, pp.150-157 http://dx.doi.org/10.14257/astl.2016.139.34 The Relationship between Crime and CCTV Installation Status by Using Artificial Neural Networks Ahyoung Jung 1, Changjae Kim 2, Dept. S/W Engr. Soongsil University aayoungs@naver.com, winchang@ssu.ac.kr 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 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. 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 Copyright 2016 SERSC 153
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 154 Copyright 2016 SERSC
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 Hidden layer node = 3 Copyright 2016 SERSC 155
Hidden layer node = 5 Hidden layer node = 7 Hidden layer node = 9 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 156 Copyright 2016 SERSC
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 Copyright 2016 SERSC 157