THE CORRELATION ANALYSIS USING IN THE MURDERS STUDY AT THE EXAMPLE OF PRIMORSKY
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JOURNAL OF CRITICAL REVIEWS
ISSN- 2394-5125 VOL 7, ISSUE 18, 2020
THE CORRELATION ANALYSIS USING IN THE
MURDERS STUDY (AT THE EXAMPLE OF PRIMORSKY
KRAY)
Kozyrev Maxim Sergeevich1, Lukashenko Dmitry Vladimirovich2, Litvishkov Vladimir Mikhailovich2, Ilina
Irina Yurievna1
1
- Russian State Social University, Faculty of Management, Department of Management and Administrative
Management, 129226, Russian Federation, Moscow,Wilhelm Pica Street, 4, Buil. 1.
2
- Research Institute of the Federal Penitentiary Service of Russia, Moscow, Russian Federation, 125130, Moscow,
Narvskaya Street, 15A, 1.
Corresponding author: Kozyrev Maxim Sergeevich, 129226, Russian Federation, Moscow, Wilhelm Pica Street, 4,
Building 1; tel. +7(495)255-67-67; e-mail: kozyrevms@rgsu.net.
Abstract: This work has aims to study the factors of certain types of crimes through correlation analysis. The object of the
study became murders in the Primorsky Kray. The data of the Federal State Statistics Service and the Ministry of Internal
Affairs of the Russian Federation were used as the information base of the work. Based on the analysis of the correlation
coefficients‟ matrices, it was found that the main reasons for murders-type of crime are financial straits, difficult family
background, and alcohol abuse. The following measures have been formulated to reduce homicide in Primorsky Kray.
Firstly, to reduce alcohol time-sell. Secondly is to promote a healthy lifestyle and to carry out prevention of too much
alcohol-using in the media, as well as to involve this region residents in a healthy lifestyle through the organization of
sports events.
Keywords: Correlation analysis, murders, murders prevention, Primorsky Kray, Russian Federation.
INTRODUCTION
Crime remains a big problem in the modern world. It is very important not only сatch the criminal but to prevent this
inhumanity act. It can be possible due to analyzing of conductive to the murder factors. Correlation analysis is one of the
most useful statistical methods in the social research. It allows to defy a problematic complex of cause-and-effect relations
[1-3]. Crimes as distinct social occurrences cannot be exceptions inappropriate for this method of statistical analysis. It is
necessary to mention that correlation analysis not only helps to identify the causes of any crime but also allows to establish
a connection with other social phenomena, as well as develop measures to counter the determination of crime [4-8].
The essence of the correlation analysis is to identify the dependence between the results of the investigation of
indicators of various factors, as well as investigation of the degree of their mutual influence. A correlation coefficient
characterizes the statistical dependence between several variables [1, 2, 9-13]. In criminology, quite a lot of material has
been accumulated in the field of the prevention of homicide [14-20] and its connection with alcoholism [21-25]. Under
the Criminal Code of the Russian Federation Article 105, murder is the voluntary infliction of death on another person.
Concerning criminological studies of violent crimes [6-12], the possible factors influencing the dynamics of homicides in
the mentioned region are economic (state of the economy), socio-demographic, criminal factors, as well as leisure and
education factors.
Not the last is the prevention actions to stop the crime spread. It had to care about alcohol consumption, got
better economy, and got the possibility to have different rest time holdin [20, 21, 25]. Must be noted, sport is a great
murder prevention factor. Moreover, sports events, especially international, get push infrastructure and economic
development due to tourism and giving the ability for the local small business. So the local economy becomes to get up
together with the citizens‟ incomes [20, 26]. Primorsky Kray has a great potential for tourism development, moreover, it
is one of the most promising territories for international ecological tourism. But this process is in its infancy and the
sports industry can help to make tourism and recreation business powerful [26-28]. But this can be trou without crime
control in the region [20].
METHODOLOGY
The correlation coefficient was founded by us, which characterizes the statistical dependence between variables. Was
estimates the dependence degree. [1, 2, 9-13]. So if we saw a lineal dependence, it can be calculated by the Pearson
correlation coefficient (formula 1).
2691JOURNAL OF CRITICAL REVIEWS
ISSN- 2394-5125 VOL 7, ISSUE 18, 2020
xy n
x y
r
( x) ( y )
xy 2 2
( x ) ( y
2 2
)
n n
rxy – the correlation coefficient;
x – observations on the first indicator;
y – observations on the second indicator;
n – the number of observations.
