KR101660160B1 - A Method for Estimating a Crime Risk Using Big Data and A System for Reporting the Crime Risk Using thereof - Google Patents

A Method for Estimating a Crime Risk Using Big Data and A System for Reporting the Crime Risk Using thereof Download PDF

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KR101660160B1
KR101660160B1 KR1020150026941A KR20150026941A KR101660160B1 KR 101660160 B1 KR101660160 B1 KR 101660160B1 KR 1020150026941 A KR1020150026941 A KR 1020150026941A KR 20150026941 A KR20150026941 A KR 20150026941A KR 101660160 B1 KR101660160 B1 KR 101660160B1
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crime
risk
user
data
relative
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KR1020150026941A
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Korean (ko)
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KR20160104221A (en
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황유민
차경현
김경호
김진영
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광운대학교 산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06F17/30—
    • G06F19/00—

Abstract

The present invention relates to a method for estimating the risk of crime using Big Data and a system for notifying a risk of crime using the method.
To this end, the present invention comprises: collecting crime data counted according to a predetermined classification criterion; Calculating an absolute crime risk level for a combination of items selected for each of the classification criteria based on the crime data; Obtaining priority item information of a user for each classification criterion; Calculating a relative risk of crime based on the obtained priority item information of the user; Generating a user crime risk by combining the absolute crime risk and the relative crime risk; And providing the generated user crime risk information to the user; A method for estimating a crime risk using Big Data, and a system for reporting a crime risk using the method.

Description

Technical Field [0001] The present invention relates to a method for predicting a crime risk using Big Data and a method for reporting a crime risk using the method,

The present invention relates to a method for estimating a crime risk using Big Data and a system for reporting a crime risk using the same, and more particularly, to provide a user-customized value of a crime risk and provide the same.

Big data refers to large-scale data that is generated in a digital environment, has a large size, has a short generation cycle, and includes not only numerical data but also various forms such as text and image data. Big Data has emerged with increasing data due to the development of various sensors and the Internet. As computers and processing technologies develop, analysis based on big data generated in the digital environment has increased the possibility of discovering new perspectives and laws about changes in diseases and social phenomena. Therefore, Big Data can be used to solve social problems, especially in crime prevention and investigation.

For example, using Big Data technology can analyze historical crime data and identify patterns that can predict places and times of high crime risk. In addition, crime location and type can be provided in detail around the address, to identify and prevent specific crimes, and to prevent crime by prioritizing police personnel in areas of high crime incidence.

However, the individual and societal values of each crime can be different. For example, in the case of a gun crime, the severity or risk of the crime may differ between countries where individual gun possession is allowed and unacceptable. In addition, since the degree of interest in each type of crime differs from person to person, the same value can not be given collectively for the same kind of crime. Therefore, it is necessary to provide differentiated crime risk information from such personal, social and political perspectives to users who are provided with the possibility of crime in each region.

The present invention has a purpose to consider the value possessed by each crime information in terms of individual, socio-political, and statistical perspectives, to quantify the crime information, and to provide such data to the user.

According to another aspect of the present invention, there is provided a method for estimating a crime risk using Big Data, the method comprising: collecting crime data counted according to a predetermined classification criterion; And at least one of a crime occurrence time, the crime data indicating the number of crime incidents according to each item in the classification standard; Calculating an absolute crime risk score for a combination of items selected for each classification criterion based on the crime data, the absolute crime risk level being determined by a combination of the number of crime occurrences in each item selected for each classification criterion; Obtaining priority item information of a user for each classification criterion; Calculating a relative risk of crime based on the obtained priority item information of the user; Generating a user crime risk by combining the absolute crime risk and the relative crime risk; And providing the generated user crime risk information to the user; And a control unit.

In this case, when the crime data is counted as n classification criteria, the absolute crime risk is calculated as an n-dimensional vector.

In addition, the absolute crime risk is an n-dimensional vector having the number of crimes occurring in each item selected for each classifier as coordinates of each axis.

