KR20210091461A - Crime prevention apparatus and method using crime risk indices - Google Patents

Abstract

An objective of the present invention is to analyze criminal history information and public information to search for relevance and similarity with past crimes and diagnose the level of crime risk index (occurrence, exposure). In addition, the objective of the present invention is to analyze classification and relevance of newly reported/received data to predict a person/location that may be related to the crime. In addition, the objective of the present invention is to facilitate visual analysis and transmission of crime information for the purpose of responding to crimes. According to one aspect of present invention, a crime prevention device based on a crime risk index comprises: an integration unit collecting and storing public history information, public information, and reported/civil complaint information when a crime accident occurs in a specific area or when a new crime report or civil complaint is received; an analysis unit analyzing factors related to occurrence of a crime by using the collected and stored information; and a utilization unit providing data to be easily visually analyzed, such as promptly notifying a user, such as a police officer and the like, of a risk.

Description

{Crime prevention apparatus and method using crime risk indices}

The present invention relates to information collection and analysis technology, and more particularly, to an apparatus and method for preventing crime using collected information.

Single-person households (households in which one person independently maintains their livelihoods such as cooking, sleeping, etc.) were mainly made up of single-person households with the elderly due to changes in the family type, but since the 2000s, it has expanded to cover all age groups. The interest in single-person households was centered on lifestyles such as welfare, leisure life, and consumption patterns, but recently, crime damage and crime risk are attracting attention. Single-person households suffer relatively more crime (physical, property, housing, etc.) than multi-person households, and in particular, female single-person households under the age of 34 have a high rate of crime.

On the other hand, single-person households living in one-room houses felt the greatest anxiety, and residents of multi-family, multi-family, and row houses showed a moderate level of anxiety and low level of anxiety among apartment dwellers. These results are analyzed to be attributable to the fact that, in the case of single-person households living in one-room houses, when choosing a place of residence, they consider accessibility to school or work more than environmental conditions including safety.

In addition, as single-person households are in a situation where they have to respond alone in case of actual crime, there is a tendency to highly evaluate the possibility of crime risk that individuals perceive and interpret, and the older the age, the greater the risk of crime. Single-person households had the highest perception of crime risk. The high crime risk perception of single-person households is due to the overlapping of individual and regional factors, such as vulnerability to crime and residence characteristics.

Accordingly, the policy paradigm was shifted from post arrest to crime prevention with the introduction of the Crime Prevention Strategy (CPTED) program using environmental design, the Framework Act on Crime Prevention and the Law on Creation of the Foundation for Crime Prevention, strengthening the public security capacity and protecting the socially disadvantaged. Despite efforts to prevent crimes across society, such as the implementation of national tasks, the number of crimes increased by 61.1% from 1.14 million in 1990 to 1.84 million in 2016. Theft crimes showed a greater increase during the same period. In particular, past criminal history, such as violent crime, appears to have a higher risk of recidivism. For crime prevention, in addition to past criminal history, recidivism predictors such as education level, marital status, age at first arrest, school-age problem behavior, number of victims, acceptance of responsibility for crimes, and antisocial tendencies should be considered.

Due to this, the establishment of a safe environment such as intensive deployment of CCTV, which has the effect of reducing crime in areas with high concentration of single-person households, strengthening patrol of public security facilities, etc., and the level of disorder in the residential area, which increase the possibility of crime damage, entertainment consumption, women Development of a device that helps to improve the safety of single-person households and prevent crime by providing information on crime occurrence factors such as population density, lack of safety facilities such as crime prevention windows, housing stability, the proportion of households with low income levels, and various racial residential areas this is required

The incidence of crime continues to increase due to various factors such as population growth, and as crime increases, many people feel the fear of crime, and many studies have been conducted to prevent crime in order to reduce the fear of such crime. Among them, methods such as generating a risk map or predicting the likelihood of a crime by analyzing big data on crime statistics have been proposed. In the past, a service for displaying the overall level of crime in a specific area using a risk map generation method or displaying the level of risk based on a specific crime is provided, but services for specific individuals such as single-person households are insufficient.

