The Relationship between Crime and CCTV Installation ...onlinepresent.org/proceedings/vol139_2016/34.pdf · The Relationship between Crime and CCTV Installation Status by Using Artificial

The Relationship between Crime and CCTV

Installation Status by Using Artificial Neural Networks

Ahyoung Jung1, Changjae Kim2,

Dept. S/W Engr. Soongsil University

[email protected], [email protected]

Abstract. In this study, correlations are found between crime and CCTV using

multiple regression analysis and Artificial Neural Network. Determine

alternative ways to reduce crime by identifying the number of CCTV

installations for strong crime committed by local regions. Through a Multiple

regression analysis, we suggested a model of a CCTV installation that

determines the relationship between powerful crime and CCTV, which can

effectively prevent violent crime and prevent the possibility of effective

prevention of the crime.

Keywords: CCTV, Artificial Neural Network, Multiple Linear Regression

1 Introduction

Social unrest is rising as the nation's violent crimes soar. The CCTVs are being used

to deter criminal crimes and identify crimes against criminals worldwide. The CCTV

is effective in preventing crime prevention, and is needed to prevent effective crime

prevention measures, considering the possibility of crime zones and crime prone

areas.

A theoretical basis for preventing crime prevention by installing CCTV cameras is

the prevention of crime prevention. The crime prevention techniques against crime

prevention are criminal crime prevention techniques that enable criminals to deter

criminal crimes by preventing criminal crimes and control of criminal crimes

committed by criminals in the mid-1990s. Moreover, preventive crime prevention

theory is not a social system improvement, but a preventive approach that relies solely

on reducing crime opportunities. Thus, the theory of crime prevention differs from the

crime of criminal criminology, which focuses on crime in the context of immediate

environmental circumstances, which are expected to focus on the immediate

environment, circumstances, and characteristics[1]. Circumstance crime prevention is

based on rational choice theory, criminal opportunity theory, and crime prevention

theories through environmental design[2].

The CCTVs are rapidly increasing CCTVs in the wake of the recent crime

prevention, and CCTV monitors, which have been investigating the Ministry of

Public Administration and Home Affairs in May 2015, are 12,5608 CCTV cameras,

and 72 percent of them are CCTV cameras. Also, the crime prevention effect is

Advanced Science and Technology Letters Vol.139 (FGCN 2016), pp.150-157

http://dx.doi.org/10.14257/astl.2016.139.34

ISSN: 2287-1233 ASTL Copyright © 2016 SERSC

expected to increase further as the expansion of CCTV tapes for crime prevention and

the development of intelligent CCTV technology progresses rapidly at a faster

pace[3].

There are limitations to preventing crime and responding to crime, such as crime

zones in crime zones and crime prone areas.

In this study, we propose to suggest a CCTV installation model that identifies the

status of crime in areas where local crime is organized and effectively prevent crime

prevention. The composition of this research is as follows. We will explore the

existing literature related to this study and explore the regression model and the

regression model of the research in this study. Chapter 3 describes the variables and

models used in the models of this study. After analyzing and verifying the processes

in Chapter 4, we will conclude the conclusion in Chapter 5, Present and Future, and

finalize this thesis.

2 Related Study

2.1 Crime Prevention and CCTV

With the recent surge in violent crime, the number of crimes in Korea is increasing,

causing the nation's stability to rise to a record low of 18.3 percent, according to data

compiled by the National Statistical Office. The installation of CCTVs in crime

prevention centers has proven to be a crime prevention effect, and a total of 23,000

criminals are found each year at the 79 CCTV service centers national wide[4].

In the United States, CCTV is being installed to prevent crime prevention in urban

areas and residential areas, and more than three times more CCTVs have been

installed since 9/11. As the U.S.'s main goal of the U.S.-led national policy toward the

United States since 9.11, the use of CCTV as a tool for prevention of crime and the

use of CCTV as a tool for the prevention of crime has been expanded. In the UK,

CCTV cameras were first introduced in the mid 1980s to prevent the country from

becoming the world`s first movable soccer field, the first in the world since the

introduction of CCTVs in the world. Currently, the nation is installing CCTVs in most

of the EU countries, but it is currently being installed as a focal point for preventing

the establishment of national security and prevention of terrorism[5].

2.2 Multiple Linear Regression Analysis

Multiple regression analysis is a form of regression analysis implied by a statistical

analysis of a causal relationship between variables. The regression analysis describes

the relationship between the independent variable and the dependent variables that

contribute to the resulting cause. Linear regression analysis is a linear regression

analysis for a linear regression model and a linear regression model with two

independent variables. Estimates of regression analysis provide estimates by

extrapolation of estimated regression models given the estimated regression model.

Advanced Science and Technology Letters Vol.139 (FGCN 2016)

Copyright © 2016 SERSC 151

Linear regression models are often fitted using the least squares approach, but they

may also be fitted in other ways, such as by minimizing the "lack of fit" in some other

norm (as with least absolute deviations regression), or by minimizing a penalized

version of the least squares loss function as in ridge regression (L2-norm penalty) and

lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit

models that are not linear models. Thus, although the terms "least squares" and "linear

model" are closely linked, they are not synonymous.

Describe the relationship between the five major crimes and the CCTV

installations and describe the relevance of data to the estimated regression model.

2.3 Artificial Neural Network

Artificial neural network is a statistical study algorithm modeled at the physical unit

of the brain, the physical unit of the brain. With the values of the neurons that have a

threshold and a function of each neuron, the value of each neuron is transmitted to the

following neurons to repeat the final output value to the next neuron. In other words,

the artificial neural network model has three levels of structure, which is output,

hidden, and input layer. It is utilized in research of artificial intelligence, such as

prediction and pattern recognition.

