Revista
de la
Universidad
del Zulia
Fundada en 1947
por el Dr. Jesús Enrique Lossada
DEPÓSITO LEGAL ZU2020000153
Esta publicación científica en formato digital
es continuidad de la revista impresa
ISSN 0041-8811
E-ISSN 2665-0428
Ciencias
Sociales
y Arte
Año 13 N° 38
Septiembre - Diciembre 2022
Tercera Época
Maracaibo-Venezuela
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DOI: http://dx.doi.org/10.46925//rdluz.38.08
116
Criminological research on statistics concerning judicial
examination of criminal proceedings using cluster analysis
algorithms
Andriy Bohatyrov
*
Kateryna Buriak**
Dmytro Kolodchyn***
Olha Pavliukh****
Maryna Larchenko
*****
ABSTRACT
The purpose of the research is to reveal the essence of criminological research on statistics
concerning judicial examination of criminal proceedings using cluster analysis algorithms. Main
content. The article demonstrates the use of the cluster analysis method in the process of
criminological research to identify regions of Ukraine with abnormal indexes of legal statistics.
Methods of the Data Mining module based on STATISTICA (StatSoft) were applied in order to
identify atypical observations. Methodology: The dialectical method of scientific knowledge is the
methodological basis of the research. Through application of this method considered was the essence
of judicial examination of criminal proceedings using cluster analysis algorithms (development,
relationship and mutual influence of these aspects). Results. Quantitative indexes of the work of
appellate courts were analyzed in absolute numbers, a graphic presentation and visualization of
individual stages of the conducted analysis was performed in order to demonstrate the method, ways
of practical application of the obtained results of cluster analysis were outlined in order to improve
the activity of courts in criminal proceedings.
KEY WORDS: Criminology, judicial examination, cluster analysis, appeal courts, criminal
proceedings.
* Associate professor of the Department of criminal and administrative law disciplines of the International economic-
humanitarian university named after academician Stepan Demyanchuk, Ukraine. ORCID: https://orcid.org/00000003-
2707-8978. E-mail: bohatyrov.prosecutor@gmail.com
** Associate professor at the Department of Entrepreneurship, Organization of Production and Theoretical and Applied
economics, Ukrainian State University of Chemical Technology, Dnipro, Ukraine. ORCID: https://orcid.org/0000-0001-
6265-9706. Email: buriak_kate@ukr.net
*** Candidate of Juridical Sciences, Prosecutor of the second department of procedural management in criminal
proceedings of investigators of the territorial administration, State Bureau of Investigations of the Kyiv City Prosecutor's
Office, Ukraine. ORCID ID: https://orcid.org/0000-0002-0820-4409. E-mail: kolodchin92@gmail.com
**** Associate Professor of the Department of Criminal Justice Educational and Scientific Institute of Law, State Tax
University, Ukraine. ORCID ID: https://orcid.org/0000-0002-7850-8977. E-mail: pavlyuh@gmail.com
***** Associate Professor, Chair of Political Science, Law and Philosophy, Nizhyn Mykola Gogol State University, Ph.D.
in Law, Associate Professor, Nizhyn, Ukraine. ORCID ID: https://orcid.org/0000-0002-2643-980X. E-
mail: urlinka2006@gmail.com
Recibido: 13/04/2022 Aceptado: 02/06/2022
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Investigación criminológica sobre estadísticas relativas al examen
judicial de procesos penales utilizando algoritmos de análisis de
conjuntos
RESUMEN
El propósito de la investigación es revelar la esencia de la investigación criminológica sobre
estadísticas relacionadas con el examen judicial de procesos penales utilizando algoritmos de
análisis de conglomerados. Contenido principal. El artículo demuestra el uso del método de
análisis de conglomerados en el proceso de investigación criminológica para identificar
regiones de Ucrania con índices anormales de estadísticas legales. Se aplicaron métodos del
módulo de Minería de Datos basados en STATISTICA (StatSoft) para identificar
observaciones atípicas. Metodología: El método dialéctico del conocimiento científico es la
base metodológica de la investigación. Mediante la aplicación de este método se consideró la
esencia del examen judicial de los procesos penales utilizando algoritmos de análisis de
conglomerados (desarrollo, relación e influencia mutua de estos aspectos). Resultados. Los
índices cuantitativos del trabajo de los tribunales de apelación se analizaron en números
absolutos, se realizó una presentación gráfica y visualización de las etapas individuales del
análisis realizado para demostrar el método, se describieron formas de aplicación práctica de
los resultados obtenidos del análisis de conglomerados para mejorar la actividad de los
tribunales en los procesos penales.
