216
217
218
219
220
221 REAR GARAGE
222
223
224
225 WINTER GARDEN APARTMENTS
226
227
228
229
230
231 THE ELLENWOOD BUILDING
232 @ROOSEVELT HIGH SCHOOL
233
234 ALLEY
235
236
237
238
239
240
241
242
243
244 EAST SIDE OF STREET IN FRONT OF A VA
245
246
247
248
249 2ND FLOOR
250
251 MIDWEST PETROLEUM
252
253 STREET
254
255
256
257
258
259
260 DUPLEX ATTACHED TO 2641 DALTON
261
262 HAROLD S CHOP SUEY
263
264 APARTMENT RIVERVIEW APARTMENTS
265
266
267
268
269 @HOTEL- ECONOMY INN
270
271
272
273 ALLEY
274
275
276 APT 412
277
278 NUMERICAL ADDRESS OF THE PRIMARY SC
279 REAR ALLEY
280
281 @DOMINOS PIZZA - N 13TH
282
283
284 ALLEY/DUMPSTER
285 INTERSECTION OF E DE SOTO AVENUE AND
286 APT C
287
288
289 VACANT LOT
290
291 APARTMENT COMPLEX CAROLINE PLACE APARTMENTS
292
293
294
295 REAR ALLEY
296
297
298
299
300 APT 3E
301 PARKING LOT MARTIN LUTHER KING PLAZA
302
303
304
305
306
307
308 STREET
309
310
311
312
313
314
315
316
317 REAR SCHMIDT EQUIPMENT AND SUPPLY
318
319
320 LOVES TRUCK STOP
321 HANK S PACKAGE LIQUOR
322
323 @BAR-BASTILLE BASTILLE
324 BALL PARK VILLAGE BALLPARK VILLAGE / BUDWEISER BREW H
325
326
327
328
329
330
331 ON STREET
332 1ST FL
333
334
335
336
337
338 GOODFELLOW PLACE APARTMENTS
339
340
341
342
343
344
345
346
347
348 PARKING LOT CLINTON PEABODY
349
350 REAR EAST ALLEY
351
352
353 REAR
354 REAR DETACHED GARAGE- VACANT RESIDEN
Neighborhood ILEADSStreet ILEADSAddress Description
1 3 S 38TH ST 5215 HOMICIDE
2 17 ITASKA ST 3111 HOMICIDE
3 1 HOLLY HILLS AVE 629 HOMICIDE
4 53 N KINGSHIGHWAY BLVD 1225 HOMICIDE
5 68 W GREEN LEA PL 4132 HOMICIDE
6 63 N 14TH ST 1908 HOMICIDE
7 67 N GRAND BLVD 4038 HOMICIDE
8 74 GIMBLIN ST 1018 HOMICIDE
9 57 ALDINE AVE 4337 HOMICIDE
10 50 BURD AVE 2524 HOMICIDE
11 59 FRANKLIN AVE 3114 HOMICIDE
12 65 KNAPP ST 3245 HOMICIDE
13 57 W COTE BRILLIANTE AVE 4229 HOMICIDE
14 8 KINSEY PL 6272 HOMICIDE
15 50 KENNERLY AVE 5972 HOMICIDE
16 50 GROVER ST 2521 HOMICIDE
17 56 DR MARTIN LUTHER KING DR 4621 HOMICIDE
18 50 COTE BRILLIANTE AVE 5895 HOMICIDE
19 48 HAMILTON AVE 1021 HOMICIDE
20 36 N 15TH ST 709 HOMICIDE
21 68 NATURAL BRIDGE AVE 4231 HOMICIDE
22 17 MICHIGAN AVE 4461 HOMICIDE
23 74 BADEN AVE 724 HOMICIDE
24 19 S GRAND BLVD 3630 HOMICIDE
25 14 HEREFORD ST 3322 HOMICIDE
26 19 S COMPTON AVE 3751 HOMICIDE
27 51 DR MARTIN LUTHER KING DR 5084 HOMICIDE
28 1 VIRGINIA AVE 5301 HOMICIDE
29 78 CLARA AVE 1401 HOMICIDE
30 22 CONGRESS ST 1909 HOMICIDE
31 67 LEE AVE 3836 HOMICIDE
32 57 W COTE BRILLIANTE AVE 4267 HOMICIDE
33 64 N BROADWAY 4828 HOMICIDE
34 57 BILLUPS AVE 1707 HOMICIDE
35 74 LOWELL ST 8861 HOMICIDE
36 78 MINERVA AVE 5634 HOMICIDE
37 17 OSCEOLA ST 3110 HOMICIDE
38 60 RAUSCHENBACH AVE 3117 HOMICIDE
39 70 MCARTHUR AVE 5910 HOMICIDE
40 59 DR MARTIN LUTHER KING DR 3731 HOMICIDE
41 74 RIVERVIEW BLVD 1124 HOMICIDE
42 15 POTOMAC ST 3949 HOMICIDE
43 5 WALLACE AVE 4314 HOMICIDE
44 50 BURD AVE 2728 HOMICIDE
45 50 UNION BLVD 2611 HOMICIDE
46 49 VERNON AVE 5326 HOMICIDE
47 56 N NEWSTEAD AVE 3224 HOMICIDE
48 59 GAMBLE ST 2900 HOMICIDE
49 76 ERA AVE 5530 HOMICIDE
50 25 LOUISIANA AVE 2328 HOMICIDE
51 15 CONNECTICUT ST 3634 HOMICIDE
52 50 SAINT LOUIS AVE 5920 HOMICIDE
53 71 EMERSON AVE 5416 HOMICIDE
54 33 DILLON DR 1124 HOMICIDE
55 68 NATURAL BRIDGE AVE 4231 HOMICIDE
56 69 CARRIE AVE 4539 HOMICIDE
57 60 N 13TH ST 1513 HOMICIDE
58 71 SHREVE AVE 5318 HOMICIDE
59 19 S COMPTON AVE 3720 HOMICIDE
60 50 PALM ST 5556 HOMICIDE
61 61 COCHRAN PL 1459 HOMICIDE
62 56 GARFIELD AVE 3915 HOMICIDE
63 72 THRUSH AVE 5400 HOMICIDE
64 16 MERAMEC ST 3711 HOMICIDE
65 76 FLOY AVE 5592 HOMICIDE
66 72 ROBIN AVE 5598 HOMICIDE
67 2 W DAVIS ST 547 HOMICIDE
68 74 SWITZER AVE 951 HOMICIDE
69 56 DICK GREGORY PL 1524 HOMICIDE
70 18 CHIPPEWA ST 2605 HOMICIDE
71 65 N 21ST ST 3915 HOMICIDE
72 66 E PRAIRIE AVE 2009 HOMICIDE
73 74 FREDERICK ST 8216 HOMICIDE
74 77 JOSEPHINE BAKER AVE 700 HOMICIDE
75 50 HIGHLAND AVE 5627 HOMICIDE
76 1 IDAHO AVE 6440 HOMICIDE
77 78 SEMPLE AVE 1416 HOMICIDE
78 74 RIVERVIEW BLVD 1052 HOMICIDE
79 72 W FLORISSANT AVE 5728 HOMICIDE
80 74 N BROADWAY 8216 HOMICIDE
81 19 LOUISIANA AVE 3513 HOMICIDE
82 69 FARLIN AVE 4892 HOMICIDE
83 72 PLOVER AVE 4936 HOMICIDE
84 69 N NEWSTEAD AVE 4149 HOMICIDE
85 51 ENRIGHT AVE 5048 HOMICIDE
86 56 LABADIE AVE 4435 HOMICIDE
87 59 N VANDEVENTER 2815 HOMICIDE
88 17 MINNESOTA AVE 4629 HOMICIDE
89 55 HIGHLAND AVE 4900 HOMICIDE
90 16 KEOKUK ST 3836 HOMICIDE
91 56 LEXINGTON AVE 3963 HOMICIDE
92 69 KOSSUTH AVE 4834 HOMICIDE
93 68 RED BUD AVE 3980 HOMICIDE
94 15 GILES AVE 3521 HOMICIDE
95 50 DR MARTIN LUTHER KING DR 5971 HOMICIDE
96 78 BELT AVE 1431 HOMICIDE
97 78 BLACKSTONE AVE 1387 HOMICIDE
98 16 LOUISIANA AVE 4114 HOMICIDE
99 67 BAILEY AVE 3215 HOMICIDE
100 58 PENDLETON AVE 924 HOMICIDE
101 33 HICKORY LN 1435 HOMICIDE
102 77 N VANDEVENTER AVE 1420 HOMICIDE
103 72 ROBIN AVE 5428 HOMICIDE
104 79 E CARRIE AVE / I 70 WESTBOUND 0 HOMICIDE
105 54 MCMILLAN AVE 4561 HOMICIDE
106 18 WISCONSIN AVE 3853 HOMICIDE
107 59 PRAIRIE AVE 3614 HOMICIDE
108 17 MICHIGAN AVE 4622 HOMICIDE
109 76 GOODFELLOW BLVD 5000 HOMICIDE
110 61 MURPHY PARK DR 1847 HOMICIDE
111 59 N GRAND BLVD 2546 HOMICIDE
112 48 N SKINKER BLVD 882 HOMICIDE
113 60 W PALM ST 2522 HOMICIDE
114 16 OSAGE ST 3019 HOMICIDE
115 78 MINERVA AVE 5363 HOMICIDE
116 17 MICHIGAN AVE 4404 HOMICIDE
117 16 CALIFORNIA AVE 4056 HOMICIDE
118 16 MERAMEC ST 3145 HOMICIDE
119 16 MINNESOTA AVE 3754 HOMICIDE
120 1 IDAHO AVE 5417 HOMICIDE
121 2 VERMONT AVE 7315 HOMICIDE
122 57 ALDINE AVE 4349 HOMICIDE
123 56 PALM ST 3926 HOMICIDE
124 38 PARKVIEW PL 4921 HOMICIDE
125 53 N KINGSHIGHWAY BLVD 900 HOMICIDE
126 56 N MARKET ST 4647 HOMICIDE
127 59 N MARKET ST 3800 HOMICIDE
128 30 VIRGINIA AVE 3429 HOMICIDE
129 54 MCMILLAN AVE 4735 HOMICIDE
130 56 N TAYLOR AVE 3120 HOMICIDE
131 65 N FLORISSANT AVE 3330 HOMICIDE
132 76 GOODFELLOW BLVD 5003 HOMICIDE
133 16 KLOCKE ST 3400 HOMICIDE
134 76 GOODFELLOW BLVD 5003 HOMICIDE
135 55 N EUCLID AVE 2944 HOMICIDE
136 66 E JOHN AVE 1439 HOMICIDE
137 73 NORTH POINTE BLVD 6139 HOMICIDE
138 15 GUSTINE AVE 3619 HOMICIDE
139 5 DELOR ST 4254 HOMICIDE
140 58 C D BANKS AVE 4158 HOMICIDE
141 65 N 14TH ST 3504 HOMICIDE
142 25 TENNESSEE AVE 2601 HOMICIDE
143 1 BATES ST 535 HOMICIDE
144 69 PENROSE ST 4481 HOMICIDE
145 50 THEODOSIA AVE 5606 HOMICIDE
146 69 SHREVE AVE 4049 HOMICIDE
147 56 GARFIELD AVE 4040 HOMICIDE
148 37 SAMUEL SHEPARD DR 2946 HOMICIDE
149 35 N 4TH ST 300 HOMICIDE
150 84 I 70 WESTBOUND / N KINGSHIGHWA 0 HOMICIDE
151 50 ROOSEVELT PL 5816 HOMICIDE
152 16 CHIPPEWA ST 3013 HOMICIDE
153 72 PLOVER AVE 4938 HOMICIDE
154 68 CLARENCE AVE 4415 HOMICIDE
155 55 DR MARTIN LUTHER KING DR 4815 HOMICIDE
156 50 GOODFELLOW BLVD 2747 HOMICIDE
157 5 GRAVOIS AVE 5401 HOMICIDE
158 50 UNION BLVD 3431 HOMICIDE
159 69 RICHARD PL 4625 HOMICIDE
160 17 S BROADWAY 4355 HOMICIDE
161 76 EMMA AVE 6307 HOMICIDE
162 48 ENRIGHT AVE 5616 HOMICIDE
163 55 MARCUS AVE 2613 HOMICIDE
164 76 SHERRY AVE 6120 HOMICIDE
165 51 RIDGE AVE 5140 HOMICIDE
166 35 CHESTNUT ST 714 HOMICIDE