In order to solve the above problem, the present invention provides personal and regional factor information according to the crime occurrence/exposure index, and provides services for specific individuals as well as differentiated crime risk information. We propose a crime prevention device and method that facilitates visual analysis and facilitates the delivery of criminal information so that people can escape from the existing crime damage and crime risk.

In order to solve the above problems, the present invention provides a crime risk index-based crime prevention device and an operating method thereof. Specifically, it analyzes criminal history information and public information to search for relevance and similarity with past crimes and diagnoses the level of crime risk index (occurrence, exposure). In addition, by analyzing the classification and relevance of newly reported/received data, the person/location that may be related to the crime is predicted. In addition, for the purpose of responding to the occurrence of crime, visual analysis is easy and the transmission of crime information is facilitated.

Specifically, the crime prevention device based on the crime risk index according to an aspect of the present invention provides public history information, public information, and report/competition reception information when a crime accident occurs in a specific area or a new crime report or civil complaint is received. an integrated unit that collects and stores; an analysis unit for analyzing factors related to the occurrence of a crime using the collected and stored information; and a utilization unit that provides data for easy visual analysis, such as promptly notifying a user of a danger, such as a police officer.

The integrator collects various information from various windows and identifies and integrates correlations between data in order to connect the collected data, and as the amount of data to be processed increases, raw data is converted into useful information through data pre-processing. Or, a collection unit for performing data pre-processing based on the data collected form and path; a report/reception unit that collects, integrates, and pre-processes complaint information obtained from the report center and civil complaints reception information based on the contents prepared according to the application form so that it can be input into the information analysis system; and authority management, encryption, de-identification for quality control, work history and version management, and data security accident prevention of the collected, integrated and pre-processed information data, and to store and easily share the results of each collection, integration, and pre-processing operation It includes a big data construction unit to make it possible.

The analysis unit may include: a first analysis unit that performs classification and correlation analysis of crime history information and public data, and a correlation analysis of public data that acts as an important influence factor according to classification and analysis of crime history information; a second analysis unit that performs classification and correlation analysis on the report/received data obtained from the report center; Crime in preparation for past events in order to search for and perceive the degree of association and similarity with past crimes by referring to the characteristics of the crime history information of the first analysis unit and the classification and correlation of the report/received data analyzed in the second analysis unit a diagnostic unit that performs risk assessment based on the level of the risk index (occurrence index) and the crime risk index (exposure index) for each location/facility; And the possibility of being involved in a crime by person (victim/perpetrator) and by predicting the dangerous time and risky place for each type of crime based on crime history information and public data such as the time and place where the crime occurred in the past. It includes a prediction unit that predicts a person who predicts a high individual/group.

The utilization unit includes: a visualization unit that performs data visualization such as crime time/space, crime network, crime distribution and case comparison for statistical, analysis, and predicted results for providing life safety information, supporting scientific investigation and supporting public affairs; And through connection with the big data portal and the national management system, it provides interlocking and real-time analysis services such as crime status and risk history to those involved in crime investigations to support crime prevention, decision-making by the person in charge, and on-the-spot security activities. It includes a connection unit that supports the opening of data such as crime statistics analysis to the public through a data portal.

In addition, the crime prevention method based on the crime risk index according to another aspect of the present invention collects, integrates, and pre-processes crime history information, public information, and reporting and civil complaint information, and big data to facilitate the storage and sharing of information. to build; In the case of non-report and civil complaint information, it is classified according to the characteristics of crime/public data (space, behavior, crime and socioeconomic/lifestyle/daily activity/physical environment characteristics). Analyzing the correlation with the public data, analyzing the correlation factors that are important in the classification of criminal history information; diagnosing the level of the crime risk index (occurrence index, exposure index) to search for and perceive the degree of association and similarity with past crimes using the information analyzed in the step; In the case of reporting and civil complaints information, the steps of classifying crimes by crime type, crime time and place, etc., and analyzing the crime-related associations with crime-related persons (victims, perpetrators, accomplices, etc.) and past incidents; Predicting crime occurrence response information (location, person) using the information analyzed in the above step and predicting the possibility of a subsequent crime of the subject of probation; and visualizing for the purpose of easily or providing the analysis of the diagnosis and prediction result of the above step to the user, and transmitting the information for the purpose of connecting and providing with other systems.