According to an embodiment of the present invention, the relative crime risk is calculated as an n-dimensional vector of a combination of items selected for each of the classification criteria, and the user crime risk is calculated using the absolute crime risk vector and the relative crime risk vector Is summed and summed.

According to another embodiment of the present invention, the relative crime risk is calculated as a weight for the priority item of the user, and the user crime risk is calculated by using the coordinates corresponding to the priority item in the absolute crime risk vector as the weight And is generated based on the multiplied value.

According to an embodiment of the present invention, the method further includes acquiring the personal information of the user, and the priority item information of the user is generated based on the personal information of the user.

The method may further include collecting real-time social crime risk information using at least one of news data, social network service (SNS) data, and web data, and the relative crime risk is calculated using the collected social crime risk information .

Meanwhile, a crime risk alert system using Big Data according to an embodiment of the present invention includes a communication unit for transmitting / receiving data to / from an external terminal or a network; Wherein the classification data includes at least one of a crime type, a place of crime occurrence and a crime occurrence time, and the crime data includes at least one of the number of crime incidents A data collection unit for acquiring priority item information of a user for each of the classification criteria; And an absolute crime risk level for a combination of items selected for each of the classification criteria based on the crime data, wherein the absolute crime risk level is determined by a combination of the number of crime occurrence times in each of the items selected for each of the classification criteria A risk calculating unit for calculating a relative crime risk based on the obtained priority item information of the user and generating a user crime risk by combining the absolute crime risk and the relative crime risk; And the system provides the generated user crime risk information to the user terminal.

According to the present invention, it is possible to calculate an absolute crime risk value according to the incidence of crime using big data, and at the same time, a weight is added to a crime type of which the user is highly interested, thereby providing user crime risk information suitable for the user .

1 is a block diagram illustrating a crime risk alert system using Big Data according to an embodiment of the present invention.
FIG. 2 is a table showing an example of crime data counted according to predetermined classification criteria according to the present invention. FIG.
FIG. 3 is a table showing one embodiment of a method in which the relative risk of crime is calculated; FIG.
FIG. 4 is a table showing another embodiment of a method in which the relative risk of crime is calculated; FIG.
5 illustrates a method for generating a user crime risk using absolute crime risk and relative crime risk according to an embodiment of the present invention.
Figure 6 illustrates a method for generating a user crime risk using absolute crime risk and relative crime risk according to another embodiment of the present invention.
7 is a flowchart illustrating a method for estimating a crime risk using Big Data according to an embodiment of the present invention.

As used herein, terms used in the present invention are selected from general terms that are widely used in the present invention while taking into account the functions of the present invention. However, these terms may vary depending on the intention of a person skilled in the art, custom or the emergence of new technology. Also, in certain cases, there may be a term arbitrarily selected by the applicant, and in this case, the meaning thereof will be described in the description of the corresponding invention. Therefore, it is intended that the terminology used herein should be interpreted relative to the actual meaning of the term, rather than the nomenclature, and its content throughout the specification.

FIG. 1 is a block diagram illustrating a crime risk alert system using Big Data according to an embodiment of the present invention. Referring to FIG. As shown in the figure, the crime risk notification system according to the embodiment of the present invention includes a server 100 and a user terminal 200.

The server 100 of the present invention collects crime data and generates and provides crime risk information and includes a communication unit 110, a data collecting unit 120, a risk calculating unit 130 and a big data DB 140 .

First, the communication unit 110 can transmit / receive data to / from an external terminal or a network using various wired or wireless communication systems. At this time, the available wireless communication methods include Wi-Fi, Long Term Evolution (LTE), Bluetooth, Near Field Communication (NFC), Zigbee, infrared communication, and the like.

Next, the data collecting unit 120 collects the crime-related data through the communication unit 110. In the embodiment of the present invention, the crime data may indicate the number of crime occurrences counted according to a predetermined classification criterion, which may include, for example, a crime type, a place of crime occurrence, and a crime occurrence time. Crime data represents the number of crimes that occur in each category in the classification criteria. A specific example of this will be described later.