167 66 N 19TH ST 4406 HOMICIDE
168 57 SAINT FERDINAND AVE 4370 HOMICIDE
169 63 N MARKET PL 1101 HOMICIDE
170 52 ST LOUIS AVE 5105 HOMICIDE
171 62 N 13TH ST 1420 HOMICIDE
172 35 N 9TH ST 205 HOMICIDE
173 37 OLIVE ST 3037 HOMICIDE
174 65 BLAIR AVE 4109 HOMICIDE
175 17 NEBRASKA AVE 4528 HOMICIDE
176 54 MCMILLAN AVE 4503 HOMICIDE
177 25 ARKANSAS AVE 3170 HOMICIDE
178 50 WABADA AVE 5969 HOMICIDE
179 54 ENRIGHT AVE 4550 HOMICIDE
180 74 HOWELL ST 1181 HOMICIDE
181 53 N KINGSHIGHWAY BLVD 930 HOMICIDE
182 74 HALLS FERRY RD 9006 HOMICIDE
183 48 JULIAN AVE 5985 HOMICIDE
184 74 HOWELL ST 1115 HOMICIDE
185 59 N GRAND BLVD 3101 HOMICIDE
186 56 GARFIELD AVE 4012 HOMICIDE
187 77 FRANKLIN AVE 3311 HOMICIDE
188 19 IOWA AVE 3420 HOMICIDE
189 53 AUBERT AVE 773 HOMICIDE
190 54 NEWBERRY TER 4502 HOMICIDE
191 61 HOGAN ST 1320 HOMICIDE
192 16 ALASKA AVE 4630 HOMICIDE
193 53 N KINGSHIGHWAY BLVD 1408 HOMICIDE
194 21 ALLEN AVE 1051 HOMICIDE
195 60 BENTON ST 1933 HOMICIDE
196 76 SHERRY AVE 6341 HOMICIDE
197 69 SEXAUER AVE 4420 HOMICIDE
198 1 IDAHO AVE 7138 HOMICIDE
199 48 GOODFELLOW BLVD 853 HOMICIDE
200 4 GRAVOIS AVE 7422 HOMICIDE
201 76 SUMMIT AVE 5629 HOMICIDE
202 75 RIVERVIEW DR / WB 270 0 HOMICIDE
203 44 W PARK AVE 6763 HOMICIDE
204 54 PAGE BLVD 4711 HOMICIDE
205 33 HICKORY LN 1468 HOMICIDE
206 78 DR MARTIN LUTHER KING DR 5390 HOMICIDE
207 78 RIDGE AVE 5368 HOMICIDE
208 59 STODDARD ST 2800 HOMICIDE
209 63 N 7TH ST / I 70 WESTBOUND 0 HOMICIDE
210 30 PENNSYLVANIA AVE 3244 HOMICIDE
211 78 RIDGE AVE 5401 HOMICIDE
212 71 N KINGSHIGHWAY BLVD 5406 HOMICIDE
213 71 ARLINGTON AVE 4943 HOMICIDE
214 69 KOSSUTH AVE 4863 HOMICIDE
215 63 CLINTON ST 1200 HOMICIDE
216 36 LOCUST ST 1527 HOMICIDE
217 56 WHITTIER ST 3047 HOMICIDE
218 37 S GRAND BLVD 715 HOMICIDE
219 72 EMERSON AVE 4921 HOMICIDE
220 50 PATTON AVE 5331 HOMICIDE
221 69 LEE AVE 4440 HOMICIDE
222 52 UNION BLVD 2700 HOMICIDE
223 59 MONTGOMERY ST 3561 HOMICIDE
224 50 HIGHLAND AVE 5824 HOMICIDE
225 46 KINGSBURY PL 5708 HOMICIDE
226 59 JAMES COOL PAPA BELL AVE 2920 HOMICIDE
227 67 SHERMAN PL 3921 HOMICIDE
228 59 COTTAGE AVE 3842 HOMICIDE
229 66 E LINTON AVE 2157 HOMICIDE
230 72 GILMORE AVE 5276 HOMICIDE
231 5 MORGANFORD RD 4528 HOMICIDE
232 25 HARTFORD ST 3230 HOMICIDE
233 55 N EUCLID AVE 2944 HOMICIDE
234 65 PENROSE ST 2115 HOMICIDE
235 50 WABADA AVE 5324 HOMICIDE
236 67 LEE AVE 3838 HOMICIDE
237 50 MAFFITT AVE 5870 HOMICIDE
238 56 DR MARTIN LUTHER KING DR 4557 HOMICIDE
239 52 SAINT LOUIS AVE 5027 HOMICIDE
240 1 PENNSYLVANIA AVE 5913 HOMICIDE
241 56 LEXINGTON AVE 4476 HOMICIDE
242 53 AUBERT AVE 1224 HOMICIDE
243 74 CANAAN AVE 915 HOMICIDE
244 57 N TAYLOR AVE 2814 HOMICIDE
245 55 MARCUS AVE 3061 HOMICIDE
246 58 EVANS AVE 4220 HOMICIDE
247 69 CARTER AVE 4836 HOMICIDE
248 16 MINNESOTA AVE 3754 HOMICIDE
249 56 LABADIE AVE 3945 HOMICIDE
250 59 BACON ST 2409 HOMICIDE
251 56 N VANDEVENTER AVE 2821 HOMICIDE
252 72 WREN AVE 5019 HOMICIDE
253 36 S 18TH ST / CLARK AVE 0 HOMICIDE
254 59 BELL AVE 3100 HOMICIDE
255 1 SCHIRMER ST 802 HOMICIDE
256 16 ALBERTA ST 3921 HOMICIDE
257 2 PENNSYLVANIA AVE 7403 HOMICIDE
258 16 DELOR ST 3659 HOMICIDE
259 66 RANDALL PL 4426 HOMICIDE
260 13 DALTON AVE 2639 HOMICIDE
261 73 MORA LN 8561 HOMICIDE
262 51 UNION BLVD 1122 HOMICIDE
263 55 HAMMETT PL 4851 HOMICIDE
264 74 HALLS FERRY RD 8612 HOMICIDE
265 78 HAMILTON AVE 1452 HOMICIDE
266 76 SHULTE AVE 6035 HOMICIDE
267 68 CARTER AVE 4042 HOMICIDE
268 53 N EUCLID AVE 785 HOMICIDE
269 67 N GRAND BLVD 4206 HOMICIDE
270 1 ALABAMA AVE 7146 HOMICIDE
271 74 ELIAS AVE 933 HOMICIDE
272 76 ACME AVE 5636 HOMICIDE
273 74 CHURCH RD 8309 HOMICIDE
274 55 LABADIE AVE 4843 HOMICIDE
275 72 WREN AVE 5055 HOMICIDE
276 36 N 21ST ST 715 HOMICIDE
277 1 LOUISIANA AVE 5205 HOMICIDE
278 63 CLINTON ST 1455 HOMICIDE
279 72 EMERSON AVE 5201 HOMICIDE
280 60 E DODIER ST 2511 HOMICIDE
281 62 N 13TH ST 1430 HOMICIDE
282 69 MARCUS AVE 4100 HOMICIDE
283 63 CHAMBERS ST 1120 HOMICIDE
284 68 E KOSSUTH AVE 4235 HOMICIDE
285 66 E DE SOTO AVE 1401 HOMICIDE
286 17 WALSH ST 308 HOMICIDE
287 16 S GRAND BLVD 3900 HOMICIDE
288 55 CUPPLES PL 4741 HOMICIDE
289 2 VERMONT AVE 7343 HOMICIDE
290 19 VIRGINIA AVE 3620 HOMICIDE
291 31 HICKORY ST 2651 HOMICIDE
292 74 ELIAS AVE 827 HOMICIDE
293 74 CANAAN AVE 971 HOMICIDE
294 70 GOODFELLOW BLVD 4301 HOMICIDE
295 69 SHREVE AVE 4106 HOMICIDE
296 39 NORFOLK AVE 4247 HOMICIDE
297 56 NEW ASHLAND PL 3100 HOMICIDE
298 27 SHENANDOAH AVE 3658 HOMICIDE
299 19 OHIO AVE 3454 HOMICIDE
300 14 CHIPPEWA ST 4939 HOMICIDE
301 58 DR MARTIN LUTHER KING DR 4308 HOMICIDE
302 16 PENNSYLVANIA AVE 3942 HOMICIDE
303 15 MERAMEC ST 4255 HOMICIDE
304 50 GOODFELLOW BLVD 3351 HOMICIDE
305 72 WREN AVE 5270 HOMICIDE
306 16 DUNNICA AVE 3946 HOMICIDE
307 60 MADISON ST 1501 HOMICIDE
308 35 CARR ST 202 HOMICIDE
309 73 GOODFELLOW BLVD 5961 HOMICIDE
310 69 FARLIN AVE 4435 HOMICIDE
311 74 RIVERVIEW BLVD 907 HOMICIDE
312 71 GERALDINE AVE 5300 HOMICIDE
313 50 WABADA AVE 5962 HOMICIDE
314 52 TERRY AVE 5252 HOMICIDE
315 50 CLARA AVE 3340 HOMICIDE
316 72 DAVISON AVE 5271 HOMICIDE
317 17 EICHELBERGER ST 411 HOMICIDE
318 1 BATES ST 1005 HOMICIDE
319 78 ARLINGTON AVE 1460 HOMICIDE
320 79 N BROADWAY 6124 HOMICIDE
321 78 ARLINGTON AVE 1401 HOMICIDE
322 59 LABADIE AVE 3850 HOMICIDE
323 21 RUSSELL AVE 1027 HOMICIDE
324 35 CLARK AVE 601 HOMICIDE
325 78 HAMILTON TER 5946 HOMICIDE
326 39 MANCHESTER AVE 4229 HOMICIDE
327 66 CONDE ST 5220 HOMICIDE
328 50 HIGHLAND AVE 5971 HOMICIDE
329 72 ROBIN AVE 5016 HOMICIDE
330 69 FARLIN AVE 4950 HOMICIDE
331 61 COCHRAN PL 1457 HOMICIDE
332 2 UPTON ST 225 HOMICIDE
333 16 MICHIGAN AVE 4108 HOMICIDE
334 15 LACKLAND AVE 3144 HOMICIDE
335 74 ETON LN 1545 HOMICIDE
336 74 BITTNER ST 732 HOMICIDE
337 16 TENNESSEE AVE 4758 HOMICIDE
338 50 GOODFELLOW PL 5713 HOMICIDE
339 19 MINNESOTA AVE 3741 HOMICIDE
340 51 MINERVA AVE 5228 HOMICIDE
341 68 ATHLONE AVE 4448 HOMICIDE
342 74 HARLAN AVE 916 HOMICIDE
343 71 CLAXTON AVE 5381 HOMICIDE
344 70 FERRIS AVE 5911 HOMICIDE
345 54 EVANS AVE 4527 HOMICIDE
346 54 PAGE BLVD 4630 HOMICIDE
347 50 COTE BRILLIANTE AVE 5951 HOMICIDE
348 33 RUTGER LN 1417 HOMICIDE
349 68 E ALICE AVE 2011 HOMICIDE
350 19 NEBRASKA AVE 3646 HOMICIDE
351 56 LEXINGTON AVE 4520 HOMICIDE
352 68 CLAY AVE 4221 HOMICIDE
353 50 BELT AVE 2738 HOMICIDE
354 51 MAPLE AVE 5009 HOMICIDE
District Crime DateOccur CodedMonth Complaint
1 1 10000 2017-02-22 23:45:00 2018-02-28 18-008475
2 1 10000 2018-03-17 23:00:00 2018-04-28 18-012176
3 1 10000 2019-06-27 01:48:00 2019-07-28 19-030335
4 5 10000 2019-10-30 21:07:00 2019-11-28 19-054615
5 6 10000 2018-12-01 17:37:00 2018-12-28 18-056958
6 4 10000 2018-11-01 23:00:00 2018-11-28 18-052272
7 4 10000 2018-11-01 13:07:00 2018-11-28 18-052152
8 6 10000 2018-08-01 23:03:00 2018-08-28 18-035664
9 5 10000 2018-05-01 21:20:00 2018-05-28 18-019411
10 5 10000 2018-05-01 12:47:00 2018-05-28 18-019311
11 4 10000 2018-05-01 11:00:00 2018-05-28 18-020625
12 4 10000 2018-03-01 18:30:00 2018-03-28 18-009700
13 5 10000 2018-03-01 08:50:00 2018-03-28 18-009431