The configuration and operation of the present invention introduced above will become clearer through specific embodiments described later with drawings.

According to the present invention, it is possible to predict information such as the probability of occurrence of a crime, a place, etc. based on the analysis of the crime risk index for the person and the environment in which the crime occurs. In addition, it is possible to provide personal and regional factor information according to the crime risk index (occurrence index, exposure index), and provide services for specific individuals as well as differentiated crime risk information. In addition, there is an effect of easy visual analysis and easy delivery of criminal information so as to escape from possible crime damage and crime risk.

Therefore, by analyzing past crime history information and identifying patterns, it predicts a place and time with a high risk of crime, and provides detailed information about the location and type of crime around the address to identify specific crime information, which results in a high crime rate. It has the effect of preventing crime by prioritizing police personnel in the area.

1 is a schematic configuration diagram of a crime prevention device based on a crime risk index according to a preferred embodiment of the present invention;
2 is a block diagram of the integration unit 100 of FIG. 1 .
3 is a block diagram of the analysis unit 120 of FIG. 1 .
4 is a configuration diagram of the utilization unit 300 of FIG. 1 .
5 is a processing flowchart of a crime prevention method based on a crime risk index according to a preferred embodiment of the present invention;

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the detailed description in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, only this embodiment allows the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform the person of the scope of the invention, and the present invention is defined by the description of the claims. On the other hand, the terms used herein are for the purpose of describing the embodiment, not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, 'comprise' or 'comprising' refers to the presence or absence of one or more other components, steps, operations and/or elements other than the stated elements, steps, acts and/or elements. addition is not excluded.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In adding reference numerals to the components of each drawing, the same components are given the same reference numerals as much as possible even though they are shown in different drawings, and in explaining the present invention, detailed descriptions of related known components or functions In the case where the gist of the present invention may be obscured, the detailed description thereof will be omitted.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 shows a schematic description of a crime prevention device based on a crime risk index according to a preferred embodiment of the present invention. Referring to FIG. 1, when a crime accident occurs in a specific area or there is a new crime report/receipt, the crime risk index-based crime prevention device according to a preferred embodiment of the present invention is a crime history information, public information, and report/receipt of complaints. Information is collected and stored through the integration unit 100, and the analysis unit 200 analyzes the factors related to the occurrence of a crime using the information, and the utilization unit 300 promptly informs users of the risk, such as the police. , provides data (analysis information) for easy visual analysis.

The 'criminal history information' collected and stored by the integration unit 100 includes crime statistical information, criminal investigation records, crime type information (crime type, place type, occurrence region, time of occurrence, etc.), criminal psychology information, etc. . In addition, 'public information' includes population-related information (number of resident population, status of the elderly living alone, resident registration population, suicide rate, educational level, etc.), regional-related information (floating population, city risk, facility and company information, traffic safety information, fire occurrence status, lighting/CCTV installation, police personnel, etc.). In addition, it includes information on weather, assembly information, public information app, and social networking service (SNS). In addition, 'report and civil complaint information' is information collected through the report center and civil complaint portal.

The analysis unit 200 categorizes the crime history information, public information, and report and civil complaint information according to the respective constituent characteristics, and analyzes the relationship between the person related to the crime, the type of crime, the time, the place, and the like. In addition, with regard to reporting and complaints information, in preparation for past cases, the relationship between crime occurrence according to the frequency/strength of risk occurrence, such as the risk index for crime targets and victims, and the risk index for exposure to crime by time/place, is explored and related to crime. Assess the level of similarity to crimes such as person, crime type, time and place. In addition, information such as the possibility and location of the occurrence of a crime is predicted by synthesizing the situation such as the victim/perpetrator, time, region, and factors identified in the report and complaint information.