The goal of the neural network is to solve problems in the same way that the

human brain would, although several neural networks are much more abstract.

Modern neural network projects typically work with a few thousand to a few million

neural units and millions of connections, which is still several orders of magnitude

less complex than the human brain and closer to the computing power of a worm.

Fig. 1. Structure of Artificial Neural Network


152 Copyright © 2016 SERSC

3 Mail Title

3.1 Design of research

Based on the crime of murder, robbery, rape, theft and assault of the five major

crimes committed in 2011-2014, it is analyze the number of CCTV cameras for local

crime prevention.

As of [Figure 2], the number of crimes in 2011 was set as an independent variable.

The status of CCTV installation is set as a dependent variable. Analyze the correlation

between variables and derive linear relationship analysis to analyze the conformity of

the data. We propose a model for installing a CCTV camera to prevent violent crime

using artificial network.

Fig. 2. Crime status and CCTV installation status



Fig. 3. A model of CCTV Installation Status Using Artificial Neural Network

3.2. A Proposal of a CCTV Installation Model Using Artificial Neural Networks

When using Artificial Neural Network, the nine of hidden layer node models is

proposed for the most effective among the five cases. The number of hidden layer

nodes was divided by 1, 3, 5, 7, 9. The performance of the model assessed SSE, Steps

of training, and correctness. The correlation analysis enhanced the reliability of the

model. The figure below of [Figure 3] is a model with nine artificial neural network

nodes.

4 Experimental and verification

The correlation was analyzed to determine how much the number of crime related

crimes and the number of CCTV installations were related in 2011-2014. [Figure 4]

shows the visualization of correlation.

Fig. 4. The correlation between crime status and CCTV installation



The correlation between the five major crimes and the number of CCTV

installations has a strong linear correlation. Perform multiple linear regression

analysis to assess the conformity of the model. The following [Figure 5] shows the

result of multiple linear regression analysis.

Fig. 5. Multiple Linear Regression

The coefficient of determination describes the complete data as the estimated

regression model is closer to 1. The coefficient of determination is assumed to be

representative of the regression model estimated at 74.3 %.

Based on a multiple linear analysis of crime and CCTV, the use of Artificial

Neural Network is used to propose effective CCTV installation models for crime

crimes. The Artificial Neural Network has five properties and an output node that

predicts the installation of the CCTV and hidden node. The Artificial Neural Network

shows the weight for each of connection, the number of repetitions, the measurement

of the error level. The number of hidden layer nodes has been increased as a way to

improve the performance of the Artificial Neural Network. When the performance of

the hidden layer node is 9, the sum of the error is the smallest of the 0.0237. The

number of training steps has become a complex model with 413.

Hidden layer node = 1







Fig. 6. Artificial Neural Network model

Fig. 7. Accuracy of Artificial Neural Network

Performance evaluation of a model measures the correlation between forecasted

and actual values. The following [Figure 7] illustrates the correctness of each model.

The accuracy of the model indicates that the correlation between the actual values

and the predicted values is closer to 1, and that it is well predicted. Considering the

sum of the error and the number of precision of the discipline and the precision of the



model, the status of the CCTV installation for crime is proposed with 9 model

numbers per node.

5 Conclusion

In this thesis, we proposed a model for the installation of CCTV in accordance with

violent crime. To ease the public's anxiety and prevent crimes from spreading, the

public is installing CCTVs in the nation and the local governments as well as citizens

to prevent crime. CCTV plays a crucial role in preventing crime and securing

evidence at the same time.

In this thesis, it analyzes the relationship between crime and the number of CCTV

cameras, and uses the Artificial Neural Network to provide a model for crime

prevention for crime prevention.

By analyzing the spatial characteristics of crime zones, it will be possible to

propose effective CCTV installation models for crime prevention by proposing a

crime prevention model for crime zones.

References

1. Kim, S.: Crime and criminal policies in South Korea. Research Institute of Criminal

Policy Studies, South Korea, (2009)

2. Hyung Jo, i., Ku Lee, K.: Journal of Governmental Studies. vol. 18. 2012, (2012), pp. 187-

227

3. National Police Agency: Institute of Public Security Policy. Security Outlook, South

Korea, (2016)

4. Korea Research Institute for Human Settlements: A Study on the Urban Safety Methods

Considering the Spatial Characteristics of the Crime Occurrence. South Korea, (2014)

5. Korea Information Technology Promotion Agency: A Case Study on Four Social Safety

Net Sites Using Spatial Information. South Korea, (2012)

6. Jahan Hossain, Md. S. and Ahmad, Dr. N.: Artificial Intelligence Based Surface

Roughness Prediction Modeling for Three Dimensional End Milling. IJAST, vol. 45,

August, (2012), pp. 1-18

7. Ramchandra Baviskar, P. and Tungikar, V. B.: Multiple Cracks Assessment using Natural

Frequency Measurement and Prediction of Crack Properties by Artificial Neural Network.

IJAST, vol. 54, May, (2013), pp. 23-38.

8. Benacer I. and Dibi, Z.: Modeling and Simulation of Organic Field Effect Transistor

(OFET) Using Artificial Neural Networks. IJAST, vol. 66, May, (2014), pp. 79-88



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The Relationship between Crime and CCTV Installation ...onlinepresent.org/proceedings/vol139_2016/34.pdf · The Relationship between Crime and CCTV Installation Status by Using Artificial