Formula 1. The detection of the correlation coefficient
The mentioned coefficient changes the scale from −1 to +1. It is supposed that if the coefficient is higher than
|0.7|, then the dependence is secure and tight, if it is not higher than |0.3|, then the dependence is weak; if it is from |0.3|
to |0.7|, then the dependence is middle. If the coefficient equals ±1, then the dependence is functional, if it equals 0, then
there is no linear dependence between indexes.
While using correlation analysis, it is necessary to consider a set of limitations.
The first limitation. If factors‟ variables are inextricably linked, it does not lead to cause-and-effect relations
between them. There is another possible factor that may influence others and might be a reason for changes in their
variables.
An intellectual experiment might serve as an example. If 1000 random people on the street are measured with an
intelligence index (IQ) and shoe size, then a close correlation may be found between them. However, it does not prove the
dependence between a person‟s intellectual development and their height. There are such people‟s features as gender and
age are the third factors here.
The second limitation. While calculating the appearance of the accidental correlation is possible. The
illustration of this limitation is English site Spurious Correlations, authors of which demonstrate rather funny
connections. In particular, the dependence between the US expenses on space and technology and the number of suicides
by hanging, strangulation (r=0.99); cheese consumption per capita and the number of people who died entangled in their
bedsheets (r=0.94); chicken consumption per capita and total import of crude oil in the USA (r=0.89), etc.
The third limitation. In studies with correlation analysis, it is desirable to do 12–15 observations for each
indicator. This restriction is not a severe problem with a broad base of data [3, 6, 7]. An algorithm of the correlation
analysis, used in this case, is the following:
1. Selection and grouping of indicators through statistical data.
2. Calculation of correlation coefficients within a group of indicators (formation of a correlation matrix).
3. Interpretation of the obtained exponents of the correlation coefficients.
With the current development of technology, the calculation of several tens or hundreds of correlation
coefficients does not demand any hard work. In particular, in this research, the capabilities of a Microsoft Excel
spreadsheet have been used [4, 5].
RESULTS AND DISCUSSION
We had a goal to envisage murder (at the example of the Primorsky Kray). Primorsky Kray was chosen because the results
of the feasibility study seemed to authors exciting, which pushed their careful scientific attention to this region.
First of all we had analyzed factors that influencing the dynamics of homicides in the mentioned region. So the
dissection the data from the official internet resources of the Federal Statistics Service of the Russian Federation, its
territorial authority for the Primorsky Kray and the Directorate of the Ministry of Internal Affairs of Russia for the
Primorsky Kray the following indicators characterized mentioned factors.
Economic factors: small or get down gross regional product (GRP) per capita; quantity of working of
enterprises and organizations; low average per capita income; the number of registered unemployed.
Socio-demographic factors: divorces; sale of vodka and alcoholic beverages; drug consuming.
Criminal factors: intentional infliction of grievous bodily harm; rape and attempted rape; robbery (and robbery
with violence); thefts; economic crime; drug trafficking crimes; crimes committed by minors or with their complicity.
Leisure and education factors: a number of spectators of theaters per 1000 people; number of sports facilities
(stadiums); the sports facilities number (flat sports facilities (playgrounds and fields); graduation of skilled workers and
employees from educational institutions of primary vocational education; graduation from educational institutions of
secondary vocational education; graduation of specialists from higher education institutions.
The results of correlation analysis expose a strong dependence on the murders from economic factors. In
particular, high correlation coefficient with GRP per capita (r = –0.94), average per income (r = –0.93), number of
registered unemployed (r = 0.9) (Table 1, 2).
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Table 1. Indicators of economic factors
Indicator 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
murders or
murderous 564 498 411 334 312 256 344 327 240 204 200 198
assaults
GRP per
130632 160417 187556 236978 281618 286057 297224 331845 371596 382587 376558 376467
capita
number of
enterprises
and 65876 61642 65532 65087 67747 67950 68010 68365 70873 70816 67353 65996
organization
s
average per
capita 12927 15486 17298 19160 25304 26500 27303 28339 32983 32446 33155 33993
income
number of
registered 32 37.6 25.7 20.7 20.7 18 15.7 13.8 16.5 14.7 10.7 8.9
unemployed
Table 2. Correlation matrix of indicators of economic factors
Line 1 Line 2 Line 3 Line 4 Line 5
Line 1 1.00
Line 2 -0.94 1.00
Line 3 -0.64 0.72 1.00
Line 4 -0.93 0.99 0.71 1.00
Line 5 0.90 -0.92 -0.66 -0.91 1.00
Tables 1 and 2 show the dependence between the number of registered unemployed and the number of
enterprises and organizations is not tight, as expected, but the middle (r = –0.64). This indicator leads to a presumption
of the high level of shadow employment and shadow economy in general, which is the criminal factor by itself.