For a person involved in a crime, in the criminal history information, the person's behavioral characteristics (personality type, crime motive, criminal image, etc.), criminal characteristics (number of offenders, crime victim experience, etc.) and crime patterns (victims of crimes, perpetrators, accomplices, crimes, etc.) methods and behavior patterns) as a checklist for diagnosing the risk index, and calculating the crime risk index (occurrence index) of the crime target and victim. The above crime risk index is a measure of social and economic characteristics (population, crime-vulnerable class, economic level, housing stability, etc.) in public characteristics, lifestyle/daily activity characteristics (routine activities including occupational/leisure activities and repetitive activities to satisfy basic needs) It is calculated by giving weights in consideration of shopping/housing activities, etc.) and physical environment characteristics (floating population, etc.). Then, by referring to the recidivism predictors, the recidivism risk index is calculated and criminal candidates are recommended, and the information is provided to the person in charge of security. Here, the people involved in the crime are the criminal, the target/victim of the crime, the controller (police power), and the protector.

As the weighting method, statistical techniques such as regression analysis or correlation analysis may be used. In the regression analysis, the weighting target index (the characteristic) is set as an independent variable, and the upper item (the crime risk index) of the index is set as a dependent variable, and the regression coefficient value estimated by performing regression analysis can be set as a weight. Correlation analysis may assume a causal structure of the change in the crime risk index item and the change in the characteristic index, and set the calculated factor score coefficient as a weight.

On the other hand, for the environment in which a crime occurs, the crime risk index (exposure index) that evaluates the crime risk according to the spatial characteristics (criminal environment, facilities, etc.) and the movement path of a person in the crime history information is a checklist for risk index diagnosis and a target It is calculated using a map of crime patterns occurring in the area. The crime risk index (exposure index) includes crime statistics (location, time, date, day of the week, etc.) for user movement path information, movement distance and movement time, transit facilities, etc., and physical environmental characteristics (guardianship/surveillance, physical /Social disorder, land use, local aging, etc.) It is calculated by assigning a weight in consideration of the data. In addition, the target area is calculated using the crime pattern map in which crime history information that has occurred in the past is reflected and information on the area where the crime is concentrated is displayed. Here, the environment in which the crime occurs is a place or a crime prevention facility.

In a specific embodiment, the risk occurrence frequency is a crime-related person, a crime type, a time, and an accident frequency by place. The level of risk occurrence frequency can be defined as shown in Table 1.

Classification of risk occurrence frequency level Explanation lowness One Occurs several times a year has exist 2 Occurs several times a month height 3 Occurs several times a day

In addition, the risk occurrence intensity is an accident damage intensity by crime-related person, crime type, time, and place. The level of risk occurrence intensity can be defined as shown in Table 2.

Classification of risk occurrence intensity level Explanation does not exist One No human loss minor 2 Minor human loss company 3 fatal human loss

And, according to the criminal history of the offender (or criminal), the recidivism risk index is calculated by referring to the recidivism predictive factors, and the criminal candidate is recommended and the information is provided to the person in charge of security. The above recidivism risk index is a characteristic of criminals that can be obtained from criminal history information. Age, age of first arrest, education level, marital status, past criminal history (homogeneous criminal history, violent criminal history), school-age problem behavior and delinquency, number of victims, It is evaluated based on factors such as acceptance of responsibility for the crime, antisocial tendencies, and psychopathic tendencies. The level evaluation of the recidivism risk index depends on the frequency of risk occurrence and the intensity of risk occurrence.

The utilization unit 300 calculates the risk index of crime in a person calculated by the analysis unit 200, the risk index of exposure to crime by time/place, reporting and civil complaints information statistics, and the recidivism risk index to crime occurrence. Visual analysis is easy for the purpose of responding and the information is visualized for the purpose of facilitating the delivery of response information, and the information is delivered to users.

2 shows a detailed configuration of the integration unit 100, and each configuration will be described in detail below. Referring to FIG. 2 , the integration unit 100 includes a collection unit 110 , a report/reception unit 120 , and a big data construction unit 130 .

The collection unit 110 collects, integrates, and pre-processes criminal history information and public information. It collects various types of information from various channels, and in order to connect the data obtained through many different collection windows, the correlation between the data is identified and integrated. As the amount of data to be processed increases, raw data can be turned into useful information through data preprocessing, and natural language processing or machine learning can be utilized for data integration and quality control. As a result, data pre-processing is performed based on the collected data type and path.