Also, the data collection unit 120 acquires the priority item information of the user for each classification criterion. According to an embodiment of the present invention, the priority item information can be directly selected by each user, and the selected priority item information can be received through the communication unit 110. [ According to another embodiment of the present invention, the server 100 may acquire personal information of each user, such as sex, age, occupation, residence, etc., and the data collecting unit 120 may acquire personal information The priority item information of the user can be generated.

Next, the risk calculation unit 130 generates a crime risk per user and provides it to the user terminal 200. According to the embodiment of the present invention, the risk calculation unit 130 calculates an absolute crime risk based on the collected crime data, and calculates a relative crime risk based on user information, real-time social crime risk information, and the like. The risk calculation unit 130 generates a user crime risk level for each user by combining the calculated absolute crime risk and relative crime risk. The risk calculation unit 130 provides the generated user crime risk information to the corresponding user terminal 200 through the communication unit 110.

The Big Data DB 140 stores various digital data and may store information such as crime data and crime risk according to an embodiment of the present invention. The big data DB 140 can be implemented through various digital data storage media such as a hard disk drive, a flash memory, a random access memory (RAM), and a solid state drive (SSD).

Meanwhile, the user terminal 200 may receive user crime risk information from the server 100 and output the received information. Various types of digital devices such as a smart phone, a desktop, a laptop, a tablet PC, a PDA (Personal Digital Assistant) A smart watch, an HMD (head mounted display), and the like. 1, the user terminal 200 may include a communication unit 210, an output unit 220, and a control unit 230.

The communication unit 210 can transmit / receive data to / from the server or the network using various wired or wireless communication systems. At this time, the available wireless communication methods include Wi-Fi, Long Term Evolution (LTE), Bluetooth, Near Field Communication (NFC), Zigbee, and infrared communication.

The output unit 220 may include various types of video and / or audio information output means, such as a display unit, a speaker, and the like. The output unit 220 can output various kinds of information in the form of video and / or audio based on the control of the control unit 230. [

The control unit 230 may control the operation of each unit of the user terminal 200 and may process data in the user terminal 200. In addition, the control unit 230 can control data transmission / reception between the respective units of the user terminal 200. [ The control unit 230 of the user terminal 200 controls the output unit 220 to output the user crime risk information received from the server 100.

FIG. 2 is a table showing an example of crime data counted according to predetermined classification criteria according to the present invention. According to the embodiment of the present invention, the number of crime occurrence times can be classified according to various classification criteria and can be classified according to, for example, a crime type?, A crime occurrence place?, A crime occurrence time? .

Each classification criterion can be composed of at least one item. The crime category (α) can be composed of different kinds of crime items (α1, α2, α3, ...). Such crime items can be organized according to the crime classification criteria of the Supreme Prosecutors' May be set. The crime data shows information on the number of crime incidents according to each crime type item (α1, α2, α3, ...).

In addition, each item (β1, β2, β3, ...) of crime occurrence place (β) can be set according to the type of crime occurrence place such as apartment, single house, highway, shop, , Schools, warehouses, subways, public transportation, and the like. According to another embodiment of the present invention, each item (? 1,? 2,? 3, ...) of the crime occurrence place (?) May be set according to the area of the place where the crime occurs, such as Seoul, Gyeonggi, Incheon, Busan, etc., or may be set according to a more detailed geographical classification.

The items of the crime occurrence time γ may be configured in units of a predetermined time as illustrated in FIG. 2, or may be configured in a time zone such as dawn, morning, day, morning, afternoon, Night, and the like.

According to the embodiment of the present invention, each item in a specific classification criterion may be set to an exclusive range mutually, and some range may be set to overlap. For example, each item? 1,? 2,? 3, ... of the crime occurrence time? May be set for mutually exclusive time zones as in the example of FIG. 2, 2 o'clock to 5 o'clock, and gamma 3 = 4 o'clock to 7 o'clock.