We see the murders have a high proportion of crimes committed in the state of alcoholic (r = 0.91) or drug
intoxication (r = 0.93). A high correlation coefficient between homicides and the number of divorces (r = 0.76)
emphasizes the significant affection of family climate on this type of crime (Table 3-4).
Table 3. Indicators of socio-economic factors
Indicator 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
murders or
murderous 564 498 411 334 312 256 344 327 240 204 200 198
assaults
number of
10804 10530 11173 10016 10363 9900 10839 11145 9607 9254 9298 9212
divorces
sale of vodka
and
2683.4 2492.9 2306 2181.8 2077 2173.2 2222.5 2211.3 1495.9 1653.7 1484.4 1598.2
alcoholic
beverages
A quantity of
drug-addicts
registered in 9401 9182 8582 8237 7565 7132 6923 6652 6132 6016 6054 6031
medical
institutions
Table 4. Correlation matrix of indicators of socio-demographic factors
Line 1 Line 2 Line 3 Line 4
Line 1 1.00
Line 2 0.76 1.00
Line 3 0.91 0.82 1.00
Line 4 0.93 0.64 0.89 1.00
The dependence between various types of crimes is extremely interesting. The high rate of correlation is found
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between intentional infliction of grievous bodily harm (r = 0.92), robberies (r = 0.94), robberies with violence (r = 0.96),
thefts (r = 0.93), economic crimes (r = 0.9) crimes committed by minors or with their complicity (r = 0.98). It is more
likely that the same factors influence all types of these crimes, or participants of these crimes are people from the same
social group. Moreover, the validity of one presumption does not exclude the validity of the other (Table 5-6).
Table 5. Criminal factors
Indicator 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
murders or
murderous 564 498 411 334 312 256 344 327 240 204 200 198
assaults
intentional
infliction of
1036 979 851 845 816 858 901 765 683 680 652 635
grievous bodily
harm
rape and
120 80 71 54 88 102 103 88 50 45 53 50
attempted rape
robbery 7203 5310 4203 3322 3893 2629 2330 1535 1191 991 801 749
robbery with
906 729 633 494 425 452 408 329 255 186 167 145
violence
theft 36575 30845 25133 22538 21923 24125 24852 19786 19185 18570 18502 18430
economic crimes 5374 4863 4270 2228 1372 1116 1006 614 851 869 870 875
drug trafficking
6013 5608 5229 5563 4061 3856 4740 8027 6494 5082 5062 5037
crimes
crimes committed
by minors or with 3681 3229 2355 2083 1488 1590 1947 1576 1212 1146 1137 1125
their complicity
Table 6. Correlation matrix of indicators of criminal factors
Line 1 Line 2 Line 3 Line 4 Line 5 Line 6 Line 7 Line 8 Line 9
Line 1 1.00
Line 2 0.92 1.00
Line 3 0.65 0.79 1.00
Line 4 0.94 0.91 0.65 1.00
Line 5 0.96 0.95 0.67 0.98 1.00
Line 6 0.93 0.94 0.72 0.94 0.95 1.00
Line 7 0.90 0.78 0.39 0.91 0.92 0.88 1.00
Line 8 0.22 -0.04 -0.06 -0.04 0.05 0.00 0.07 1.00
Line 9 0.98 0.92 0.60 0.94 0.96 0.97 0.94 0.15 1.00
It also may be mentioned that the dependence on the crimes related to drug trafficking is weak (r = 0.22). It
seems that all niches and business roles of drug trafficking in the Primorsky Kray are occupied. Redistribution in spheres
of this criminal business and its roles is hardly expected [13].
Among leisure and education factors, the number of sports facilities (flat sports facilities (playgrounds and
fields)) (r = –0.47) and the number of spectators of theaters (r = –0.62) may be particularly emphasized. The dependence
of both factors is middle and reverse. That is why visits to theatres and doing sport may be supposedly an alternative to
drunk gatherings that, to some extent, may reduce the likelihood of domestic murders (Table 7-8).