The report/receipt unit 120 collects, integrates, and pre-processes report/civil complaint information. The person receiving the report center receives the report information, such as the report receipt processing table, the monthly report of the report operation status, and the work diary, obtained in accordance with the report handling rules, and the civil complaint information based on the contents written according to the form of the application to be input into the information analysis system. Collect, integrate, and pre-process so that In addition, the report center secures information related to crime (act) or disorder through the National Reporting App or SNS.

The big data building unit 130 collects, integrates, and pre-processes information data quality management, work history, and version management, and rights management/encryption/de-identification for data security accident prevention and each collection, integration, and pre-processing work Save the results of your work and make it easy to share. In addition, data preprocessing is performed based on which analysis the corresponding data is used for.

3 shows a detailed configuration of the analysis unit 120, each configuration will be described in detail below. Referring to FIG. 3 , the analysis unit 200 includes a first analysis unit 210 , a second analysis unit 220 , a diagnosis unit 230 , and a prediction unit 240 .

The first analysis unit 210 classifies the crime history information and public data and analyzes the correlation. Criminal history information is classified into spatial characteristics (criminal environment, facilities, etc.), behavioral characteristics (personality type, crime motive, criminal image, etc.), and criminal characteristics (number of crimes, number of reports, number of suspects, crime victim experience, etc.) and their relevance Analyze In addition, crime patterns such as crime types, crime time and place, crime victims, perpetrators, accomplices, crime methods, and behavior patterns included in the criminal history information are compared with those of past events and the correlation is analyzed.

And the first analysis unit 210 classifies public data into socioeconomic characteristics, lifestyle/daily activity characteristics, and physical environment characteristics, which are public characteristics, and analyzes the correlation. Socioeconomic characteristics include population, crime-vulnerable class, economic level, and housing stability. Lifestyle/daily activity characteristics include daily activities including occupational and leisure activities, and repetitive shopping and residential activities to satisfy basic needs. Physical environment characteristics include guardianship/surveillance, floating population, physical/social disorder, land use, and local aging. In addition, the first analysis unit 210 analyzes the correlation acting as an important influence factor with the public data according to the classification and analysis of the criminal history information.

The second analysis unit 220 analyzes the classification and relevance of the new report/received data. The classification of the reported/received data classifies crimes by crime type, crime time and place, etc., and analyzes crime relevance by comparing the relationship with crime victims, crime perpetrators, accomplices, and past events.

The diagnosis unit 230 is based on spatial characteristics, behavior characteristics, and crime characteristics of the crime history information of the first analysis unit 210 and socioeconomic characteristics of public data, lifestyle/daily activity characteristics, and physical environment characteristics. , crime in preparation for past events in order to search for and perceive (distinguish, judge, identify) the degree of association and similarity with past crimes with reference to the classification and relevance of the report/received data analyzed by the second analysis unit 220 The level of risk is diagnosed based on the crime risk index (occurrence index) of the target/victim and the crime risk index (exposure index) by location/facility. The degree of risk can be determined as shown in Table 3 based on the above-described risk occurrence frequency and risk occurrence intensity. In Table 3, values 1 to 2 are negligible risks, values 3 to 4 are minor risks, and values 6 to 9 are significant risks.

Hazard intensity
Hazard occurrence frequency ＼ None(1) Minor(2) Company (3) Low(1) daytime (1) daytime (2) daytime (3) Yes(2) Yes, No (2) I, Kyung (4) Yes, Middle (6) High(3) high, no (3) high, hard(6) high, medium (9)

The prediction unit 240 performs a location prediction for predicting a dangerous time and a dangerous place that may occur for each type of crime based on public data and crime history information such as a time and place where a past crime occurred. In addition, person prediction is performed to predict individuals/groups that are highly likely to be involved in crimes by person (victim/perpetrator). When performing location prediction and person prediction, the level of risk based on the crime risk index (occurrence index) of the crime target/victim of the diagnosis unit 230 and the crime risk index (exposure index) level for each place/facility is used.

And, based on the crime pattern of the first analysis unit 210, the likelihood of a subsequent crime is predicted for each crime type by referring to the possibility of recurrence of a crime of the subject of probation and the correlation of similar crimes. The second analysis unit 220 recommends criminal candidates (criminal perpetrators and accomplices) using the crime correlation and recidivism prediction index and provides the information.