PALABRAS CLAVE: Criminología, examen judicial, análisis de conglomerados, tribunales de
apelación, proceso penal.
Introduction
Throughout the world, activities of courts are an important component of countering
and preventing crime. There are established mechanisms for implementing stages of the
judicial examination of criminal proceedings. In particular, appeal in criminal proceedings
plays an important role and it is one of the guarantees of observance of the rights of both the
defendant and other participants in the judicial process.
Ukraine has been reforming the judicial system for many years. Implementation of this
reform involves improvement of both the legislation on the judicial system and norms of
procedural legislation regulating the administration of justice.
The strategy of judicial reform in May 2015 provided for consolidation of judicial
districts; as it was declared, this consolidation should have become one of the ways to
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improve efficiency of courts. In December 2017, a decree was issued which eliminated 27
appeal courts. Instead, 26 “new” courts were created. Although in fact, only their renaming
took place. Only the appeal courts of the city of Kyiv and the Kyiv region were united into
one - the Kyiv Court of Appeal. However, the courts started their work in the new format
much later.
It should be noted that different regions of Ukraine are characterized by different
social and economic indexes, demographic characteristics, local traditions as well as by
different ratios of certain types of crime. Criminological characteristics of a separate region
of Ukraine are aimed at forming an improved analytical concept of judicial reform, as well as
(in a broader context) the concept of law enforcement reform and administrative reform in
Ukraine.
The purpose of the research is to reveal the essence of criminological research on
statistics concerning judicial examination of criminal proceedings using cluster analysis
algorithms.
1. Literature review
There are many scientific studies on formation and development of appeal courts in
Ukraine. In particular, we should highlight the dissertation work by V.M. Koval “Appeal
Courts in Ukraine: Formation and Development” (2004). The author emphasizes that
formation of a full-fledged judicial power in Ukraine requires adoption of additional
organizational and legal measures. At the same time, functions of the judicial power include
delivery of justice and preventing crime, and the control activity of courts is carried out
within the scope of the function of justice (Koval, 2004).
L.M. Moskvich analyzed international standards of effective justice with
simultaneous projection on the Ukrainian judicial system (2009). In particular, these
standards include: 1) actual (real) access of a person to institutions of the judicial system; 2)
openness of the judicial process; 3) reasonable terms of the judicial process; 4) due legal
procedure; 5) legally established independence and impartiality of courts (Moskvych, 2009).
Analysis of international scientific publications also provides an opportunity to get an
idea about effectiveness of legal procedure of criminal cases, in particular, in countries of the
European Union and in the United States. Such efficiency mainly based on optimal terms of
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legal procedure, terms of appeals and execution of judgments (decisions) of courts (Leheza
ect., 2021).
Thus, when analyzing activities of appeal courts in England and the USA Burton
Atkins notes that despite the research on appeal courts conducted in the last few decades,
little attention has been paid to assessing the role of appeal courts at the international level.
The author considers in particular appellate courts in England, in particular the English
Court of Appeal and the US courts of appeals (Leheza ect., 2021).
The American and English systems are proposed by the author as alternative models
of interaction between courts and their political systems in Western common law regimes.
Through highlighting the work of intermediate courts of appeals and their interaction with
the respective courts of the last resort, the data presented show greater commonality than
divergence in the work of the two judicial hierarchies (Burton, 1990).
2. Materials and methods
The research is based on the work of foreign and Ukrainian researchers on revealing
the essence of the criminological research on statistics concerning judicial examination of
criminal proceedings using cluster analysis algorithms.
The role of statistics concerning judicial examination of criminal proceedings using
cluster analysis algorithms has been determined with the help of the gnoseological method.
Constituent elements of the judicial examination of criminal proceedings using cluster
analysis algorithms were investigated by means of system-structural method. Structural-
logical method was used to designate the main directions in optimization of criminal
proceedings using cluster analysis algorithms.