On the other hand, the crime data may show the absolute value of the number of crimes according to each item in the classification standard, or may show the relative value with respect to the total number of crimes. If the crime data is represented by a relative value, the crime data for each item can be determined by the ratio of the number of crimes in the item to the total number of crimes according to each item in the same classification standard.

In this manner, the data collection unit 120 of the server 100 collects the crime data and transfers the collected crime data to the risk calculation unit 130. The risk calculation unit 130 calculates an absolute crime risk based on the collected crime data. According to the embodiment of the present invention, the risk calculation unit 130 can calculate the absolute crime risk level for a combination of items selected for each classification criterion of crime data. For example, if crime C (α3) is selected in the classification criterion α, place S2 (β2) is selected in the classification criterion β, and 0:00 to 3:00 (γ1) is selected in the classification criterion γ, The absolute risk of crime V (α3, β2, γ1) in which crime C occurs is calculated. At this time, the absolute crime risk V (α3, β2, γ1) is calculated by combining the crime occurrence counts (52, 34, 65) in each item. Therefore, the higher the number of crimes in each item, the higher the absolute crime risk.

According to one embodiment, the crime risk V (? 3,? 2,? 1) can be expressed as a three-dimensional vector having an x value of 52, a y value of 34, That is, the absolute crime risk level can be expressed by an n-dimensional vector having the number of crimes occurring in the items selected for each classification standard as the coordinates of each axis. Thus, when crime data is counted as n classification criteria, the absolute crime risk can be calculated as an n-dimensional vector.

FIG. 3 is a table showing an embodiment of a method in which the relative risk of crime is calculated. The risk calculation unit 130 of the server 100 may calculate the relative risk of crime based on user information, real-time social crime risk information, and the like. According to one embodiment of the present invention, the relative risk of crime can be calculated in the form of a vector, such as absolute crime risk.

In order to calculate the relative risk of crime, the data collection unit 120 of the server 100 obtains priority item information of the user for each classification criterion. According to an embodiment of the present invention, the priority item information can be directly selected by each user, and the selected priority item information can be received through the communication unit 110. [ According to another embodiment of the present invention, the server 100 may acquire personal information of each user, such as sex, age, occupation, residence, etc., and the data collecting unit 120 may acquire personal information The priority item information of the user can be generated.

According to the embodiment of FIG. 3, the risk calculation unit 130 calculates an added risk value for each item based on the obtained priority item information of the user. The additive risk is a value added to the corresponding item value of the absolute risk. At this time, the addition risk value may be determined to be a value within a predetermined range, and a higher value is assigned to the item as the priority of the item is higher. For example, an additive risk value of 50 is assigned to the item? 3 having a priority of 1 in the classification criterion?, And an additive risk value of 30 can be assigned to the item? 1 having a 5th priority. Likewise, an additive risk value of 50 is assigned to the item? 1 having the priority of 1 in the classification criterion?, But an additive risk value of 40 can be assigned to? 2 of the 3 rd priority.

Next, the risk calculation unit 130 may calculate the relative risk of crime using the calculated risk value for each item. At this time, the relative risk of crime can be calculated for the combination of items selected for each classification criterion of crime data as well as absolute crime risk. For example, the relative crime risk V '(α3, β2, γ1) corresponding to the absolute crime risk V (α3, β2, γ1) is calculated by combining the additive risk values 50, 50, do. Therefore, the higher the user's priority for each item, the higher the risk of relative crime.

According to an embodiment of the present invention, the risk calculation unit 130 may be expressed as an n-dimensional vector having the additive risk values in the items selected for each classification criterion as the coordinates of each axis. That is, in the embodiment of FIG. 3, the relative risk of crime V '(? 3,? 2,? 1) can be represented by a three-dimensional vector having x value of 50, y value of 40, Therefore, when the crime data is counted as n classification criteria, the relative crime risk can be calculated as an n-dimensional vector which is the same as the absolute crime risk. In this manner, the risk calculation unit 130 can calculate the relative risk of crime in the form of a vector based on the obtained priority item information of the user.