14 2 10000 2018-02-01 16:30:00 2018-02-28 18-005350
15 5 10000 2018-01-01 17:00:00 2018-01-28 18-000142
16 5 10000 2019-04-01 15:29:00 2019-04-28 19-014217
17 5 10000 2019-06-01 01:03:00 2019-06-28 19-025297
18 5 10000 2019-06-01 02:48:00 2019-06-28 19-025312
19 5 10000 2019-09-01 04:15:00 2019-09-28 19-043120
20 4 10000 2019-11-01 02:22:00 2019-11-28 19-054833
21 6 10000 2018-12-02 15:16:00 2018-12-28 18-057082
22 1 10000 2018-08-02 14:02:00 2018-08-28 18-035796
23 6 10000 2018-07-02 22:00:00 2018-07-28 18-030399
24 3 10000 2018-06-02 01:34:00 2018-06-28 18-024776
25 2 10000 2018-05-02 19:00:00 2018-05-28 18-019792
26 3 10000 2018-05-02 15:01:00 2018-05-28 18-019544
27 5 10000 2018-02-02 23:25:00 2018-02-28 18-005382
28 1 10000 2019-06-02 08:52:00 2019-06-28 19-025531
29 5 10000 2019-06-02 10:05:00 2019-06-28 19-025525
30 3 10000 2018-10-02 17:45:00 2018-10-28 18-046912
31 6 10000 2018-10-02 20:35:00 2018-10-28 18-046933
32 5 10000 2018-10-02 23:40:00 2018-10-28 18-046944
33 6 10000 2018-12-03 01:24:00 2018-12-28 18-057161
34 5 10000 2018-07-03 17:34:00 2018-07-28 18-030526
35 6 10000 2018-04-03 18:00:00 2018-04-28 18-014712
36 5 10000 2019-01-03 13:36:00 2019-01-28 19-000429
37 1 10000 2019-02-03 19:30:00 2019-02-28 19-005180
38 4 10000 2019-06-03 06:03:00 2019-06-28 19-025632
39 6 10000 2019-07-03 08:31:00 2019-07-28 19-031517
40 4 10000 2019-10-03 15:02:00 2019-10-28 19-049602
41 6 10000 2018-11-04 21:05:00 2018-11-28 18-052745
42 2 10000 2018-07-04 23:45:00 2018-07-28 18-030719
43 1 10000 2018-06-04 21:43:00 2018-06-28 18-025251
44 5 10000 2019-03-04 20:03:00 2019-03-28 19-009857
45 5 10000 2019-05-04 03:13:00 2019-05-28 19-020139
46 5 10000 2019-05-04 10:40:00 2019-05-28 19-020183
47 6 10000 2019-06-04 01:40:00 2019-06-28 19-025830
48 4 10000 2019-07-04 21:55:00 2019-07-28 19-031835
49 6 10000 2019-10-04 23:45:00 2019-10-28 19-049868
50 3 10000 2018-10-04 09:00:00 2018-10-28 18-047230
51 2 10000 2018-12-05 16:45:00 2018-12-28 18-057634
52 5 10000 2018-09-05 08:18:00 2018-09-28 18-041936
53 6 10000 2018-08-05 21:10:00 2018-08-28 18-036365
54 3 10000 2018-07-05 21:26:00 2018-07-28 18-030894
55 6 10000 2018-07-05 19:44:00 2018-07-28 18-030877
56 6 10000 2018-07-05 11:18:00 2018-07-28 18-030812
57 4 10000 2018-07-05 00:20:00 2018-07-28 18-030729
58 6 10000 2018-03-05 00:45:00 2018-03-28 18-009971
59 3 10000 2018-01-05 21:45:00 2018-01-28 18-000847
60 5 10000 2019-03-05 13:00:00 2019-03-28 19-009999
61 4 10000 2019-08-05 01:18:00 2019-08-28 19-037801
62 5 10000 2019-09-05 00:51:00 2019-09-28 19-043868
63 6 10000 2019-09-05 13:25:00 2019-09-28 19-043969
64 1 10000 2019-11-05 09:26:00 2019-11-28 19-055608
65 6 10000 2018-09-06 19:09:00 2018-09-28 18-042231
66 6 10000 2018-07-06 21:42:00 2018-07-28 18-031078
67 1 10000 2018-06-06 05:40:00 2018-06-28 18-025503
68 6 10000 2018-05-06 16:25:00 2018-05-28 18-020241
69 5 10000 2018-04-06 20:10:00 2018-04-28 18-015241
70 3 10000 2019-01-06 15:30:00 2019-01-28 19-000915
71 4 10000 2019-04-06 15:35:00 2019-04-28 19-015072
72 6 10000 2019-04-06 21:50:00 2019-04-28 19-015262
73 6 10000 2019-05-06 23:15:00 2019-05-28 19-020657
74 4 10000 2019-06-06 11:58:00 2019-06-28 19-026300
75 5 10000 2019-11-06 23:00:00 2019-11-28 19-055927
76 1 10000 2018-10-06 09:46:00 2018-10-28 18-047573
77 5 10000 2019-12-06 21:15:00 2019-12-28 19-061107
78 6 10000 2018-08-07 22:30:00 2018-08-28 18-036817
79 6 10000 2018-06-07 04:04:00 2018-06-28 18-025671
80 6 10000 2019-01-07 22:09:00 2019-01-28 19-001140
81 3 10000 2019-07-07 01:45:00 2019-07-28 19-032275
82 6 10000 2019-07-07 13:56:00 2019-07-28 19-032327
83 6 10000 2019-08-07 16:26:00 2019-08-28 19-038332
84 6 10000 2019-09-07 00:45:00 2019-09-28 19-044294
85 5 10000 2019-10-07 10:01:00 2019-10-28 19-050231
86 6 10000 2018-09-08 20:39:00 2018-09-28 18-042567
87 4 10000 2018-08-08 22:35:00 2018-08-28 18-037014
88 1 10000 2018-07-08 19:20:00 2018-07-28 18-031390
89 5 10000 2019-02-08 11:55:00 2019-02-28 19-005986
90 1 10000 2019-04-08 18:30:00 2019-04-28 19-015480
91 6 10000 2019-05-08 21:28:00 2019-05-28 19-021058
92 6 10000 2019-05-08 21:35:00 2019-05-28 19-021064
93 6 10000 2019-06-08 22:30:00 2019-06-28 19-026743
94 2 10000 2019-10-08 21:23:00 2019-10-28 19-050574
95 5 10000 2018-10-08 17:14:00 2018-10-28 18-047961
96 5 10000 2018-10-08 18:41:00 2018-10-28 18-047980
97 5 10000 2018-10-08 22:00:00 2018-10-28 18-048002
98 3 10000 2018-12-09 00:01:00 2018-12-28 18-058563
99 4 10000 2018-07-09 21:12:00 2018-07-28 18-031594
100 5 10000 2018-03-09 15:00:00 2018-03-28 18-010716
101 3 10000 2018-02-09 21:09:00 2018-02-28 18-006392
102 4 10000 2018-01-09 17:31:00 2018-01-28 18-001431
103 6 10000 2019-01-09 00:10:00 2019-01-28 19-001348
104 6 10000 2019-01-09 22:00:00 2019-01-28 19-001524
105 5 10000 2019-04-09 17:58:00 2019-04-28 19-015697
106 3 10000 2019-04-09 22:45:00 2019-04-28 19-015726
107 4 10000 2019-06-09 15:56:00 2019-06-28 19-026862
108 1 10000 2019-06-09 20:32:00 2019-06-28 19-026905
109 6 10000 2019-08-09 13:54:00 2019-08-28 19-038644
110 4 10000 2019-08-09 22:30:00 2019-08-28 19-038720
111 4 10000 2019-11-09 01:50:00 2019-11-28 19-056263
112 5 10000 2018-10-09 20:00:00 2018-10-28 18-048475
113 4 10000 2019-12-09 12:05:00 2019-12-28 19-061480
114 3 10000 2019-12-09 12:50:00 2019-12-28 19-061497
115 5 10000 2018-09-10 21:49:00 2018-09-28 18-042941
116 1 10000 2018-05-10 20:10:00 2018-05-28 18-020963
117 3 10000 2018-03-10 01:04:00 2018-03-28 18-010757
118 3 10000 2019-07-10 11:28:00 2019-07-28 19-032846
119 3 10000 2018-10-10 09:48:00 2018-10-28 18-048273
120 1 10000 2018-09-11 22:20:00 2018-09-28 18-043140
121 1 10000 2018-07-11 17:40:00 2018-07-28 18-031935
122 5 10000 2018-06-11 14:41:00 2018-06-28 18-026460
123 6 10000 2018-05-11 16:47:00 2018-05-28 18-021101
124 5 10000 2018-01-11 10:28:00 2018-01-28 18-001737
125 5 10000 2019-02-11 16:00:00 2019-02-28 19-007995
126 5 10000 2019-02-11 20:50:00 2019-02-28 19-006513
127 4 10000 2019-02-11 22:00:00 2019-02-28 19-006512
128 3 10000 2019-04-11 19:40:00 2019-04-28 19-016075
129 5 10000 2019-07-11 00:42:00 2019-07-28 19-032961
130 6 10000 2019-07-11 18:00:00 2019-07-28 19-033107
131 4 10000 2019-08-11 02:00:00 2019-08-28 19-038947
132 6 10000 2019-09-11 23:27:00 2019-09-28 19-045265
133 3 10000 2019-11-11 15:54:00 2019-11-28 19-056681
134 6 10000 2018-12-12 12:53:00 2018-12-28 18-058750
135 6 10000 2018-05-12 21:15:00 2018-05-28 18-021274
136 6 10000 2018-02-12 19:39:00 2018-02-28 18-006829
137 6 10000 2018-01-12 22:47:00 2018-01-28 18-001995
138 2 10000 2018-01-12 10:00:00 2018-01-28 18-001895
139 1 10000 2019-01-12 22:22:00 2019-01-28 19-001896
140 5 10000 2019-03-12 00:45:00 2019-03-28 19-010953
141 4 10000 2019-08-12 17:06:00 2019-08-28 19-039284
142 3 10000 2019-12-12 18:20:00 2019-12-28 19-062140
143 1 10000 2019-12-12 21:22:00 2019-12-28 19-062167
144 6 10000 2018-08-13 23:55:00 2018-08-28 18-037897
145 5 10000 2018-06-13 22:34:00 2018-06-28 18-026927
146 6 10000 2019-05-13 20:52:00 2019-05-28 19-021920