3. Results and discussion
The data structure is represented as 27 indexes which were selected from the public
records of the State Judicial Administration of Ukraine for 2019 and 2020. The figures for
2021 and the beginning of 2022 are anomalous due to the quarantine and military aggression
of the Russian Federation against Ukraine. The rows of the formed table are presented for
the regions of Ukraine in the amount of 25 observations. Data from Donetsk and Luhansk
regions represent only the territory controlled by Ukraine. Data from the Autonomous
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Republic of Crimea and the city of Simferopol is absent due to temporary occupation of the
peninsula (Fediuk, 2016).
Variables of the analysis include the following parameters:
1) Number of revised sentences (total);
2) Number of sentences left unchanged;
3) Number of changed sentences;
4) Number of changed sentences with mitigation of the imposed punishment;
5) Number of changed sentences with a change in the legal qualification of the
criminal offense to a less serious one;
6) Number of sentences with changed amounts to be recovered;
7) Number of canceled sentences (total);
8) Number of canceled absolutory sentences;
9) Number of sentences canceled on the basis of an agreement;
10) Number of sentences canceled due to incomplete judicial examination;
11) Number of verdicts canceled due to inconsistency of the court’s conclusions with
the actual circumstances;
12) Number of sentences canceled due to a significant violation of the requirements of
the criminal procedural legislation;
13) Number of sentences canceled due to an incorrect application of the law on
criminal responsibility;
14) Number of sentences canceled due to an inconsistency of the prescribed
punishment with the severity of the respective criminal offense and the personality of the
accused;
15) Number of canceled sentences with appointment of a new trial (total);
16) Number of canceled sentences with appointment of a new trial due to absence of
a defense attorney during the trial;
17) Number of canceled sentences with appointment of a new trial due to absence of
the victim during the court proceedings;
18) Number of canceled sentences with appointment of a new trial due to absence of
a court session log or a technical information carrier;
19) Number of canceled sentences with closure of the criminal proceedings (total);
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20) Number of canceled sentences with closure of the criminal proceedings due to
insufficient evidence to prove the person’s guilt;
21) Number of canceled sentences with the closure of the criminal proceedings due to
the death of the suspect (accused);
22) Number of canceled sentences with the closure of the criminal proceedings due to
relief of the accused person from criminal responsibility;
23) Number of canceled sentences with adoption of a new one (total);
(24) Number of canceled sentences with adoption of a new one due to the need to
apply the law on a more serious criminal offense;
25) Number of canceled sentences with adoption of a new one due to the need to apply
a more severe punishment;
26) Number of canceled sentences with adoption of a new one due to the abolition of
the unjustified absolutory sentence;
27) Number of canceled sentences with adoption of a new one due to an unlawful
relief of the accused from enduring his/her punishment.
A fragment of the formed database is presented in tables 1, 2.
If we compare the data of 2019 and 2020, it is obvious that in 2020 these data reflect
the situation worse than the respective data for 2019 due to the Covid-19 pandemic and
mostly quarantine restrictions which also affected the work of judicial institutions, and in
different regions such an impact could differ significantly (sometimes courts were almost out
of operation for a long period of time) Also, taking into account the fact that during the
period 2019-2021 there were no significant changes to the procedural legislation regulating
operation of appeal courts, the data for 2019 most plausibly characterize the situation
concerning consideration of cases by the appeal authority in various regions of Ukraine.
In relation to the methods chosen by us for performing the analysis, the following
should be said. In general, most of the methods used to detect atypical observations solve the
problem of classification. They require the presence of objects in advance known to belong
to this or that class (of the two classes available). Such methods belong to the class of
supervised learning.
Our task consists in the need to identify atypical observations, without obtaining
information about their belonging to this or that class. It is clustering that belongs to the
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class of unsupervised learning. The problem of clustering is solved at the initial stages of the
research. Solution of this problem helps to better understand the data itself and its nature.
