Business Analytics

This document provides an analysis of data related to crime rates and law enforcement in major cities. It explores the patterns of crime, victims' demographics, and the weapons used. The findings help in understanding the burden of crime and enable appropriate measures to be put in place.

Business analytics
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Business analytics
Background
Dealing with lawlessness in major cities of the world has really posed a major challenge to law
enforcers. There has been rising statistics of every kind of crime from cheap threats to robbery
with violence. The increase in this lawlessness has been attributed to rapidly growing population
without employment. It has also been caused by high poverty levels among the low class living
in the shanties of the urban areas. In this regard, various governments have instituted methods of
curbing the crime rates in the major urban areas. The US government has been collecting all data
related to crime from type of crime, number of crimes, location, sex, age and any other
distinguishing characteristics that may help in profiling crime. The analysis of this type of data
help the police department understand the burden of crime that they have thereby enabling them
to put appropriate measures in place. It also helps understand the patterns of crime across a
region which goes a long way in helping to manage lawlessness. However, due to advance level
of crimes, there is need employ better technology to combat this vice. The subsequent section
contains analysis and results from data obtained from the US security department.
Data analysis and findings
Victims’ descent by gender tabulation
GENDER
VICTIM DESCENT F H M X Grand
Total
A 260 252 512
B 1373 1762 3135
C 3 5 8
F 11 4 15
H 2484 1 3372 5857
I 5 7 12
J 4 1 5
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Business analytics
K 19 35 54
O 490 1096 1586
P 3 3 6
W 1093 1804 2897
X 7 26 68 101
Grand Total 5752 1 8367 68 14188
Table 1
Table 1 above is a tabulation of how victims are distributed gender wise and according to their
descent. From the results in the table, it can be observed that majority of crime victims were of H
descent (5857). The other large group that fell victims of crime was of B origin (3135). They are
followed closely by victims from H descent (2897). The least affected groups were victims of J,
P and C descents. They were 5, 6 and 8 respectively. It can also be observed that in terms of
gender, the males had majority of cases (8367) compared to females (5752).
Tabulation of victims according to location and type of arrest
AREA NAME Adult
Arrest
Adult
Other
Invest
Cont
Juv
Arrest
Juv
Other
Grand
Total
77th Street 1 2 12 15
Central 916 1118 7987 25 11 10057
Devonshire 13 13
Foothill 5 1 6
Mission 12 12
N Hollywood 1 4 5
Newton 1 3 13 17
Northeast 2 8 10
Olympic 1 3 8 12
Pacific 3 2 15 20
Rampart 623 742 3351 65 8 4789
Southeast 7 7
Topanga 16 16
Van Nuys 4 5 9
West Valley 2 9 11
Grand Total 1547 1877 11465 90 20 14999
Table 2
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Business analytics
In a bid to understand the patterns of crimes, the study sought to cross tabulate location and the
type of arrests that occurred in those areas. The results were as in table 2 above. It can be
observed that the city of Central had the highest number of arrests (10,057). It was followed from
far by the city of Rampart which had 4789 cases. The rest of the cities had the arrest numbers
relatively equal and not more than 20 cases. In terms of distribution of arrests, the invest cont
was the major type of arrest having 11465 victims. This is followed from far by adult other
(1877) and adult arrest (1547). The least arrests were juvenile arrests (90) and juvenile other (20)
Age descriptive statistics of the victims
AGE DESCRIPTIVE STATISTICS
Mean 41.5073671
6
Standard Error 0.20371791
4
Median 36
Mode 114
Standard
Deviation
24.9494153
3
Sample Variance 622.473325
5
Kurtosis 2.29427139
6
Skewness 1.37923747
2
Range 115
Minimum 0
Maximum 115
Sum 622569
Count 14999
Table 3
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Business analytics
The table above is of the descriptive statistics for the ages of victims of crime. It can be observed
that the mean age for victims is 41.5 years. The median age is 36 years while the modal age of
the victims was 114 years. The oldest victim was 115 years old.
The box and whisker plot of victims’ ages
Figure 1
Box and whisker plot is normally used to determine the distribution of a given variable. It is
usually one of the methods to visually determine the distribution apart from the histogram. It is
also able to show the outliers which affect the normality of data. For this reason, it was employed
in this study to establish the distribution of the victims’ age. As can be observed the age was not
perfectly normally distributed since the median line did not cut the box into two equal parts.
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Business analytics
Table of distribution of weapons used by victims
TOP 5 WEAPONS USED
WEAPON DESCRIPTION
NUMBE
R
PERCENTAG
E
STRONG-ARM (HANDS, FIST, FEET OR BODILY
FORCE) 3938 62.70%
UNKNOWN WEAPON/OTHER WEAPON 508 8.08%
VERBAL THREAT 409 6.50%
HAND GUN 180 2.87%
OTHER KNIFE 153 2.43%
Table 4
Graph of distribution of weapons used by victims
3938
508 409 180 153
Distribution of weapons used
Figure 2
The table and graph above show the number of victims and the weapons used. It was found that
the weapon that was used most was strong arm which had 3938 victims constituting to 62.7%.
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Business analytics
The use of unknown weapons was 508 constituting to 8.08%. Verbal threats were 409
constituting to 6.5%. The least number of weapons that were used are the hand guns and other
knife. They were 180 and 153 respectively. this represented 2.87% and 2.43% respectively.
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