147 5 10000 2019-09-13 18:43:00 2019-09-28 19-045605
148 4 10000 2019-10-13 18:46:00 2019-10-28 19-051389
149 4 10000 2018-10-13 00:18:00 2018-10-28 18-048718
150 6 10000 2018-12-14 03:46:00 2018-12-28 18-058996
151 5 10000 2018-08-14 09:15:00 2018-08-28 18-037957
152 3 10000 2019-06-14 23:50:00 2019-06-28 19-027970
153 6 10000 2018-10-14 00:27:00 2018-10-28 18-048841
154 6 10000 2018-11-15 19:18:00 2018-11-28 18-054501
155 5 10000 2018-09-15 06:30:00 2018-09-28 18-044019
156 5 10000 2018-07-15 13:32:00 2018-07-28 18-032477
157 1 10000 2018-04-15 01:11:00 2018-04-28 18-016585
158 5 10000 2019-01-15 15:12:00 2019-01-28 19-002278
159 6 10000 2019-06-15 11:10:00 2019-06-28 19-028033
160 1 10000 2019-07-15 02:54:00 2019-07-28 19-033672
161 6 10000 2018-10-15 22:32:00 2018-10-28 18-049140
162 5 10000 2019-12-15 00:10:00 2019-12-28 19-062475
163 5 10000 2018-12-16 07:38:00 2018-12-28 18-059333
164 6 10000 2018-09-16 16:10:00 2018-09-28 18-044013
165 5 10000 2018-08-16 04:00:00 2018-08-28 18-038296
166 4 10000 2018-06-16 12:34:00 2018-06-28 18-027359
167 4 10000 2018-06-16 04:30:00 2018-06-28 18-027307
168 5 10000 2019-01-16 23:48:00 2019-01-28 19-002489
169 4 10000 2019-08-16 17:13:00 2019-08-28 19-040096
170 5 10000 2019-08-16 22:21:00 2019-08-28 19-040121
171 4 10000 2019-10-16 21:01:00 2019-10-28 19-052008
172 4 10000 2018-08-17 10:48:00 2018-08-28 18-038533
173 4 10000 2018-06-17 23:55:00 2018-06-28 18-027605
174 4 10000 2018-06-17 00:05:00 2018-06-28 18-027449
175 1 10000 2018-03-17 23:19:00 2018-03-28 18-012030
176 5 10000 2018-03-17 16:10:00 2018-03-28 18-011972
177 3 10000 2018-01-17 23:45:00 2018-01-28 18-002737
178 5 10000 2019-01-17 16:31:00 2019-01-28 19-002622
179 5 10000 2019-03-17 21:40:00 2019-03-28 19-011872
180 6 10000 2019-06-17 03:10:00 2019-06-28 19-028344
181 5 10000 2019-07-17 00:06:00 2019-07-28 19-034095
182 6 10000 2019-07-17 23:00:00 2019-07-28 19-034271
183 5 10000 2019-07-17 23:10:00 2019-07-28 19-034276
184 6 10000 2019-10-17 01:16:00 2019-10-28 19-052025
185 4 10000 2018-08-18 12:58:00 2018-08-28 18-038739
186 5 10000 2018-03-18 09:19:00 2018-03-28 18-012080
187 4 10000 2018-01-18 13:41:00 2018-01-28 18-002826
188 3 10000 2019-03-18 21:08:00 2019-03-28 19-012060
189 5 10000 2019-03-18 23:39:00 2019-03-28 19-012105
190 5 10000 2019-07-18 10:10:00 2019-07-28 19-034345
191 4 10000 2019-08-18 02:15:00 2019-08-28 19-040288
192 1 10000 2019-11-18 00:50:00 2019-11-28 19-057833
193 5 10000 2018-10-18 15:10:00 2018-10-28 18-049640
194 3 10000 2018-12-19 23:35:00 2018-12-28 18-059926
195 4 10000 2018-12-19 23:30:00 2018-12-28 18-059924
196 6 10000 2018-07-19 18:30:00 2018-07-28 18-033260
197 6 10000 2018-05-19 21:45:00 2018-05-28 18-022532
198 1 10000 2018-05-19 02:40:00 2018-05-28 18-022391
199 5 10000 2018-03-19 10:26:00 2018-03-28 18-012231
200 1 10000 2018-01-19 17:25:00 2018-01-28 18-003034
201 6 10000 2019-03-19 11:07:00 2019-03-28 19-012127
202 6 10000 2019-05-19 20:06:00 2019-05-28 19-023050
203 2 10000 2019-07-19 12:50:00 2019-07-28 19-034583
204 5 10000 2019-07-19 20:31:00 2019-07-28 19-034640
205 3 10000 2019-09-19 20:51:00 2019-09-28 19-046892
206 5 10000 2019-11-19 07:08:00 2019-11-28 19-058060
207 5 10000 2019-11-19 10:41:00 2019-11-28 19-058099
208 4 10000 2019-11-19 19:34:00 2019-11-28 19-058200
209 4 10000 2018-12-20 23:39:00 2018-12-28 18-060116
210 3 10000 2018-07-20 05:02:00 2018-07-28 18-033301
211 5 10000 2018-03-20 19:37:00 2018-03-28 18-012492
212 6 10000 2018-01-20 22:53:00 2018-01-28 18-003218
213 6 10000 2019-05-20 21:38:00 2019-05-28 19-023244
214 6 10000 2019-07-20 15:00:00 2019-07-28 19-034765
215 4 10000 2019-10-20 05:10:00 2019-10-28 19-052582
216 4 10000 2018-11-21 15:24:00 2018-11-28 18-055391
217 6 10000 2018-09-21 09:53:00 2018-09-28 18-044906
218 4 10000 2018-08-21 18:38:00 2018-08-28 18-039325
219 6 10000 2019-01-21 07:00:00 2019-01-28 19-003132
220 5 10000 2019-06-21 13:55:00 2019-06-28 19-029263
221 6 10000 2019-06-21 17:00:00 2019-06-28 19-029441
222 5 10000 2019-07-21 05:09:00 2019-07-28 19-034864
223 4 10000 2019-07-21 12:59:00 2019-07-28 19-034925
224 5 10000 2019-09-21 11:13:00 2019-09-28 19-047170
225 5 10000 2019-12-21 01:00:00 2019-12-28 19-063436
226 4 10000 2019-12-21 23:53:00 2019-12-28 19-063479
227 6 10000 2018-12-22 19:35:00 2018-12-28 18-060407
228 4 10000 2018-07-22 19:34:00 2018-07-28 18-033754
229 6 10000 2018-05-22 13:12:00 2018-05-28 18-022949
230 6 10000 2018-04-22 06:00:00 2018-04-28 18-017730
231 1 10000 2018-02-22 23:21:00 2018-02-28 18-008449
232 3 10000 2019-05-22 23:00:00 2019-05-28 19-023675
233 6 10000 2019-06-22 18:32:00 2019-06-28 19-029491
234 4 10000 2019-08-22 07:36:00 2019-08-28 19-041089
235 5 10000 2019-09-22 03:35:00 2019-09-28 19-047288
236 6 10000 2019-09-22 14:51:00 2019-09-28 19-047376
237 5 10000 2019-09-22 23:31:00 2019-09-28 19-047468
238 5 10000 2019-11-22 10:35:00 2019-11-28 19-058671
239 5 10000 2019-12-22 03:15:00 2019-12-28 19-063500
240 1 10000 2018-12-23 16:45:00 2018-12-28 18-060518
241 6 10000 2018-12-23 12:32:00 2018-12-28 18-060494
242 5 10000 2018-12-23 04:00:00 2018-12-28 18-060452
243 6 10000 2018-09-23 20:38:00 2018-09-28 18-045308
244 5 10000 2018-09-23 12:23:00 2018-09-28 18-045238
245 6 10000 2018-09-23 11:47:00 2018-09-28 18-045235
246 5 10000 2018-09-23 07:22:00 2018-09-28 18-045200
247 6 10000 2018-08-23 10:15:00 2018-08-28 18-039664
248 3 10000 2018-07-23 17:30:00 2018-07-28 18-033958
249 6 10000 2018-07-23 03:01:00 2018-07-28 18-033795
250 4 10000 2018-07-23 00:40:00 2018-07-28 18-033782
251 5 10000 2018-03-23 20:10:00 2018-03-28 18-012955
252 6 10000 2018-01-23 22:40:00 2018-01-28 18-003685
253 4 10000 2018-01-23 13:29:00 2018-01-28 18-003611
254 4 10000 2019-01-23 07:48:00 2019-01-28 19-003372
255 1 10000 2019-01-23 21:12:00 2019-01-28 19-003512
256 1 10000 2019-01-23 22:56:00 2019-01-28 19-003515
257 1 10000 2019-04-23 13:00:00 2019-04-28 19-018137
258 1 10000 2019-06-23 00:20:00 2019-06-28 19-029522
259 4 10000 2019-06-23 15:00:00 2019-06-28 19-029774
260 2 10000 2019-07-23 21:00:00 2019-07-28 19-035553
261 6 10000 2019-07-23 21:55:00 2019-07-28 19-035400
262 5 10000 2019-08-23 20:06:00 2019-08-28 19-041462
263 5 10000 2019-09-23 19:40:00 2019-09-28 19-047675
264 6 10000 2019-09-23 20:45:00 2019-09-28 19-047678
265 5 10000 2019-09-23 21:49:00 2019-09-28 19-047681
266 6 10000 2019-10-23 20:30:00 2019-10-28 19-053304
267 6 10000 2018-12-24 12:22:00 2018-12-28 18-060607
268 5 10000 2018-09-24 04:00:00 2018-09-28 18-045348
269 4 10000 2018-04-24 03:14:00 2018-04-28 18-018038
270 1 10000 2019-03-24 21:15:00 2019-03-28 19-012978
271 6 10000 2019-04-24 03:08:00 2019-04-28 19-018259
272 6 10000 2019-05-24 21:47:00 2019-05-28 19-024031
273 6 10000 2019-06-24 12:00:00 2019-06-28 19-030268
274 6 10000 2019-06-24 22:58:00 2019-06-28 19-029879
275 6 10000 2019-08-24 14:25:00 2019-08-28 19-041580
276 4 10000 2019-08-24 14:46:00 2019-08-28 19-041611
277 1 10000 2019-11-24 23:23:00 2019-11-28 19-059088