Table 7. Leisure and education factors
Indicator 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
murders or
564 498 411 334 312 256 344 327 240 204 200 198
murderous assaults
number of spectators
of theaters (per 1000 155 165 176 183 188 193 205 248 273 205 215 200
people)
number of sport
42 45 44 31 26 27 30 28 29 26 25 27
facilities (stadiums)
number of sport
1596 1662 1722 1849 1880 1895 1903 1941 1953 1783 1699 1720
facilities
graduation of skilled
workers (primary 8200 8100 9000 8400 6600 7200 6200 5900 6100 2400 3000 2800
vocational education
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institutions)
graduation
(institutions
8800 8600 8600 7900 8000 6100 6300 6700 6300 6800 6300 5300
secondary vocational
education)
graduation (higher
education 16700 16600 17400 17000 15800 15500 15300 14300 15100 12100 11800 12000
institutions)
Table 8. Correlation matrix of indicators of leisure and education factors
Line 1 Line 2 Line 3 Line 4 Line 5 Line 6 Line 7
Line 1 1.00
Line 2 -0.62 1.00
Line 3 0.88 -0.59 1.00
Line 4 -0.47 0.70 -0.59 1.00
Line 5 0.77 -0.43 0.70 -0.02 1.00
Line 6 0.84 -0.64 0.78 -0.45 0.73 1.00
Line 7 0.76 -0.44 0.69 0.01 0.99 0.75 1.00
Sports sphere development has its effect not only on health but can help to get up the local social sphere (due to
new spots objects building together with the infrastructure). The other hand is the small business grows up because of
opportunities for local supply chains [26].
The strict dependence of education grades with murders and murderous assaults is hard to explain. There may be
an additional occasion or third factor here, which influences the dynamics of indicators as well as in investigating the
type of crimes.
To sum up, there is a hypothesis about a problematic complex of cause-and-effect links with the mutual influence
of various factors. In particular, low incomes, material disadvantages lead to equally unfortunate family circumstances
and narrow the leisure opportunities for some social groups for which drunk gatherings remains the most acceptable way
to spend time. As expected, unfortunate family circumstances cause aggression and push towards alcohol abuse.
Consequently, the main subjects of murders-maker or murderous assaults are alcohol addicted representatives of
low-income social groups.
CONCLUSSION
Correlation analysis results and thire interpretation show the following directions for the prevention of murders are
proposed.
1. Improving the material well-being of the inhabitants of Primorskiy Kray. It seems that the possibilities of the
regional authorities are narrowed since their work depends more on the characteristics of the socio-economic formation,
as well as on the global and national economic conditions (for example, fluctuations in oil prices) rather than on the
activities of state bodies of a constituent entity of the Russian Federation. For this reason, this area should be considered
as a long-term guideline, the achievement of which will be implemented only with favorable conditions.
2. Decreasing of alcohol consumption. First of all, in this case, it is necessary to limit the time to sell alcohol.
For instance, to introduce the days of “temperance” in a week (month) along with an alcohol sale for a limited time.
Undoubtedly, the most effective way to reduce alcohol consumption is to increase its price by increasing excise
taxes. However, under the Russian tax legislation, the regional authorities have no right to introduce this practice. The
Legislative Assembly of Primorsky Kray can only initiate legislation to amend the Tax Code of the Russian Federation,
but the opposition from the alcohol lobby should be taken into account here.
Secondly, to carry out propaganda of healthy lifestyle and prevention of alcoholism in the media, as well as to
involve residents of Primorskiy Kray in a healthy lifestyle through the organization of sports events and anti-alcohol
campaigns. It may promote an increase in the construction of sports facilities. Unfortunately, statistical data do not argue
for a steady tendency to increase the number of sports facilities in the Primorsky Kray. This looks very strange since the
state program of the Primorsky Kray “Development of Physical Culture and Sports of the Primorsky Kray” for
2013–2021 is currently in force. Under this program, one of the priorities of state policy in the sphere of physical culture
and sports is the creation of material and technical base in this region.
REFERENCES
1. Orlov, A.I. (2004). Applied statistics. Textbook. Moscow: Exam.
2. Utkina, V.B. (2012). Econometrics: Textbook, 2nd ed. Moscow: Dashkov and Co.
3. Gusev, A.N., Izmailov, Ch.A., Mikhalevskaya, M.B. (1998). Measurement in Psychology: A General Psychological
Workshop. 2nd ed. Moscow: Sense.
4. Luneev, V.V. (2010). Legal statistics. Moscow: Norma.
2695JOURNAL OF CRITICAL REVIEWS
ISSN- 2394-5125 VOL 7, ISSUE 18, 2020
5. Bajnova, M.S., Kozyrev, M.S., Petrov, A.V. (2016). Correlation analysis of state influence on certain aspects of the
Russian economy. Contemporary problems of economics, 182 (8), pp. 334-343.
