Clases Controladoras (CC)

The same procedure as the one followed to estimate the median point of network graph for sector p38 is applied for the estimation of the median point of sector p38. The median point of the incident network for sector p38 is located on (39.3671358, 22.9502072). In Figure 3.7.3.2-1 the incident points based on the historical data from 2010 until 2017 and the median point of sector p38 are depicted.

Figure 3.7.3.2-1: Incidents points and median point of sector p38

By applying the same procedure to each city sector and especially to those which have high volume of criminality the median points, which minimize the average distance to or from the incidents occurred in the sector based on the available historical data, can be estimated.

p38

4. CONCLUSIONS

The overarching goal of the work presented in this thesis is to study the data, provided by the Police Department of Volos (PDV), which refer to thefts as criminal offences, in the city of Volos, capital of the district of Magnesia, over the period 2010 - 2017 and to export results about the most common type of offense and the number of offenses that occurred every year, every month, every weekday, every hour and every important time period and day during the year.

For this purpose statistical experiments have been carried out and have been developed comparative charts, so as to compare the searches results and to come to conclusions. For the time period studied, the majority of offenses refer to Burglaries (43%) and Motorcycle Robberies (46%), with the later to be slightly more, while the Car Robberies refer to 11% of the total offenses. Α general reduction of offenses was observed with only two years, the year 2011 and 2014, to show an increase over the previous year. From 2015 onwards, we have significant reduction of offenses with 2017 showing a 54.87% decrease over 2010. The highest reduction rate between the years has been observed in 2015. In fact, in 2015, there were 31.46% less offenses than in 2014. The total of offenses does not show any particular difference between the months. In fact the totals show a slight divergence of 1% and 2%. The months of September and November correspond to the lowest total of offenses, while the months in which the most offenses were committed are the months July, August, May, October, January and December.

From the above we infer that offenses are prone to occur during the summer months, where the schools are closed and most of the people leave for summer vacations, and the months that include important days such as Christmas. Statistical experiments related to those important days and specific time periods saw that the day with the display of most offenses is Tuesday. Then follow without much difference the day of Friday sharing the same rate with Wednesday, the day

that the most offenses occurred mainly in the afternoon and evening hours. In particular, 22:00 hours seems to have the highest percentage of offenses occurrence, while 04:00 seems to have the lowest one. Also, the 12:00 turns out to be in the highest risk after the 22:00.

As for the specific weeks of the year, it appears that the most offenses occurred during Easter Week. Easter is usually celebrated in Greece in May or April, according to the Orthodox calendar.

Furthermore by examining and comparing the important days of the year it is concluded that the majority of offenses took place in the 1^st of January, New Year's Day , and then in the 15^th of August, Celebration of Virgin Mary (a great religious feast for the country).

In regard to the location of incidents, it turned out that most of the incidents occurred nearby the main streets while they spread throughout the city. All the results are expressed in the forms of charts and are presented in the figures below.

Figure 4.1: The most common type of offense over the period 2010-2017

Figure 4.2: The month with most offenses over the period 2010-2017

Figure 4.3: The year with most offenses over the period 2010-2017

Figure 4.4: The weekday with most offenses over the period 2010-2017

Figure 4.5: The hour with most offenses over the period 2010-2017

Figure 4.6: The specific week of the year with most offenses over the period 2010-2017

Figure 4.7: The important day of the year with most offenses over the period 2010-2017

It should be noted that the occurrence of offenses is based on many factors, both social and demographic. According to the discussion we had with the police we were informed that in the city of Volos the people related to thefts are known to the police. Therefore, their arrest has the effect of reducing offenses, whereas when they are left free they are again active and contribute to the increase of offenses. Ιt is a fact that there are specific criminal gangs operating throughout the country and that if they are caught, there is a fall in the offenses for the following months. When important police successes are published, then a general fall of offenses is observed as the occurrence of offenses for the next period. In particular if the penalty is great then the guilts do not act for as long as they are imprisoned and those who are not arrested yet, abstain from committing offenses for the next period.

The Security Department of Volos, in order to be effective and reduce the offenses applied a predictive model based on the observation of offenses. Specifically if there are about twenty offenses with the same features, that is, a common day and time of action and certain areas and way of acting, then they are attributed to a particular gang. So it is possible for the police to create a map with predictions of the action of this particular gang,

Prediction of crime incidents is essential in order to prevent and decrease future occurrences.

Therefore, Splunk Machine Learning Toolkit is used for prediction purposes in this dissertation.

The city of Volos was divided into three big sectors indicating the west (Sector 1), the center (Sector 2) and the east (Sector 3) parts of the city. The implementations of the prediction

algorithms, for these 3 Sectors and for every type of offense led to the predictive results which are presented in the below comparative charts.

Figure 4.8: Prediction for 2018 with confidence interval 95% regarding all crime incidents

Figure 4.9: Crime prediction for 2018 with confidence interval 95%

Figure 4.10: Prediction for 2018 with confidence interval 95% regarding Burglaries

Figure 4.11: Burglary prediction for 2018 with confidence interval 95%

Figure 4.12: Prediction for 2018 with confidence interval 95% regarding Car Robberies

Figure 4.13:Car Robbery prediction for 2018 with confidence interval 95%

Figure 4.14: Prediction for 2018 with confidence interval 95% regarding Motorcycle Robberies

Figure 4.15: Motorcycle Robbery prediction for 2018 with confidence interval 95%

Figure 4.16: Prediction of type of offense for the year 2018

Figure 4.17: Crime Prediction for 2018 over the months

It is concluded from the above charts that the Sector 2, the center part of the city, is the one with the most offenses and that the months where the largest number of offenses will appear in the 2018 are the months of January and February. Also the most common type of offense for the 2018 is expected to be the Burglaries.

Subsequently was defined the Busiest day, the day with the largest number of crime incidents. This day is Saturday 1st February of 2014 and 8 incidents have been occurred. By applying the Absolute Center Algorithm for the busiest day was determined the absolute center which indicates the point where the police car can be parked for stakeout. In fact the police car was located so as to minimize the maximum distance to or from the crime incidents points. The coordinates of the absolute center are: 39.37391, 22.93782.

In order to study how the police cans serve all the incidents occurred in the busiest day was applied the DARP problem and was concluded that the police vehicle can reach all the crime incidents points on time. In Section 3.6 is presented the aggregate table of the DARP results (table 3.6.2).

Furthermore the city of Volos was divided in 68 sectors and the median points for sectors with high criminality were estimated by applying the Single Median Algorithm. The median points are considered valuable for police officers, as they indicate which minimize the average distance to or from the incidents occurred in the sectors. These points can be considered as hot spots across the city and police officers can do patrol around them. Sectors p.25 and p.38 were the ones with the highest volume of crime incidents. In particular in Sector p.25 occurred 34 crime incidents and in Sector p.38 occurred 42 crime incidents. For Sector p.25 the median point is the point with coordinates 39.3704493, 22.9357456 (Leoforos Eirinis 1, N. Ionia) and is the incident point with id equals to 2257. The median point of the incident network for sector p38 is located on 39.3671358, 22.9502072 (Koutarelia 120, Volos).