KR20210146182A - Commercial area analysis automation system based on web-map information algorithm - Google Patents

Abstract

The present invention relates to a commercial area analysis automation system based on web-map information algorithm. The present invention provides a commercial area analysis automation system method based on web-map information algorithm that includes: a first step of allowing a commercial area analysis server to calculate the selection probability of each store in an analysis target area including an analysis target store; and a second step of allowing the commercial area analysis server to estimate the number of available populations according to a visit rate for each population-inducing facility for each of the analysis target store and the competing store in which the selection probability is set in a preset range.

Description

Web-map information algorithm-based commercial area analysis automation system {Commercial area analysis automation system based on web-map information algorithm}

The present invention relates to a commercial area analysis of an arbitrarily specified analysis target store, and more specifically, to a web-map information algorithm-based commercial area analysis automation system for automatically evaluating how appropriate a specific commercial space is as a store for commercial use. it's about

One of the conventional market analysis methods is related to the theory of market analysis, and the other is related to a methodology using a computer.

When looking at the research on the theory of commercial district analysis, it is mainly focused on estimating the expected sales when the store to be analyzed is used for a specific purpose. Most of the models for estimating sales are in the form of estimating possible sales in consideration of the first, establishing the commercial area, second, estimating the size of users in the commercial area, and third, considering the purchasing ability of the user.

The first study on commercial district setting is based on the Huff Model, although the external form looks different. The difference between individual studies revealed in this section can be summarized as the difference in methodology presented in relation to “how to extract factors affecting the commercial district” and “how much influence the extracted factors on the selection probability”. have.

Second, research on estimating the size of users in the commercial area has been actively conducted. Research in this field can be said to have been focused on "how to segment the analysis target area" and "how to grasp the characteristics of the available population in the analysis target area".

Existing research generally divides the analysis target area into a square matrix and subdivides the area for each cell, and the available population in the cell is mainly limited to those living in houses. Accordingly, there is a limitation that the analysis of individual building units cannot be broken down and the resident personnel cannot consider those who live in business or school facilities.

On the other hand, there are studies that deal with simultaneously setting up the above three fields of business, estimating the size of users in the commercial area, and estimating possible sales in consideration of users' purchasing ability.

Many studies have been conducted in this area as well, but the limitations of these studies are that they are research or system development limited to specific uses. The common point is that it does not take into account the existing population.

In addition, if we look at the research on the method of using computers in relation to the analysis of the commercial area, it can be divided into a study on the development of a commercial area analysis system and a study on the application of a previously developed system. The former case is described in the web-based Marketing Area Analysis System (Ministry of Information and Communication's final report on excellent new technology designation support project, January 2000). It is described in the analysis of the ripple effect of large-scale complex development using MAS on the surrounding commercial area (refer to the Journal of the Korea Land and Urban Planning Association, Vol. 44 No. 1, Feb. 2009).

The limitations of the above-mentioned existing studies are mainly revealed in the available population estimation part. In other words, in estimating the available population, it is assumed that the analysis target area setting is not detailed, and that the available population in the analysis target area is induced only in houses. In addition, rigor is reduced due to excessive sales comparison, and there is no commercial area analysis system targeting stores for various purposes in various regions while operating based on the web.

Accordingly, in the technical field, there is a demand for technology development to solve the above-described problems.

The present invention is to solve the above problems, according to the user's input information on the analysis target store, commercial area information formed by stores for a specific purpose in the analysis target area, and the size of the available population of the analysis target store This is to provide an automated system for market analysis based on a web-map information algorithm to inform the comparison result with the size of the available population of other competing stores.

In addition, the present invention is to provide a web-map information algorithm-based commercial area analysis automation system that can visually show the distribution of the commercial area of a specific store and at the same time show the current state of dividing the commercial area with other competing stores.

However, the objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, the web-map information algorithm-based commercial area analysis automation system comprises a first step in which the commercial area analysis server calculates the selection probability of each store in the analysis target area including the analysis target store, and the commercial area analysis server a, a second step of estimating the number of available populations according to the visit rate for each population-inducing facility for each of the stores to be analyzed and the competing stores in which the selection probability is set in a preset range.

The first step is, the commercial area analysis server receiving, from the GIS server, new address map information of a predetermined radius or more including the analysis target store selected by the user of the user terminal, from the GIS server, the commercial area analysis server, the Receiving the type and location of the building in which the analysis target store is located from the user terminal according to a user's selection and specifying it on the new address map information, the commercial area analysis server maps the new address according to the user's input Receiving information from the user terminal a factor affecting the selection for each building, and the commercial area analysis server receiving the coefficient value of the factor, forming a linear regression equation and calculating the selection probability.

And, in the second step, the business district analysis server sets the business district for the business district constituting the analysis target store in the analysis target area according to the selection probability, and the competing stores in which the selection probability is included in a preset ranking. a setting step and an estimating step of estimating, by the commercial district analysis server, the number of available populations for each of the commercial districts according to a visit rate for each population-inducing facility for the analysis target store and the competitive store.

On the other hand, in the web-map information algorithm-based commercial area analysis automation system, the commercial area analysis server compares and displays the available population of the analysis target store and the available population of the competitive store, which is performed after the estimation step. may contain more

For each population-inducing facility, the visit rate is set by classifying it into residential, business, and school facilities, and the factors are the distance to the store to be analyzed, the number of floors where the store to be analyzed is located, and the area of the store to be analyzed. , it is set as the number of parking spaces possessed by the store building to be analyzed.