Table 1: Official data of the State Judicial Administration regarding activities of appeal courts
of Ukraine in 2019
Regions of Ukraine
Var 1
Var 2
Var 3
Var 7
Var 15
Var 19
Var 23
Vinnytsia region
807.00
271.00
316.00
220.00
90.00
20.00
109.00
Volyn region
344.00
173.00
82.00
89.00
42.00
5.00
41.00
Dnipropetrovsk
region
1832.00
613.00
489.00
730.00
347.00
45.00
331.00
Donetsk region
863.00
371.00
240.00
252.00
113.00
27.00
110.00
Zhytomyr region
438.00
222.00
63.00
153.00
90.00
3.00
59.00
Zakarpattia region
160.00
54.00
42.00
64.00
50.00
9.00
5.00
Zaporizhia region
936.00
538.00
120.00
278.00
140.00
29.00
109.00
Ivano-Frankivsk
region
291.00
147.00
46.00
98.00
68.00
13.00
17.00
Kyiv region
485.00
211.00
116.00
158.00
80.00
15.00
60.00
Kirovohrad region
372.00
162.00
86.00
124.00
75.00
7.00
41.00
Luhansk region
184.00
63.00
66.00
55.00
30.00
6.00
17.00
Lviv region
659.00
300.00
146.00
213.00
117.00
38.00
54.00
М. Kyiv (city)
1083.00
676.00
178.00
229.00
87.00
31.00
107.00
Mykolayiv region
450.00
220.00
76.00
154.00
107.00
8.00
35.00
Odesa region
502.00
195.00
145.00
162.00
90.00
14.00
49.00
Poltava region
559.00
376.00
96.00
87.00
14.00
16.00
57.00
Rivne region
298.00
164.00
71.00
63.00
35.00
3.00
23.00
Sumy region
389.00
178.00
70.00
141.00
109.00
6.00
26.00
Ternopil region
158.00
83.00
29.00
46.00
29.00
10.00
7.00
Kharkiv region
1023.00
508.00
236.00
279.00
145.00
41.00
93.00
Kherson region
556.00
204.00
145.00
207.00
119.00
11.00
77.00
Khmelnytskyi region
437.00
206.00
77.00
154.00
65.00
10.00
79.00
Cherkasy region
158.00
94.00
19.00
45.00
34.00
2.00
9.00
Chernivtsi region
311.00
166.00
56.00
89.00
18.00
17.00
54.00
Chernihiv region
591.00
311.00
90.00
190.00
77.00
7.00
104.00
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Table 2: Official data of the State Judicial Administration regarding activities of appeal courts
of Ukraine in 2020
Regions of Ukraine
Var 1
Var 2
Var 3
Var 7
Var 15
Var 19
Var 23
Vinnytsia region
580.00
251.00
184.00
145.00
75.00
9.00
61.00
Volyn region
230.00
132.00
62.00
36.00
12.00
4.00
19.00
Dnipropetrovsk city
1428.00
576.00
347.00
505.00
285.00
21.00
198.00
Donetsk region
676.00
299.00
177.00
200.00
81.00
14.00
102.00
Zhytomyr region
310.00
158.00
41.00
111.00
65.00
8.00
38.00
Zakarpattia region
89.00
35.00
16.00
38.00
29.00
6.00
3.00
Zaporizhia region
753.00
433.00
109.00
211.00
107.00
20.00
84.00
Ivano-Frankivsk
region
239.00
113.00
52.00
74.00
51.00
8.00
15.00
Kyiv region
361.00
199.00
66.00
96.00
37.00
12.00
47.00
Kirovohrad region
357.00
185.00
71.00
101.00
65.00
4.00
31.00
Luhansk region
179.00
92.00
53.00
34.00
11.00
2.00
21.00
Lviv region
588.00
333.00
103.00
152.00
81.00
26.00
44.00
М. Kyiv
739.00
424.00
99.00
216.00
84.00
30.00
91.00
Mykolayiv region
389.00
215.00
50.00
124.00
78.00
8.00
36.00
Odesa region
678.00
299.00
176.00
203.00
104.00
28.00
68.00
Poltava region
431.00
279.00
74.00
78.00
16.00
19.00
40.00
Rivne region
226.00
119.00
33.00
74.00
57.00
6.00
11.00
Sumy region
241.00
137.00
45.00
59.00
41.00
10.00
8.00
Ternopil region
155.00
93.00
25.00
37.00
24.00
1.00
12.00
Kharkiv region
1208.00
582.00
266.00
360.00
192.00
76.00
89.00
Kherson region
494.00
176.00
122.00
196.00
97.00
16.00
80.00
Khmelnytskyi region
275.00
182.00
36.00
57.00
13.00
10.00
33.00
Cherkasy region
301.00
171.00
48.00
82.00
43.00
8.00
31.00
Chernivtsi region
279.00
159.00
52.00
68.00
16.00
10.00
42.00
Chernihiv region
499.00
303.00
83.00
113.00
46.00
6.00
61.00
Cluster analysis gives an opportunity to divide objects into clusters not one by one
feature, but by a whole set of features, automatically determining the optimal number of
clusters.