278 4 10000 2018-09-25 22:03:00 2018-09-28 18-045713
279 6 10000 2018-09-25 12:29:00 2018-09-28 18-045634
280 4 10000 2018-06-25 21:36:00 2018-06-28 18-029092
281 4 10000 2018-06-25 17:15:00 2018-06-28 18-029066
282 6 10000 2018-06-25 10:39:00 2018-06-28 18-029029
283 4 10000 2018-05-25 01:15:00 2018-05-28 18-023389
284 6 10000 2018-01-25 10:00:00 2018-01-28 18-004168
285 6 10000 2019-01-25 22:07:00 2019-01-28 19-003800
286 1 10000 2019-03-25 23:32:00 2019-03-28 19-013127
287 3 10000 2019-04-25 05:00:00 2019-04-28 19-018478
288 5 10000 2019-06-25 03:13:00 2019-06-28 19-029897
289 1 10000 2019-08-25 05:56:00 2019-08-28 19-041690
290 3 10000 2019-11-25 18:38:00 2019-11-28 19-059224
291 3 10000 2018-12-26 21:56:00 2018-12-28 18-060906
292 6 10000 2018-09-26 19:35:00 2018-09-28 18-045926
293 6 10000 2018-09-26 10:30:00 2018-09-28 18-045789
294 6 10000 2018-04-26 11:45:00 2018-04-28 18-018445
295 6 10000 2018-02-26 00:25:00 2018-02-28 18-008902
296 2 10000 2019-09-26 19:02:00 2019-09-28 19-048273
297 6 10000 2019-11-26 10:32:00 2019-11-28 19-059309
298 2 10000 2018-11-27 22:00:00 2018-11-28 18-056435
299 3 10000 2018-07-27 22:36:00 2018-07-28 18-034695
300 2 10000 2018-04-27 23:00:00 2018-04-28 18-018711
301 5 10000 2018-02-27 16:27:00 2018-02-28 18-009211
302 3 10000 2018-01-27 14:12:00 2018-01-28 18-004367
303 2 10000 2018-01-27 13:00:00 2018-01-28 18-004387
304 5 10000 2018-01-27 01:22:00 2018-01-28 18-004265
305 6 10000 2019-01-27 15:01:00 2019-01-28 19-004046
306 1 10000 2019-01-27 21:00:00 2019-01-28 19-004099
307 4 10000 2019-04-27 05:17:00 2019-04-28 19-018891
308 4 10000 2019-05-27 01:08:00 2019-05-28 19-024375
309 6 10000 2019-05-27 23:15:00 2019-05-28 19-024517
310 6 10000 2019-09-27 18:50:00 2019-09-28 19-048489
311 6 10000 2019-11-27 16:00:00 2019-11-28 19-059586
312 6 10000 2018-12-28 01:15:00 2018-12-28 18-061082
313 5 10000 2018-09-28 11:30:00 2018-09-28 18-046177
314 5 10000 2018-07-28 22:15:00 2018-07-28 18-034867
315 5 10000 2018-04-28 03:20:00 2018-04-28 18-018735
316 6 10000 2019-01-28 12:40:00 2019-01-28 19-004172
317 1 10000 2019-09-28 16:20:00 2019-09-28 19-048627
318 1 10000 2019-10-28 14:51:00 2019-10-28 19-054188
319 5 10000 2019-11-28 00:25:00 2019-11-28 19-059621
320 6 10000 2019-11-28 13:09:00 2019-11-28 19-059669
321 5 10000 2018-11-29 20:49:00 2018-11-28 18-056624
322 4 10000 2018-07-29 00:56:00 2018-07-28 18-034886
323 3 10000 2018-05-29 00:11:00 2018-05-28 18-023978
324 4 10000 2018-04-29 20:00:00 2018-04-28 18-018976
325 5 10000 2018-04-29 17:00:00 2018-04-28 18-018954
326 2 10000 2018-01-29 00:55:00 2018-01-28 18-004573
327 6 10000 2019-04-29 07:05:00 2019-04-28 19-019206
328 5 10000 2019-04-29 16:10:00 2019-04-28 19-019314
329 6 10000 2019-05-29 06:10:00 2019-05-28 19-024763
330 6 10000 2019-05-29 12:00:00 2019-05-28 19-025192
331 4 10000 2019-05-29 12:17:00 2019-05-28 19-024821
332 1 10000 2019-06-29 21:00:00 2019-06-28 19-030913
333 3 10000 2019-08-29 11:58:00 2019-08-28 19-042610
334 2 10000 2018-10-29 07:30:00 2018-10-28 18-051495
335 6 10000 2018-10-29 10:40:00 2018-10-28 18-051536
336 6 10000 2018-10-29 15:48:00 2018-10-28 18-051586
337 1 10000 2018-09-30 20:27:00 2018-09-28 18-046560
338 5 10000 2018-09-30 15:33:00 2018-09-28 18-046536
339 3 10000 2018-09-30 12:09:00 2018-09-28 18-046508
340 5 10000 2018-07-30 20:48:00 2018-07-28 18-035236
341 6 10000 2018-07-30 05:20:00 2018-07-28 18-035065
342 6 10000 2018-06-30 18:44:00 2018-06-28 18-029981
343 6 10000 2018-01-30 10:40:00 2018-01-28 18-004812
344 6 10000 2019-04-30 23:45:00 2019-04-28 19-019547
345 5 10000 2019-05-30 12:47:00 2019-05-28 19-025009
346 5 10000 2019-05-30 20:56:00 2019-05-28 19-025076
347 5 10000 2019-05-30 21:58:00 2019-05-28 19-025102
348 3 10000 2019-07-30 00:01:00 2019-07-28 19-036579
349 6 10000 2019-12-30 18:10:00 2019-12-28 19-064753
350 3 10000 2018-05-31 01:30:00 2018-05-28 18-024433
351 6 10000 2018-01-31 11:15:00 2018-01-28 18-004969
352 6 10000 2019-05-31 12:59:00 2019-05-28 19-025205
353 5 10000 2019-08-31 04:34:00 2019-08-28 19-042948
354 5 10000 2018-10-31 16:23:00 2018-10-28 18-051996
NHD_NAME
1 Holly Hills
2 Mount Pleasant
3 Carondelet
4 Fountain Park
5 O'Fallon
6 Old North St. Louis
7 Fairground Neighborhood
8 Baden
9 The Ville
10 Wells Goodfellow
11 Jeff Vanderlou
12 Hyde Park
13 The Ville
14 St. Louis Hills
15 Wells Goodfellow
16 Wells Goodfellow
17 Greater Ville
18 Wells Goodfellow
19 West End
20 Downtown West
21 O'Fallon
22 Mount Pleasant
23 Baden
24 Gravois Park
25 North Hampton
26 Gravois Park
27 Academy
28 Carondelet
29 Hamilton Heights
30 Benton Park
31 Fairground Neighborhood
32 The Ville
33 Near North Riverfront
34 The Ville
35 Baden
36 Hamilton Heights
37 Mount Pleasant
38 St. Louis Place
39 Mark Twain I-70 Industrial
40 Jeff Vanderlou
41 Baden
42 Tower Grove South
43 Bevo Mill
44 Wells Goodfellow
45 Wells Goodfellow
46 Visitation Park
47 Greater Ville
48 Jeff Vanderlou
49 Walnut Park West
50 Tower Grove East
51 Tower Grove South
52 Wells Goodfellow
53 Mark Twain
54 Peabody Darst Webbe
55 O'Fallon
56 Penrose
57 St. Louis Place
58 Mark Twain
59 Gravois Park
60 Wells Goodfellow
61 Carr Square
62 Greater Ville
63 Walnut Park East
64 Dutchtown
65 Walnut Park West
66 Walnut Park East
67 Patch
68 Baden
69 Greater Ville
70 Marine Villa
71 Hyde Park
72 College Hill
73 Baden
74 Covenant Blu-Grand Center
75 Wells Goodfellow
76 Carondelet
77 Hamilton Heights
78 Baden
79 Walnut Park East
80 Baden
81 Gravois Park
82 Penrose
83 Walnut Park East
84 Penrose
85 Academy
86 Greater Ville
87 Jeff Vanderlou
88 Mount Pleasant
89 Kingsway East
90 Dutchtown
91 Greater Ville
92 Penrose
93 O'Fallon
94 Tower Grove South
95 Wells Goodfellow
96 Hamilton Heights
97 Hamilton Heights
98 Dutchtown
99 Fairground Neighborhood
100 Vandeventer
101 Peabody Darst Webbe
102 Covenant Blu-Grand Center
103 Walnut Park East
104 North Riverfront
105 Lewis Place
106 Marine Villa
107 Jeff Vanderlou
108 Mount Pleasant
109 Walnut Park West
110 Carr Square
111 Jeff Vanderlou
112 West End
113 St. Louis Place
114 Dutchtown
115 Hamilton Heights
116 Mount Pleasant
117 Dutchtown
118 Dutchtown
119 Dutchtown
120 Carondelet
121 Patch
122 The Ville
123 Greater Ville
124 Central West End
125 Fountain Park
126 Greater Ville
127 Jeff Vanderlou
128 Benton Park West
129 Lewis Place
130 Greater Ville
131 Hyde Park
132 Walnut Park West
133 Dutchtown
134 Walnut Park West
135 Kingsway East
136 College Hill
137 North Pointe
138 Tower Grove South
139 Bevo Mill
140 Vandeventer
141 Hyde Park
142 Tower Grove East
143 Carondelet
144 Penrose
145 Wells Goodfellow
146 Penrose
147 Greater Ville
148 Midtown
149 Downtown
150 Penrose Park
151 Wells Goodfellow
152 Dutchtown
153 Walnut Park East
154 O'Fallon
155 Kingsway East
156 Wells Goodfellow
157 Bevo Mill
158 Wells Goodfellow
159 Penrose
160 Mount Pleasant
161 Walnut Park West
162 West End
163 Kingsway East
164 Walnut Park West
165 Academy
166 Downtown
167 College Hill
168 The Ville
169 Old North St. Louis