Meanwhile, according to a further embodiment of the present invention, the data collection unit 120 of the server 100 collects real-time social crime risk information using at least one of news data, social network service (SNS) data and web data And the risk calculation unit 130 can calculate the relative risk of crime using the collected social crime risk information. The crime risk notification system of the present invention can weight a socially and politically high frequency of crime, a specific type of crime that is an issue in real time, or a specific crime occurrence place.

The data collecting unit 120 may assign priorities of items in each classification criterion of the crime data based on the crime information that becomes an issue in real time. At this time, the priority of each item according to the social crime risk information can be determined based on the number of reports, the number of posts, the number of retweets, the reliability of the news media, and the like. The risk calculation unit 130 may calculate the risk value for each item based on the determined priorities.

The risk calculating unit 130 may calculate the relative risk of crime using the calculated risk value for each item calculated as described above. At this time, the risk calculation unit 130 adds up the risk value added according to the user's priority item information and the risk value added according to the social crime risk information, and calculates the relative risk of crime based on the summed risk value Can be calculated. The server 100 of the present invention can provide crime risk information of a high degree of interest to the user according to each age group, sex, nationality, etc. through the calculated relative crime risk.

4 is a table showing another embodiment of a method in which the relative risk of crime is calculated. In the embodiment of FIG. 4, a detailed description of the parts overlapping with the embodiment of FIG. 3 will be omitted.

According to the embodiment of FIG. 4, the relative risk of crime can be calculated as a weight for each item. The risk calculation unit 130 determines the weight for each item based on the obtained priority item information of the user. The weight is a value that is multiplied by the item value of the absolute risk. At this time, the weight for each item can be determined to be a value within a predetermined range, and a higher value is assigned to the item as the priority of the item is higher. For example, the weight k? 3 of the item? 3 having the priority of 1 in the classification criterion? Is assigned a value of 2, and the weight k? 1 of the item? 1 having the 5th priority can be assigned a value of 1. Likewise, a weight k ? 1 of item? 1 having a priority of 1 in the classification criterion? Is assigned a value of 2, and a weight k ? 2 of an item? 2 having a priority of 3 in a priority order can be assigned a value of 1.5.

Next, the risk calculation unit 130 may calculate the relative risk of crime using the calculated weight value of each item. At this time, the relative risk of crime can be calculated for the combination of items selected for each classification criterion of crime data as well as absolute crime risk. For example, the relative crime risk V '(α3, β2, γ1) corresponding to the absolute crime risk V (α3, β2, γ1) is calculated by combining the weights (2, 1.5, 1.5) . Therefore, the higher the user's priority for each item, the higher the risk of relative crime.

According to a further embodiment, the risk calculator 130 may further use the social crime risk information to set item-specific weights. That is, the data collecting unit 120 assigns priorities of items in each classification criterion of the crime data based on the crime information that is an issue in real time, and the risk calculating unit 130 calculates the risk The weight value of each item can be calculated. At this time, the risk calculating unit 130 may calculate the relative risk of crime based on the sum of the weight value according to the priority item information of the user and the weight value according to the social crime risk information, and based on the summed weight value have.

Meanwhile, in the embodiment of FIGS. 3 and 4, the addition risk value or the weight value for an item not included in the user's priority item may be determined as a predetermined basic value. For example, in the example of FIG. 3 and FIG. 4, the added risk value or the weight value of? 2,? 4,? 2,? 4, etc., which are not included in the priority item of the user, However, the default value of these items is not limited to 0, and may be set to a different value.

5 is a diagram illustrating a method for generating a user crime risk using absolute crime risk and relative crime risk according to an embodiment of the present invention. According to an embodiment of the present invention, the relative risk of crime can be calculated in the form of a vector as described above in Fig. 3, and the risk of user crime can be generated by a vector sum of absolute crime risk and relative crime risk.