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To implement the clustering module, it is necessary that the variables should have the
same variability (range). For this purpose we will use the standardization procedure in Data
Mining.
After standardization we get a slightly modified table and it is for these data that we
will use the K-means clustering algorithm available in the module. This algorithm divides a
subset of the vector space elements into a known number of clusters k. The algorithm’s effect
is that it seeks to minimize the root mean square deviation at the points of each cluster. The
main idea is that at each iteration recalculated is the mass center for each cluster obtained in
the previous step; then the vectors are divided into clusters again based on the fact which of
the new centers is closer according to the chosen metric. The algorithm ends when there is
no change in centers of clusters at a certain iteration.
The results of clustering are presented in Table 3.
Table 3: Summary for k-means clustering (standardized)
Summary for k-means clustering (Standardize)
Number of clusters: 3
Total number of training cases: 25
Algorithm
Distance method
Initial centers
MD casewise deletion
Cross-validation
Testing sample
Training cases
k-Means
Euclidean distances
Maximize initial distance
Yes
10 folds
0
25
Averages in each cluster, obtained as a result of the analysis, are brought by the
program to the table. Its fragment is shown in the form of Table 4.
The rows of Table 4 contain cluster numbers. Number of columns in the table
obtained in STATISTICA is 27 columns (according to number of variables involved in the
analysis) The fragment presented in Table 4 contains only 7 main variables of the analysis.
The last two columns of the table indicate number of observations and their percentage in
each cluster.
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Table 4: Centroids for k-means clustering (standardized). Number of clusters: 3
Total number of observations: 25
Clusters
Number of
revised
sentences
(average)
Number of
unchanged
sentences
(average)
Number of
changed
sentences
(average)
Number of
canceled
sentences
(average)
Number of
canceled
sentences
with a new
trial
appointed
(average)
Number of
canceled
sentences
with
criminal
proceedings
closed
(average)
Number of
canceled
Sentences
with a new
sentence
adopted
(average)
1
0.90257
1.09402
0.78722
0.54047
0.43278
1.22715
0.46223
2
-0.48927
-0.48135
-0.45708
-0.40670
-0.36379
-0.53969
-0.37952
3
3.39149
2.10016
3.50409
4.08315
3.95152
2.35150
4.05801
Continuation of Table 4
Clusters
Number of
observations
Percentage (%)
1
6
24.000
2
18
72.000
3
1
4.000
The following clusters were obtained:
Cluster 1: Appeal courts in regions of Ukraine with annual average number of revised
sentences – 895, with average number of sentences left unchanged – 444, average number of
changed sentences – 206, average number of canceled sentences – 245. In total, 6 regions, the
share in the total volume of observations is 24%.
Cluster 2: Appeal courts in regions of Ukraine with annual average number of revised
sentences – 371, with average number of sentences left unchanged – 179, average number of
changed sentences – 76, average number of canceled sentences – 116. In total, 18 regions, the
share in the total volume of observations is 72%.
Cluster 3: Appeal court in the region of Ukraine with annual average number of revised
sentences – 1832, with average number of sentences left unchanged – 613, average number of
changed sentences – 489, average number of canceled benefits – 730. In total, 1 region, the
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share in the total volume of observations is 4%. Regions divided into clusters are shown in
Table 5.
Table 5: Regions of Ukraine, grouped by clusters
Cluster 1
Cluster 2
Cluster 3
Vinnytsia,
Donetsk,
Zaporizhzhya,
Lviv, Kharkiv
regions, Kyiv (city)
Volyn, Zhytomyr, Zakarpattia, Ivano-Frankivsk,
Kyiv, Kirovograd, Lugansk, Nikolaev, Odessa,
Poltava, Rivne, Sumy, Ternopil, Kherson,
Khmelnytsky, Cherkasy, Chernivtsi, Chernihiv
regions
Dnipropetrovsk
region,
The distances between clusters (Cluster distances) are shown in Table 6.