170 Kingsway West
171 Columbus Square
172 Downtown
173 Midtown
174 Hyde Park
175 Mount Pleasant
176 Lewis Place
177 Tower Grove East
178 Wells Goodfellow
179 Lewis Place
180 Baden
181 Fountain Park
182 Baden
183 West End
184 Baden
185 Jeff Vanderlou
186 Greater Ville
187 Covenant Blu-Grand Center
188 Gravois Park
189 Fountain Park
190 Lewis Place
191 Carr Square
192 Dutchtown
193 Fountain Park
194 Soulard
195 St. Louis Place
196 Walnut Park West
197 Penrose
198 Carondelet
199 West End
200 Boulevard Heights
201 Walnut Park West
202 Riverview
203 Hi-Pointe
204 Lewis Place
205 Peabody Darst Webbe
206 Hamilton Heights
207 Hamilton Heights
208 Jeff Vanderlou
209 Old North St. Louis
210 Benton Park West
211 Hamilton Heights
212 Mark Twain
213 Mark Twain
214 Penrose
215 Old North St. Louis
216 Downtown West
217 Greater Ville
218 Midtown
219 Walnut Park East
220 Wells Goodfellow
221 Penrose
222 Kingsway West
223 Jeff Vanderlou
224 Wells Goodfellow
225 Skinker DeBaliviere
226 Jeff Vanderlou
227 Fairground Neighborhood
228 Jeff Vanderlou
229 College Hill
230 Walnut Park East
231 Bevo Mill
232 Tower Grove East
233 Kingsway East
234 Hyde Park
235 Wells Goodfellow
236 Fairground Neighborhood
237 Wells Goodfellow
238 Greater Ville
239 Kingsway West
240 Carondelet
241 Greater Ville
242 Fountain Park
243 Baden
244 The Ville
245 Kingsway East
246 Vandeventer
247 Penrose
248 Dutchtown
249 Greater Ville
250 Jeff Vanderlou
251 Greater Ville
252 Walnut Park East
253 Downtown West
254 Jeff Vanderlou
255 Carondelet
256 Dutchtown
257 Patch
258 Dutchtown
259 College Hill
260 Southwest Garden
261 North Pointe
262 Academy
263 Kingsway East
264 Baden
265 Hamilton Heights
266 Walnut Park West
267 O'Fallon
268 Fountain Park
269 Fairground Neighborhood
270 Carondelet
271 Baden
272 Walnut Park West
273 Baden
274 Kingsway East
275 Walnut Park East
276 Downtown West
277 Carondelet
278 Old North St. Louis
279 Walnut Park East
280 St. Louis Place
281 Columbus Square
282 Penrose
283 Old North St. Louis
284 O'Fallon
285 College Hill
286 Mount Pleasant
287 Dutchtown
288 Kingsway East
289 Patch
290 Gravois Park
291 The Gate District
292 Baden
293 Baden
294 Mark Twain I-70 Industrial
295 Penrose
296 Forest Park South East
297 Greater Ville
298 Shaw
299 Gravois Park
300 North Hampton
301 Vandeventer
302 Dutchtown
303 Tower Grove South
304 Wells Goodfellow
305 Walnut Park East
306 Dutchtown
307 St. Louis Place
308 Downtown
309 North Pointe
310 Penrose
311 Baden
312 Mark Twain
313 Wells Goodfellow
314 Kingsway West
315 Wells Goodfellow
316 Walnut Park East
317 Mount Pleasant
318 Carondelet
319 Hamilton Heights
320 North Riverfront
321 Hamilton Heights
322 Jeff Vanderlou
323 Soulard
324 Downtown
325 Hamilton Heights
326 Forest Park South East
327 College Hill
328 Wells Goodfellow
329 Walnut Park East
330 Penrose
331 Carr Square
332 Patch
333 Dutchtown
334 Tower Grove South
335 Baden
336 Baden
337 Dutchtown
338 Wells Goodfellow
339 Gravois Park
340 Academy
341 O'Fallon
342 Baden
343 Mark Twain
344 Mark Twain I-70 Industrial
345 Lewis Place
346 Lewis Place
347 Wells Goodfellow
348 Peabody Darst Webbe
349 O'Fallon
350 Gravois Park
351 Greater Ville
352 O'Fallon
353 Wells Goodfellow
354 Academy
These records are in much better shape.
They have both X and Y coordinates.
Function transforms all the State Plane Coordinate values into NAD84 lat/long coordinates.
More modern mapping structure used for GPS Mapping.
Used censusxy library to pull latitude/longitude.
The geocode function from the library requires a street address and number, city, and zip code (if available).
It goes to the US Census Bureau to look up the address reported on police record and returns a lat/long.
It creates an sf file and allows plotting of locations on a map.
Can only convert 22 instances with censusxy since some addresses locations are missing.
Add neighborhoods.
From https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html
These records are overlaid on the neighborhood polygons.
They have both X and Y coordinates.
***
Peaks illustrate highest crime numbers for that area.
Contours indicate similiar occurrances.
It uses clusters counts to illustrate homicice numbers in selected city areas.
As you drill down it recalculates the numbers over city areas.
From intersection of Goodfellow and MLK.
North along Goodfellow to W. Florissant.
Then Southeast along W. Florissant to Prarie.
Then southwest along Prarie/Vandeventner to MLK.
Back to MLK and Goodfellow.
This is how it plots out with homicides.
A better prediction here, but the box still misses the south side hotspot.
Also, note the area running west along Interstate 55 and Northwest along Interstate 70.
And the mayor said she would give him an A?
Established in 2014.
These are the 6 police districts.
Now they are considering restructuring them again.
They want to increase the number.
Improvement or just more overhead?
Need to collect more data for greater understanding of crime parameters.
This data set has close to 8,000 instances of “FIREARM” defined crime. Where are the locations?
Need to plot heroine and cocaine locations to see overlaps.
There is no gang data available since 2012. St Louis does not have a Gang Division. Does it need one?
UCR reporting structure is poorly constructed for nation as a whole. How could it be improved?
---
title: "Homicides"
output:
flexdashboard::flex_dashboard:
storyboard: true
source_code: embed
theme: cerulean
---
```{r, echo=FALSE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = FALSE,
include = FALSE,
eval = TRUE,
message = FALSE,
warning = FALSE,
fig.retina = 1,
tidy = TRUE)
```
```{r echo=FALSE}
# install all the library packages
library(rgdal)
library(sp)
library(sf)
library(raster)
library(leaflet)
library(leafpop)
library(mapview)
library(tidyverse)
library(censusxy)
library(tidycensus)
library(ggplot2)
library(ggmap)
library(plotly)
library(RColorBrewer)
library(data.table)
library(fasttime)
library(sparklyr)
library(lubridate)
library(maps)
library(stringr)
library(readr)
library(knitr)
```
### 1. Begin by collecting crime data from the STL Metropolitan Police Website
```{r, include=TRUE}
# Collect St Louis City crime UCR statistics
# pull in state coordinate system files from st louis police reports using data.table
crime <- fread("Group2018.csv", stringsAsFactors=FALSE)
head(crime)
```
***
- The STL Metropolitan Police produces a monthly crime update.
- Stored in a csv format and can be downloaded.
- Located at .
- The file provides all crime details collected from the preceding month.
- Contains locations, neighborhoods, precincts, map coordinates and times of crimes in the St Louis Metropolitan Area.
### 2. Look at the Data Values
```{r, include=TRUE}
summary(crime)
```
***
- Again, some fields are irrelevant to our analysis.
- We will remove these elements using a tidyverse library called *dplyr*.
- We will also have to restructure certain date/time variables.
- Flags are not needed.