4 shows a specific configuration of the utilization unit 300, each configuration will be described in detail below. Referring to FIG. 4 , the utilization unit 300 is configured to include a visualization unit 310 and a connection unit 320 .

The visualization unit 310 visualizes data such as crime time/space, crime network, crime distribution, and case comparison for statistical, analysis, and predicted results for providing life safety information, supporting scientific investigation, and supporting public affairs. carry out Here, the life safety information is crime occurrence (hot spot) information for life safety guidance focusing on public safety. The forensic support is to quickly find information similar to the crime method through machine learning to track the suspect's crimes, and to support violent crime prevention and local safety information analysis. In addition, it is possible to prevent crimes in advance by identifying areas and times where crimes occur frequently, and by deploying patrol personnel at a time when there is a high probability of occurrence of crimes. The above-mentioned public service support is to support citizens to easily check safety information such as risk factors that they frequently encounter in their daily life on a map anytime, anywhere.

The connection unit 320 is connected to a big data portal and a national management system (geographical profiling system, crime analysis system, etc.) It interlocks services such as risk history and provides real-time analysis services to support crime prevention, decision-making of the person in charge, and on-site security activities. In addition, it supports the opening of data such as crime statistics analysis to the public through a public data portal.

5 is a flowchart illustrating a method of operating a crime risk index-based crime prevention apparatus according to a preferred embodiment of the present invention. The method according to this embodiment may be performed through the crime risk index-based crime prevention device described with reference to FIGS. 1 to 4 .

In step S10, it collects, integrates, and pre-processes criminal history information, public information, and report/competition reception information, and builds big data to facilitate the storage and sharing of information.

In step S20, if the collected information is not report or civil complaint information, it is classified according to the characteristics of crime/public data (space, behavior, crime and socioeconomic/lifestyle/daily activity/physical environment characteristics). In addition, for criminal history information, the correlation between crime patterns and past events is analyzed, and for public data, correlation factors that are important for classification of criminal history information are analyzed.

In step S30, the level of the crime risk index (occurrence index, exposure index) is diagnosed in order to search for and perceive the degree of association and similarity with past crimes using the information analyzed in step S20.

In step S40, if the collected information is report and civil complaint information, the crime is classified by crime type, crime time and place, and the crime related to the crime-related (victims, perpetrators, accomplices, etc.) and the past events are analyzed.

In step S50, crime occurrence response information (location, person) is predicted using the information analyzed in step S40. It also predicts the likelihood of subsequent crimes of probation subjects.

In step S60, the diagnosis and prediction results of steps S40 and S50 are visualized for the purpose of easy (forensic investigation support) or providing users (living safety information, public service support, and services for persons concerned with criminal investigations), and Information is transmitted for the purpose of connection and provision of systems (big data portals, public data portals, national management systems, etc.).

As described above, the present invention can be implemented in terms of an apparatus or a method. In particular, the function or process of each component of the present invention is a digital signal processor (DSP), a processor, a controller, and an ASIC (application-specific IC). ), a programmable logic device (FPGA, etc.), and other electronic devices, and a combination thereof. In addition, it can be implemented as software in combination with a hardware element or independently, and the software can be stored in a recording medium.

As mentioned above, although the configuration of the present invention has been described in detail through preferred embodiments of the present invention, those of ordinary skill in the art to which the present invention pertains will appreciate the content disclosed in the present specification without changing the technical spirit or essential features of the present invention It will be understood that the present invention may be implemented in a specific form other than the above. It should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of protection of the present invention is defined by the following claims rather than the above detailed description, and all changes or modifications derived from the claims and their equivalent concepts should be construed as being included in the technical scope of the present invention. .

Claims (1)

An integrated unit that collects and stores public history information, public information, and reporting and civil complaint information when a criminal accident occurs or a new crime report or complaint is received;
an analysis unit for analyzing factors related to the occurrence of a crime by using the collected and stored information; and
A crime prevention device based on a crime risk index that includes an application that visually provides data to users.

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