Table 6: Standardized distance between centroids of k-means clustering (standardized)
Standardized distance between centroids of k-means clustering (Standardize)
Number of clusters: 3
Cluster 1
Cluster 2
Cluster 3
Cluster 1
Cluster 2
Cluster 3
0,000000
1,561369
2,926194
1,561369
0,000000
3,917100
2,926194
3,917100
0,000000
In cluster 1, number of changed sentences is average and number of canceled sentences
is also average, but number of sentences left unchanged is rather large.
In cluster 2, number of changed sentences is the smallest as well as number of canceled
sentences, but at the same time number of sentences left unchanged is also the smallest.
Cluster 3 includes an appeal court of only one region of Ukraine (Dnipropetrovsk
region). It is characterized by a large number of changed sentences, a large number of
canceled sentences and a large number of sentences left unchanged.
In order to identify atypical observations (i.e. appeal courts in regions where results
of the work performed differ significantly from the average results in the corresponding
cluster) it is necessary to compare the average value of each variable in each cluster (data
from Table 4) with the value of each variable separately for each region (data from Table 1).
In our research there is no possibility for such comparison in cluster 3, because it
includes only one region of Ukraine. Therefore, further comparisons will apply only to
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clusters 1 and 2. We will also limit ourselves to comparing the indexes of the following
variables: Var2, Var3, Var7 (variables being the most informative in this research).
As a result of the comparison performed, anomalous observations (that is, those that
do not fit into the general trend of the respective cluster) were revealed in three regions of
Ukraine. Operation indexes of the appeal courts in these regions have significant deviations
of at least one significant index in the direction of increase and at least one significant index
in the direction of decrease, compared to the average index for the cluster (Leheza ect., 2020).
It is about appeal courts of Vinnytsia, Zaporizhzhya and Poltava regions. All the three
mentioned regions have a rich and diverse investment and resource potential and broad-
based economy, ranging from agriculture to food processing, mining, construction, machine-
building and tourism. They are located at the crossroads of transport routes; they are
important transit points for passenger and cargo transportation, as well as for supply of
electricity, oil and gas. Another feature of these regions consists in availability of networks
of higher and professional-technical educational institutions; and this fact gives an
opportunity to have a well-trained competitive labor force. The unemployment rate in
general does not differ significantly. Zaporizhzhya region is a developed industrial region and
it significantly dominates in terms of number of active enterprises and the level of wages, but
is inferior to Poltava region in terms of the gross regional product (Fediuk, 2016).
At the next stage, crime and justice indexes of 2019 in the regions were analyzed
separately from social and economic indexes. It was the following information: Number of
crimes detected; number of persons found to commit crimes; number of persons found to
have committed crimes previously; number of persons convicted of crimes; number of
convicted persons whose court decisions have come into legal force (Verner, 2020).
All the above-mentioned predictors have been converted into relative indexes. Thus,
the index “number of crimes detected” as a percentage of the permanent population of the
region (crime rate per 100 persons of the permanent population of the region) allows to
separate Vinnytsia region – 0.77; Zaporizhzhia region – 1.35; Poltava region – 1.39. That is,
only Zaporizhzhia and Poltava regions differ significantly by the noted index, which is higher
than the average value for Ukraine – 1.07. Vinnytsia region, on the contrary, has a low crime
rate.
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Based on number of identified persons who committed crimes we have the following
coefficients: Vinnytsia region – 0.23; Zaporizhzhya region – 0.36; Poltava region – 0.33; at the
average Ukrainian value – 0.27. This trend is quite logically in line with the previous one.
The index “number of persons found to have committed crimes previously” was
converted into a relative index by calculating number of such persons per 100 convicted in
2019 by region. The following data was obtained: Vinnytsia region – 29.9; Zaporizhzhya
region – 42.4; Poltava region – 45.9. The average index in Ukraine is 30.83.
The index “number of convicted persons whose court decisions have come into legal
force” was converted into a relative one by means of similar actions: number of such persons
per 100 convicted persons in the region in 2019 was calculated. The obtained information
comes down to the following: Vinnytsia region – 78.4 (and this time it is a noticeably higher
index than in any other region of Ukraine); Zaporizhzhya region – 60.6; Poltava region – 75.3.
The average index of this kind in Ukraine is 64.0. That is, at this stage of the research exactly
Vinnitsa and Poltava regions have the highest analysis indexes (Leheza ect., 2020).