6. Kudryavtseva, V.N., Eminova, V.E. (2009). Criminology: a textbook, 4th ed., Revised. and add. Moscow: Norma.
7. Dolgovoi, A.I. (2016). Criminology: Textbook for universities, 4th ed., Rev. and add. Moscow: Yur.Norma, SIC
INFRA-M.
8. Kurganov, S.I. (2017). Criminology. Textbook. Mounds. Moscow: Unity-Dana.
9. Kamaluddin, M.R., Md Shariff, N.S., Mat Saat, G.A. (2018). Mechanical profiles of murder and murderers: An
extensive review. Malaysian journal of pathology, 40 (1), pp. 1-10.
10. Pylkin, A., Stroganova, O., Sokolova, N., Pylkina, M. (2019). The Development of Information Technology and the
Problem of Identity. 2019 8th Mediterranean Conference on Embedded Computing, MECO 2019 – Proceedings 2019,
conference-paper. http://dx.doi.org/10.1109/MECO.2019.8760159
11. Quinney, R. (1973). Crime Control in Capitalist Society: A Critical Philosophy of Legal Orde. Issues in
Criminology, 8 (1), pp. 74-99.
12. Peter, E., Seidenbecher, S., Bogerts, B., Dobrowolny, H., Schöne, M. (2019). Mass murders in
Germany–classification of surviving offenders based on the examination of court files. Journal of Forensic Psychiatry and
Psychology, 30 (3), pp. 381-400.
13. Bychkova, A.M. (2018). Criminology of drug crime. General part: textbook. Allowance. BSU. The electron. text
data. Irkutsk: Publishing house of BSU.
14. Kuzmenkovv, A. (2019). Criminal anomie as a social problem. Sotsiologicheskie Issledovaniya, (1), pp. 96-105
15. Spelman, W.J. (2017). The Murder Mystery: Police Effectiveness and Homicide. Journal of Quantitative
Criminology, 33 (4), pp. 859-886.
16. Brookman, F. (2015). Killer decisions: The role of cognition, affect and „expertise‟ in homicide. Aggression and
Violent Behavior, 20, pp. 42-52.
17. Kizatova, M.Y., Medvedkov, Y.B., Shevtsov, A.A., Drannikov, A.V., Tlevlessova, D.A. (2017).
Experimental-Statistical Analysis and Multifactorial Process Optimization of the Crust from Melon Pulp Separation
Process. Journal of Engineering and Applied Sciences, 12, pp. 1762-1771.
http://dx.doi.org/10.36478/jeasci.2017.1762.1771
18. Dennington, L. (2012). Bullying, suicide and homicide: Understanding, assessing and preventing threats to self.
Journal of mental health, 21 (6), pp. 614-616.
19. Cusson M (2001). Studying and preventing homicide. Canadian journal of criminology-revue canadienne de
criminology, 43 (3), pp. 421-423.
20. Gilling, D. (2019). Thinking about Crime Prevention. Policing-a journal of policy and practice, 13 (3), pp. 263-270.
21. Bakhteev, S.S. (2014). Problems of alcoholism and crime. Law and order: history, theory, practice, 1 (2), pp.
101-103.
22. Zhizhina, I.V. (2017). Prevention of drunkenness and alcoholism of minors. Improving the activities of law
enforcement agencies taking into account modern realities. Materials of the international scientific-practical conference.
Aktobe, pp. 105-108.
23. Nemtsov, A.V. (2009). Alcoholic History of Russia: The Newest Period. Moscow: MNIIIP, LIBROKOM Book
House.
24. Nemtsov, A.V. (2016). Mortality from alcoholism in Russia in 2004-2014. Issues of narcology, 5-6, pp. 35-54.
25. Cheremisina, N.V., Ivliev, M.I., Talalaev, D.D. (2014). Alcoholism: the global problem of modern Russia.
Socio-economic phenomena and processes, 9 (11), pp. 163-167.
26. Dedusenko, E.A. (2019). Sporting mega-events as catalysts for sustainability and tourism development in Russia.
Journal of Environmental Management and Tourism, 10 (2), pp. 346-353. http://dx.doi.org/10.14505/jemt.v10.2(34).08
27. Dedusenko, E. A. (2017). Hospitality investment environment in Russia //Journal of Environmental Management
and Tourism, 8(2), 291-300. DOI: 10.14505/jemt.v8.2(18).02
28. Martyshenko, N.S. (2012). Ecological tourism is the most important area of development international tourism in
the Primorsky Territory. Russian Journal of Ecotourism, 3, 34-38.
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