For example, the user crime risk V * (α3, β2, γ1) for crime C (α3) at 0 to 3 o'clock (γ1) in place S2 (β2) is calculated as the absolute crime risk vector Can be generated by the vector sum of V (α3, β2, γ1) and the relative crime risk vector V '(α3, β2, γ1). That is, the coordinate value of the absolute crime risk vector (x α3, y β2, z γ1), and the relative crime risk vector V '(α3, β2, γ1) coordinate values (x a' α3, y 'β2, z' γ1) one time, the coordinate values of the user crime risk vector V * (α3, β2, γ1 ) (x * α3, y * β2, z * γ1) is (x α3 + x 'α3, y β2 + y' β2, z? 1 + z '? 1 ).

6 is a diagram illustrating a method for generating a user crime risk using absolute crime risk and relative crime risk according to another embodiment of the present invention. According to another embodiment of the present invention, the relative crime risk may be calculated as a weight value for the user priority item as described above in Fig. 4, and the user crime risk may be calculated from the absolute crime risk vector And may be generated based on a value obtained by multiplying the coordinate by the weight.

More specifically, the user crime risk V * (α5, β4, γ2) for the crime E (α5) at 4 o'clock to 6 o'clock (γ2) at the place S4 (β4) is calculated as the absolute crime risk vector V may be generated by the coordinate values obtained by multiplying each coordinate value (x α5, β4 y, z γ2) a weight value of the item (k α5, β4 k, γ2 k) of (α5, β4, γ2). That is, when the absolute coordinates of the crime risk vector (x α5, y β4, z γ2), and the relative crime risk corresponding (k α5 = 1, k β4 = 1, k γ2 = 2), the user crime risk vector V * coordinate value (x * α5, y * β4 , z * γ2) of (α5, β4, γ2) can be determined as (x α5, y β4, 2z γ2).

The server 100 may provide the user with the determined user crime risk information according to the embodiment of FIGS. At this time, the server 100 may transmit the determined user crime risk vector, or may calculate the vector size of the vector and provide it to the user. The user terminal 200 receiving the user crime risk information visualizes the corresponding information and outputs the visualized information through the output unit 220.

7 is a flowchart illustrating a method for estimating the risk of crime using Big Data according to an embodiment of the present invention. Each step of FIG. 7 can be performed by the server 100 shown in FIG. 1 or a crime risk notification system including the same.

First, the server collects crime data counted according to predetermined classification criteria (S110). The classification standard includes at least one of the type of the crime, the location of the crime and the time of occurrence of the crime, and the crime data represents the number of crimes according to each item in the classification standard.

Next, the server calculates an absolute crime risk level for a combination of items selected for each classification criterion based on the crime data (S120). The absolute crime risk is determined by a combination of the number of crimes in each item selected for each classification criterion. The absolute crime risk is determined as the higher the frequency of crime occurrence in each item is, the higher the frequency of occurrence of crime in each item, the n times the number of crimes in the selected item can be expressed as an n-dimensional vector with the coordinates of each axis.

On the other hand, the server obtains priority item information of the user for each classification criterion (S130). According to an embodiment of the present invention, the priority item information may be directly selected by each user and delivered to the server. According to another embodiment, the server may include priority item information of the user based on the personal information of each user Can be generated.

Next, the server calculates the relative risk of crime based on the obtained priority item information of the user (S140). According to one embodiment of the present invention, the relative risk of crime can be calculated in the form of a vector, such as absolute crime risk. That is, the server can calculate the risk risk value for each item based on the obtained priority item information of the obtained user, and calculate the relative risk type of the crime vector using the risk risk value. At this time, the coordinates of each axis of the relative crime risk vector are determined by the addition risk value of the selected item for each classification criterion. According to another embodiment of the present invention, the relative risk of crime can be calculated as a weight for each item.