- Don't see how count field is significant in the analysis.
### 3. Adjust Data Structures to Match that Needed for Analysis
```{r, include=TRUE}
crimeA <- crime %>%
dplyr::select(-FlagCrime, -FlagUnfounded, -FlagAdministrative, -Count, -FlagCleanup) %>%
filter(Crime == 10000) %>%
distinct(Complaint, .keep_all = TRUE)
glimpse(crimeA)
```
***
- I wanted to select a specific crime. In this case we will look at Homicides.
- Some data fields are not relevant to the analysis so I've limited the data to the following 6 elements.
- Homicides are UCR coded as *10000*.
- Although the STLMPD website states rows are unique, they are *NOT*.
- During this phase I also wanted to determine data types.
- The mix is a combination of characters string and integers.
- I will have to re-charactize some elements to more easily manipulate later.
- "CodedMonth" and "DateOccur" are not date/time elements, so they need to be changed.
### 4. Prepare Data for Manipulating Date/time Fields
```{r, include=FALSE}
crimeA$CodedMonth <- str_c(crimeA$CodedMonth, "28", sep = "-") # use stringr to create add a day to the y/m structure
crimeA$CodedMonth <- as_date(crimeA$CodedMonth) # use lubridate to convert to actual y/m/d
crimeA$DateOccur <- mdy_hm(crimeA$DateOccur) # use lubridate to change string to date/time structure
```
```{r, include=TRUE}
### Result of Changing String Value {data-background=#fae5e3}
# - "CodedMonth" is now a date format and "DateOccur" is now a POSIX date time data type.
# - Check structures of the data.
str(crimeA)
```
***
- Need to use some R libraries to convert data types.
- Used *stringr* and *lubridate* libraries to change data types.
- Changed "CodedMonth" to a string value closer to one resembling a year/month/day field.
- Used 28 days as the day value so I do not have to constantly worry about the changing days/month values.
- Since the data is collected as of the last day of the month, it will not affect the monthly crime perspective.
- Next I created a concatonated string group and convert that field into a "POSIX" day/month/day variable.
```{r}
### Check Final Data Structure {data-background=#fae5e3}
summary(crimeA)
```
```{r}
### Make Date Structures Compatable and Calculate Reporting Delays {data-background=#fae5e3}
# - An interesting side note is to see the differences between reporting day and actual incident date.
# - Some of the records are reported significantly longer than 30 days.
crimeB <- crimeA %>% mutate(Reporting.diff = CodedMonth - as_date(DateOccur)) %>%
dplyr::select(Reporting.diff:Complaint) %>%
arrange(desc(Reporting.diff))
crimeB$Neighborhood <- as_factor(crimeB$Neighborhood) # change to factor for later join
```
### 5. Review Reporting Delays
```{r, include=TRUE}
crimeB
```
### **6. Bring in the Neighborhood Details**
```{r, include=TRUE}
### Now join neighborhoods with names
#add neighborhood shapes to a data frame
# From https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html
hoods.sf <- readOGR("St Louis Shape files/nbrhds_wards/BND_Nhd88_cw.shp")
hoods.sf <- spTransform(hoods.sf, CRS("+proj=longlat +datum=WGS84"))
hoods <- mapview(hoods.sf, map.types = c("OpenStreetMap"),
layer.name = c("Neighborhoods"),
alpha.regions = 0.1,
alpha = 2,
legend = FALSE,
zcol = c("NHD_NAME"))
hoods
```
***
- Collected US Census data to bring in geospatial polygons that represent St Louis Neighborhoods.
- Transformed mapview data into *WGS84* structure.
- Check to make sure data is a geospatial object.
- Use census geospatial data to generate a map.
```{r}
### Convert Neighborhood Details {data-background=#fae5e3}
# - Change SF file into a data frame.
# collect neighborhood details from shape file
hoods.df <- as(hoods.sf, "data.frame")
class(hoods.df) # check class
```
### 7. Look at the data frame after adding in Neighborhood data
```{r, include=TRUE}
glimpse(hoods.df)
```
***
- We have 88 neighborhoods and their name and number are factor types in R.
- The polygon shapes are included in this data frame.
```{r}
### Clean Up Data - Trim Neighborhoods and Prepare for Joins {data-background=#fae5e3}
# - Bring in the neighborhood name with their respective number codes.
# - Create a new data frame.
crimeC <- hoods.df %>% dplyr::select(NHD_NUM, NHD_NAME)
# crimeC$NHD_NUM <- as.integer(crimeC$NHD_NUM) # convert to integer
# join carkacks table with hoods table to get neighborhood names
crimeD <- left_join(crimeB, crimeC, by = c("Neighborhood" = "NHD_NUM"))
```
```{r}
### See the Final Data Frame
glimpse(crimeD)
```
### 8. Group by Month and Count Number of Homicides per Month
```{r, include=TRUE}
crimeA %>%
group_by(CodedMonth) %>%
count(Crime) %>%
arrange(desc(n))
```
***
- Group data by coded month.
- Count the number of *homicides per month*.
- Data presented in a bar graph with totals displayed above the bar.
- I added a smoothing line to get a better view of the crime movement.
- Note that October 2018 was the peak.
- It was when Channel 5 reported the sever increase in carjackings. Looks like homicids too.
- It was also the timeframe when they reported establishing atask force.
```{r, include=FALSE}
### Plot the count by month
crime.month <- crimeA %>%
group_by(CodedMonth) %>%
count(Crime) %>%
arrange(desc(n))
xx = ggplot(crime.month, aes(x = CodedMonth, y = n)) +
geom_text(aes(label = n, y = n), size = 5, position = position_stack(vjust = 1.2)) +
geom_col(color = "cornflowerblue") +
geom_point() +
stat_smooth() + # add a smoothing regerssion for time series
scale_x_date(date_breaks = "4 weeks", date_labels = "%m") +
theme(axis.text.x = element_text(angle = 90)) + # change tex to verticle
labs(title = "Homicides Per Month", x= "Month", y = "C
Homicide Count")
```
### **9. Plot Homicides per Month Using _ggplot2_ Library**
```{r, include=TRUE}
### Homicides by Month
xx
```
### 10. Look at Neighborhood's by Name and Count Numbers {data-background=#fae5e3}
```{r, include=TRUE}
### Neighborhood By Name
### Group by Neighborhood and count
crimeD %>%
mutate_if(is.factor,
fct_explicit_na,
na_level = "to_impute") %>%
group_by(NHD_NAME) %>%
count(Crime, sort = TRUE) %>%
arrange(desc(n)) %>%
ungroup()%>%
mutate (cumulative = cumsum(n), total = sum(n), cumul.percent = cumsum(c(n/total *100)))
```
***
- Had to adjust the factor variables (NHD_NAME) and to account for missing variables (NA).
- Count by crime and put in decending order.
- This is a display of the highest crime neighborhoods.
- 70% of the homicides are committed in the top 21 neighborhoods (23%)
```{r}
### 11. Neighborhoods Count by Month
# - Group by Neighborhood Name.
# - Chart puts data in a descending order and presents greater than 5.
### Plot the count by month
hood.number <- crimeD %>%
mutate_if(is.factor,
fct_explicit_na,
na_level = "to_impute") %>%
group_by(NHD_NAME) %>%
count(Crime) %>%
filter(n > 5) %>%
arrange(desc(n))
```
```{r}
xy = ggplot(hood.number, aes(x = reorder(NHD_NAME, +n), y = n)) +
geom_bar(stat = "identity") +
geom_col(color = "cornflowerblue") +
coord_flip() +
theme(axis.text.x = element_text(angle = 90)) + # change tex to verticle
labs(title = "Homicides by Neighborhood", x= "Neighborhood", y = "Homicide Count")
```
### **11. Neighborhoods Count by Month**
```{r, include=TRUE}
xy
```
***
- Group by Neighborhood Name.
- Chart puts data in a descending order and presents greater than 5.
```{r, echo=FALSE, include=FALSE}
### 12. Time of Day Carjacks
## create and mutate an hour of day field using lubridate
hour.day <- as.integer(format(crimeA$DateOccur, "%H"))
crimeA <- crimeA %>% as_tibble() %>%
mutate(hr.day = as.integer(format(crimeA$DateOccur, "%H")))
## This adds a new field to crimeA data frame to categorize a day into 6 hour blocks
## used a logic functons to segment day categories
## adds field to crimeA
crimeA$day.cat <- ifelse(crimeA$hr.day > 0 & crimeA$hr.day < 6, "night",
ifelse(crimeA$hr.day >= 6 & crimeA$hr.day < 12, 'morning',
ifelse(crimeA$hr.day > 12 & crimeA$hr.day <= 18, "afternoon",
ifelse(crimeA$hr.day > 18 & crimeA$hr.day < 24, "evening",
ifelse(crimeA$hr.day == 0, "night",
ifelse(crimeA$hr.day == 12, "afternoon", NA ))))))
## arrange as factors
day.lvls <- c("morning", "afternoon", "evening", "night")
crimeA$day.cat <- factor(crimeA$day.cat, levels = day.lvls)
```
### **12. Time of Day Carjacks**
```{r, echo=FALSE, include=TRUE}
ggplot(crimeA) +
geom_bar(aes(x = CodedMonth, fill = factor(day.cat)))+
scale_x_date(date_breaks = "28 days", date_labels = "%B") +
scale_fill_discrete(name = "Timeframe", labels = c("Morning", "Afternoon", "Evening", "Night")) +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Monthly Homicide Timeframe", x= "Time of Day", y = "Homicides Count")
```
***
- Create and mutate an hour of day field using lubridate.
- This adds a new field to crimeA data frame to categorize a day into 6 hour blocks.
- Used a logic functions to segment day categories
### 13. Let's Look at the Geospatial Aspects of the Homicide Analysis
```{r, include=TRUE}
### Summary of the Characteristics of the Crime Data {data-background=#fae5e3}
summary(crimeD)
```
***
- We will use the data we restructed earlier in the analysis.
- We will use the crime D file.
- Check the structure of the file we selected.
### 14. Important to understanding the geospatial structures of the data
- XCoord and YCoord coordinates are based on the State Plane North American Datum 1983 (NAD83) format.
- This data will have to be converted to lat/long values.
- Some of the XCoords and YCoords have values of O. This will need to be accounted for later in the analysis.
```{r}
### Let's Review the Basic Data Structure {data-background=#fae5e3}
str(crimeD)
```
### 18. Must Account For Inconsistent Coordinate Data
```{r}
crimeD.zeros <- crimeD %>% filter(XCoord < 1)
```
```{r, include=TRUE}
### Missing Coordinates {data-background=#fae5e3}
crimeD.zeros # there are 20 homicide records that cannot be processed directly
```
***
- Collect those records whose X/Y values are zeros.