Based on the systematic analysis of the results of our research, we can state that appeal
courts of three regions of Ukraine (namely: Vinnytsia, Zaporizhzhya and Poltava regions)
have real differences in results of their work during the consideration of criminal
proceedings.
Conclusions
Thus, we conducted an analysis of raw data on activities of appeal courts in 25 regions
of Ukraine. With the help of special clustering algorithms, 3 atypical observations were
revealed; they are presented in statistical reports on the revision of court decisions by appeal
courts of Vinnytsia, Zaporizhzhia, and Poltava regions. Data obtained during the research
indicates the existing need for further reform of general jurisdiction courts in Ukraine as well
as the need to pay special attention to activities of judicial institutions located in the named
regions of Ukraine. It is definitely necessary to conduct an additional research of causes of
atypical statistical data, which, in turn, may lead to a possible revision of personnel policy in
the judicial branch of government in the above regions. The revealed discrepancies should be
brought to the attention of the Supreme Court; according to the law this court must: to carry
out analysis of judicial statistics and generalization of judicial practice, ensure equal
application of law by courts of various specializations in the order and manner determined
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by procedural law (Art. 38 of the Law of Ukraine “On judicial System and Status of Judges”
(Law of Ukraine, 2010).
References
Burton, Atkins (1990). Interventions and Power in Judicial Hierarchies: Appellate Courts in
England and the United States, Law & Society Review, Vol. 24, No. 1. P. 71-104. DOI:
10.2307/3053787
Fediuk, Vasyl (2016). Analysis of the state of investment activity in Vinnytsia, Zaporizhzhya,
Ivano-Frankivsk and Poltava regions and practical recommendations for improving the
investment environment" prepared as part of the international technical assistance project
"Partnership for Urban Development" (Project PROMIS): Report, Kyiv, Ukraine.
Koval, Volodymyr Mykolaiovych (2004). Appellate courts in Ukraine: formation and
development. Abstract of the dissertation of the Doctor of Jurisprudence, Odesa, Ukraine.
Law of Ukraine (2010). On the judicial system and the status of judges: Law of Ukraine dated
07.07.2010 No. 2453-VI, . Information of the Verkhovna Rada of Ukraine. Ukraine.
Leheza, Yevhen; Tiutchenko, Svitlana; Stanina, Olha; Shatrava, Serhii; Rezanov, Serhii (2021).
Uso y protección del suelo: regulación legal y experiencia extranjera. Revista De La Universidad Del
Zulia, 12(33), P.70-81. DOI: https://doi.org/10.46925//rdluz.33.06
Leheza, Yevhen; Dorokhina, Yuliia; Shamara, Oleksandr; Miroshnychenko, Serhii; Moroz, Vita
(2021). Citizens ‘participation in the fight against criminal offences: political and legal aspects.
Cuestiones Políticas, 39(69), P. 212-224. DOI: https://doi.org/10.46398/cuestpol.3969.12
Leheza, Yevhen; Filipenko, Tatiana; Sokolenko, Olha; Darahan, Valerii; Kucherenko, Oleksii
(2020). Ensuring human rights in ukraine: problematic issues and ways of their solution in the
social and legal sphere. Cuestiones políticas. Vol. 37 №º 64 (enero-junio 2020). P. 123-136.
DOI: https://doi.org/10.46398/cuestpol.3764.10
Leheza, Yevhen; Savielieva, Maryna; Dzhafarova, Olena (2018). Structural and legal analysis of
scientific activity regulation in developed countries. Baltic journal of economic studies. 4(3), P. 147-
157. DOI: https://doi.org/10.30525/2256-0742/2018-4-3-147-157
Moskvych Lidiia Mykolaivna (2009). International standards of the effectiveness of the judicial
system and their importance during the judicial reform in Ukraine, Bulletin of the Academy of
Advocacy of Ukraine, 2009, No. 2(15), pp. 151-156.
Osypova, Inna (2020). Self-assessment by Ukrainian households of their income level.
(according to the data of a sample survey of households in January 2020). Statistical collection.
State Statistics Service of Ukraine, Kyiv, Ukraine.
Werner, Ihor (2020). Statistical collection "Regions of Ukraine" 2019. Part I. State Statistics
Service of Ukraine, Kyiv, Ukraine.