According to a further embodiment of the present invention, the server can further collect social crime risk information in real time, and use the collected social crime risk information together with user priority item information to calculate the relative crime risk.

Thus, when the absolute crime risk and the relative crime risk are calculated, the server generates a user crime risk by combining the absolute crime risk and the relative crime risk (S150). If the relative risk of crime is calculated as a vector, the risk of user crime can be generated by a vector sum between the absolute and relative crime risk vectors. If the relative crime risk is calculated as a weight for each item, the user crime risk can be generated based on the absolute crime risk vector, the coordinates of each item multiplied by the corresponding weight.

Next, the server provides the generated user crime risk information to the user (S160). The server may transmit user crime risk information to the user terminal through the communication unit, and the user terminal receiving the information may output the user crime risk information as video and / or audio.

While the present invention has been described with reference to the particular embodiments, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the spirit and scope of the invention. Therefore, it is to be understood that those skilled in the art can easily deduce from the detailed description and the embodiments of the present invention that they fall within the scope of the present invention.

100: server 110:
120: Data collecting unit 130: Risk calculating unit
140: Big data DB 200: User terminal
210: communication unit 220: output unit
230:

Claims (8)

As a method for estimating crime risk of servers using Big Data,
The method comprising the steps of: collecting crime data counted according to a predetermined classification criterion, the classification criterion including at least one of a crime type, a place of crime occurrence and a crime occurrence time, Indicates the number of times;
Calculating an absolute crime risk level for a combination of items selected for each classification criterion based on the crime data, wherein the absolute crime risk level is an n-dimensional level of the number of crimes occurring in each item selected for each classification criterion, Calculated as a vector;
Obtaining priority item information of a user for each classification criterion;
Calculating a relative crime risk based on the obtained priority item information of the user, calculating the relative crime risk as an n-dimensional vector having each axis selected for each classification criterion, The higher the priority of the user, the higher the risk of relative crime is determined;
Generating a user crime risk by combining the absolute crime risk vector and the relative crime risk vector for each item; And
Providing the generated user crime risk information to the user;
And calculating the risk of crime using the Big Data.
delete delete The method according to claim 1,
Wherein the user crime risk level is generated by a sum of the absolute crime risk vector and the relative crime risk vector.
The method according to claim 1,
The relative risk of crime is calculated as a weight for the priority item of the user,
Wherein the user crime risk level is generated based on a value obtained by multiplying a coordinate corresponding to the priority item in the absolute crime risk vector by the weight value.
The method according to claim 1,
Further comprising the step of acquiring the personal information of the user,
Wherein the priority item information of the user is generated based on the personal information of the user.
The method according to claim 1,
Collecting real-time social crime risk information using at least one of news data, social network service (SNS) data, and web data,
Wherein the relative risk of crime is calculated using the collected social crime risk information.
As a crime risk notification system using Big Data,
A communication unit for transmitting / receiving data to / from an external terminal or a network;
Wherein the classification data includes at least one of a crime type, a place of crime occurrence and a crime occurrence time, and the crime data includes at least one of the number of crime incidents A data collection unit for acquiring priority item information of a user for each classification criterion; And
And calculating an absolute crime risk level for a combination of the items selected for each classification criterion based on the crime data, wherein the absolute crime risk level is an n-dimensional vector having the number of crimes occurrence in each item selected for each classification criterion as coordinates of each axis And calculating the relative risk of crime based on the obtained priority item information of the user, wherein the relative risk of crime is calculated as an n-dimensional vector having each axis selected for each classification criterion, Wherein the relative risk of crime is determined to be higher as the user's priority for the item is higher, and a risk calculating unit for generating a user crime risk by combining the absolute crime risk vector and the relative crime risk vector for each item; / RTI >
Wherein the system provides the generated user crime risk information to the user terminal.
KR1020150026941A 2015-02-26 2015-02-26 A Method for Estimating a Crime Risk Using Big Data and A System for Reporting the Crime Risk Using thereof KR101660160B1 (en)

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