- These records will need a different type of processing.
```{r}
### Records That Can Be Directly Converted to Lat/Long {data-background=#fae5e3}
crimeD.complete <- crimeD %>% filter(XCoord > 1)
```
### 19. Complete Records
```{r, include=TRUE}
crimeD.complete
```
***
- These records are in much better shape.
- They have both X and Y coordinates.
### 20. Now we need to convert the NAD83 Coordinates to WGS84 Structure
```{r, echo=TRUE}
nad83_coords <- data.frame(x=crimeD.complete$XCoord, y=crimeD.complete$YCoord) # My coordinates in NAD83
nad83_coords <- nad83_coords *.3048 ### Feet to meters
coordinates(nad83_coords) <- c('x', 'y')
proj4string(nad83_coords)=CRS("+init=epsg:2815")
coordinates_deg <- spTransform(nad83_coords,CRS("+init=epsg:4326"))
coordinates_deg
#str(coordinates_deg)
#class(coordinates_deg)
# add converted lat-lonf and convert to numeric values
crimeD.complete$lon <- as.numeric(coordinates_deg$x)
crimeD.complete$lat <- as.numeric(coordinates_deg$y)
#class(crimeD.complete)
```
***
- Function transforms all the State Plane Coordinate values into NAD84 lat/long coordinates.
- More modern mapping structure used for GPS Mapping.
```{r}
### Review Charistics of Downloaded Crime Data {data-background=#fae5e3}
glimpse(crimeD.complete)
```
### 21. Get Incomplete Data Missing Coordinates {data-background=#fae5e3}
- Used _censusxy_ library to pull latitude/longitude.
- The geocode function from the library requires a street address and number, city, and zip code (if available).
- It goes to the US Census Bureau to look up the address reported on police record and returns a lat/long.
- It creates an _sf_ file and allows plotting of locations on a map.
- Can only convert 22 instances with _censusxy_ since some addresses locations are missing.
```{r}
data <- mutate(crimeD.zeros, address.comb = paste(CADAddress, CADStreet, sep = " "), city = "St Louis", state = "MO")
crimeD_sf <- cxy_geocode(data, address = address.comb, city = city, state = state, style = "minimal", output = "sf")
STL_homicides.small <- mapview(crimeD_sf,
map.types = c("OpenStreetMap"),
legend = FALSE,
popup = popupTable(data,zcol = c("Complaint",
"CodedMonth",
"NHD_NAME",
"District",
"Crime",
"Description")))
```
```{r}
### Locations Obtained From US Census With Addresses Only ...
STL_homicides.small
```
```{r}
### Larger Grouping that Contained Coordinates
#- These records contain the X/Y plotted locations.
### create an sf file that will map coordinates
data.one <- mutate(crimeD.complete, address.comb = paste(CADAddress, CADStreet, sep = " "), city = "St Louis", state = "MO")
crimeD_one.sf <- st_as_sf(data.one, coords = c("lon", "lat"), crs = 4326, agr = "constant")
STL_homicides <- mapview(crimeD_one.sf, map.types = c("OpenStreetMap"),
legend = FALSE,
popup = popupTable(data.one, zcol = c("Complaint",
"CodedMonth",
"NHD_NAME",
"District",
"Crime",
"Description")))
```
### 22. Combine Map Sets to View the Entire Picture of Homicide Location in St Louis
```{r, include=TRUE}
total_homicides <- STL_homicides + STL_homicides.small
total_homicides
```
```{r}
### Bring Up Neighborhood Map {data-background=#fae5e3}
hoods
```
***
- Add neighborhoods.
- From
### **24. Final Map of Homicides with Neighborhood Overlays**
```{r, include=TRUE}
#- Combine all the maps.
total_homicides <- STL_homicides + STL_homicides.small + hoods
total_homicides
```
***
- These records are overlaid on the neighborhood polygons.
- They have both X and Y coordinates.
```{r, echo=FALSE}
### Now We Look at Some Plots Targeting the Intensity of the Crime Area {data-background=#fae5e3}
# - Start with a quick plot of the homicides locations.
### reduce crime to violent crimes in downtown
violent_crimes <- crimeD.complete %>%
filter(
Crime == 10000,
-90.3238 <= lon & lon <= -90.1794334,
38.0 <= lat & lat <= 39.0 )
# use qmplot to make a scatterplot on a map
qmplot(lon, lat, data = violent_crimes,
maptype = "toner-lite", color = I("red"), zoom = 12)
```
### **25. Now We Look at These Homicides Plots with Density Contours**
```{r, include=TRUE}
### Density contour plots
qmplot(lon, lat, data = violent_crimes, maptype = "toner-lite",
geom = "density2d", color = I("red"), zoom = 12)
```
***
- Peaks illustrate highest crime numbers for that area.
- Contours indicate similiar occurrances.
### **26. Another View Using Same Data Set Gives Us Heat Map**
```{r, include=TRUE}
### This provides a good look at the density of homicides in the city
qmplot(lon, lat, data = violent_crimes, geom = "blank",
zoom = 14, maptype = "toner-background", legend = FALSE) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = .35, colour = NA) +
scale_fill_gradient2("Homicides\nHeatmap", low = "white", mid = "yellow", high = "red", midpoint = 20)
```
***
- Darker areas indicate higher level of homicides.
```{r}
### Another View of Crime Area Numbers {data-background=#fae5e3}
# - Use clusters to illustrate numbers in an area
zz <- leaflet(data=crimeD.complete) %>%
addTiles() %>%
setView(-90.222, 38.608, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(lng = ~lon,
lat = ~lat,
fillColor = blues9,
stroke = FALSE, fillOpacity = 0.8,
clusterOptions = markerClusterOptions(),
popup = ~DateOccur) %>%
addPolygons(data= hoods.sf, label = ~NHD_NAME,
color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.005,
highlightOptions = highlightOptions(color = "white",
weight = 2,
bringToFront = TRUE))
```
### **27. Here is a Very Interesting View Called a Cluster Map**
```{r, include=TRUE}
zz
```
***
- It uses clusters counts to illustrate homicice numbers in selected city areas.
- As you drill down it recalculates the numbers over city areas.
```{r}
#### Task force focus
### Created database that defines the crime focus area
police_crime_focus <- fread("police_crime_focus.csv", stringsAsFactors=FALSE)
### Create a spatial file of the police crime focus
# police_crime_focus
police_point.sf <- st_as_sf(police_crime_focus,
coords = c("lon", "lat"),
crs = 4326, agr = "constant")
###police points
police_point.sf
### Create matrisx of lat/long
df <- data.frame(police_crime_focus$lon, police_crime_focus$lat)
# You need first to close your polygon
# (first and last points must be identical)
df <- rbind(df, df[1,])
### Create a lolygon of the area of the police box
police.polygon <- st_sf(st_sfc(st_polygon(list(as.matrix(df)))), crs = 4326)
# police.polygon
police.box <- mapview(police.polygon, map.types = c("OpenStreetMap"),
layer.name = c("Police Box"),
legend = FALSE,
alpha.regions = 0.3,
alpha = 6,
label = NULL,
color = "red",
col.regions = "red")
## Show police box in red
```
### 28. This Illustrates the "Hayden Rectangle" Plotted Out
```{r, include=TRUE}
police.box
```
***
- From intersection of Goodfellow and MLK.
- North along Goodfellow to W. Florissant.
- Then Southeast along W. Florissant to Prarie.
- Then southwest along Prarie/Vandeventner to MLK.
- Back to MLK and Goodfellow.
```{r}
# Add in Police Box
STLtotal_homicides <- STL_homicides + STL_homicides.small + police.box
```
### **29. This is the Chief's Box Overlaid with Homicides**
```{r, include=TRUE}
STLtotal_homicides
```
***
- This is how it plots out with homicides.
- A better prediction here, but the box still misses the south side hotspot.
- Also, note the area running west along Interstate 55 and Northwest along Interstate 70.
- And the mayor said she would give him an *A*?
```{r}
mapshot(total_homicides, url = paste0(getwd(), "/homicide_map.html"),
file = paste0(getwd(), "/homicide_map.png"))
```
```{r}
mapshot(zz , url = paste0(getwd(), "/cluster_homicides.html"),
file = paste0(getwd(), "/cluster_homicides.png"))
```
```{r}
mapshot(STLtotal_homicides , url = paste0(getwd(), "/homicides_police_box.html"),
file = paste0(getwd(), "/homicides_police_box.png"))
```
```{r, message=FALSE}
#add police district shapes to a data frame
police_district.sf <- readOGR("police-districts/GIS.STL.POLICE_DISTRICTS_2014.shp")
police_district.sf <- spTransform(police_district.sf, CRS("+proj=longlat +datum=WGS84"))
police_district <- mapview(police_district.sf, map.types = c("OpenStreetMap"),
layer.name = c("DISTNO"),
alpha.regions = 0.1,
alpha = 7,
legend = FALSE,
zcol = c("DISTNO"))
```
### **30. View Crime based on Police Districts**
```{r, include=TRUE}
police_district
```
***
- Established in 2014.
- These are the 6 police districts.
- Now they are considering restructuring them again.
- They want to increase the number.
- Improvement or just more overhead?
```{r}
# combine total crimes and pokice districts
district_homicides <- police_district + STL_homicides + STL_homicides.small
```
### **31. This Overlays Homicides Within the Police Districts**
```{r, include=TRUE}
district_homicides
```
```{r, echo=FALSE}
# Provide cluster view with current police districts using
xxx <- leaflet(data=crimeD.complete) %>%
addTiles() %>%
setView(-90.222, 38.608, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(lng = ~lon,
lat = ~lat,
fillColor = blues9,
stroke = FALSE, fillOpacity = 0.8,
clusterOptions = markerClusterOptions(),
popup = ~DateOccur) %>%
addPolygons(data=police_district.sf, label = ~DISTNO,
color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.005,
highlightOptions = highlightOptions(color = "white",
weight = 3,))
```
### **32. Finally We Look at Police Districts with Crime Clustering**
```{r, include=TRUE}
xxx
```
***
- Review crimes by each of 6 police districts.
### **33. Food for Thought**
- Need to collect more data for greater understanding of crime parameters.
- This data set has close to 8,000 instances of "FIREARM" defined crime. Where are the locations?
- Need to plot heroine and cocaine locations to see overlaps.
- There is no gang data available since 2012. St Louis does not have a Gang Division. Does it need one?
- UCR reporting structure is poorly constructed for nation as a whole. How could it be improved?