In the web-map information algorithm-based commercial area analysis automation system according to another embodiment of the present invention, the user terminal transmits the selection of new address map information equal to or greater than a preset radius including the analysis target store to the commercial area analysis server, A first step of receiving the new address map information from the GIS server by the analysis server, the user terminal transmits the type and location of the building in which the analysis target store is located according to the user's input to the commercial area analysis server In the second step of specifying on the new address map information, the user terminal constructs the analysis target store according to the selection probability calculation that affects the selection of each building in the new address map information by the commercial area analysis server The third step of completing the setting of the commercial district for the commercial district and the competitive store of the analysis target store, and the user terminal, by the commercial district analysis server, the visit rate by population-inducing facility to the store store analyzed and the competitive store Accordingly, when the estimation of the available population for each of the commercial districts is completed, a fourth step of receiving and outputting the available population of the analysis target store and the competitive store from the commercial district analysis server is included.

Here, in the fourth step, the user terminal receives an analysis table comparing the available population of the analysis target store and the available population of the competitive store and the set commercial area information, and receives and displays from the commercial area analysis server do.

In order to achieve the above object, the web-map information algorithm-based commercial area analysis automation system is a commercial area analysis automation system in which a user terminal and a commercial area analysis server are connected through a communication network, wherein the commercial area analysis server includes an analysis target store and a selection probability calculation module for calculating the selection probability of each store in the region, and an available population calculation module for estimating the number of available populations according to the visit rate for each population-inducing facility for the analysis target store, which is the store with the highest selection probability.

The commercial area analysis server, when receiving new address map information of a radius greater than a preset radius including the analysis target store selected by the user of the user terminal, from the GIS server, the analysis target store from the user terminal according to the user's selection It further includes an information extraction function module for receiving the type and location of the building in which is located and specifying on the new address map information.

And, the selection probability calculation module receives the factors and coefficient values affecting the selection of each building in the new address map information according to the user's input from the user terminal, and forms a linear regression equation according to the factors. The selection probability is calculated by inputting the coefficient values into the linear regression equation.

On the other hand, the business district analysis server, according to the selection probability, the business district constituting the analysis target store in the analysis target area, and the business district setting function module for setting the business district for the competing stores included in the selection probability in a preset ranking. may include

The available population calculation module includes a first available population calculation module for estimating the number of available population for a commercial area formed according to the visit ratio for each population-inducing facility to the analysis target store, and a visit rate for each population-inducing facility for the competitive store It can be divided into a second available population calculation module for estimating the number of available population for the commercial district formed according to .

The web-map information algorithm-based commercial area analysis automation system according to an embodiment of the present invention includes commercial area information formed by stores for a specific purpose in the analysis target area according to the user's input information for the analysis target store, and the analysis target store It has the effect of providing a result of comparing the size of the available population of the store with the size of the available population of other competing stores.

In addition, the web-map information algorithm-based commercial district analysis automation system according to another embodiment of the present invention can visually show the distribution of the commercial district of a specific store, and at the same time output the status of dividing the commercial district with other competing stores. It works.

1 is a diagram showing a web-map information algorithm-based commercial area analysis automation system according to an embodiment of the present invention.
FIG. 2 is a diagram showing the configuration of a commercial area analysis server in the web-map information algorithm-based commercial area analysis automation system of FIG. 1 .
3 to 6 are diagrams showing a user interface screen in which the information input process of the user terminal and the analysis result output process of the commercial district analysis server of the web-map information algorithm-based commercial area analysis automation system of FIG. 1 are implemented in the user terminal.
7 is a flowchart illustrating a web-map information algorithm-based commercial area analysis automation system according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A detailed description of a preferred embodiment of the present invention will be described with reference to the accompanying drawings. In the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

In the present specification, when one component 'transmits' data or signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component This means that data or signals can be transmitted to other components.

1 is a diagram showing a web-map information algorithm-based commercial area analysis automation system according to an embodiment of the present invention, and FIG. 2 is a diagram showing the configuration of a commercial area analysis server in the web-map information algorithm-based commercial area analysis automation system of FIG. am.

1 and 2 , the web-map information algorithm-based commercial area analysis automation system includes a user terminal 10 , a communication network 20 , a commercial area analysis server 30 and a GIS server 40 .

The user terminal 10 accesses the commercial area analysis server 30 through the communication network 20 and transmits input information. Here, the input information is “selection of new address map information” with information on the use of all stores in the complex and information on the number of houses in individual polygons that display residential buildings, and the stores to be analyzed. There can be four types of "type and location" of the , "coefficient value expressing factors and their influence" to be included in the linear regression equation, and "visit rate by population-inducing facility".

Accordingly, the user terminal 10 receives and outputs the commercial area information that visually expresses the commercial area of the same type of store, including the analysis target store and the competitive store in the analysis target area, from the commercial area analysis server 30 through the communication network 20. do.

In addition, the user terminal 10 receives and outputs the analysis table indicating the size of the available population of the analysis target store and the competitive store of the same industry from the commercial area analysis server 30 through the communication network 20 .

The communication network 20 is a high-speed backbone network of a large-scale communication network capable of large-capacity, long-distance voice and data services, and may be, for example, the Internet. In addition, the communication network 20 may be a next-generation network for providing high-speed multimedia services based on ALL IP (Internet Protocol). The communication network 20 serves to mutually transmit signals and data between the user terminal 10 , the commercial area analysis server 30 , the GIS server 40 , and other systems.

The commercial area analysis server 30 receives, from the GIS server 40 , new address map information including more than a preset radius including the analysis target store selected by the user of the user terminal 10 . Here, the new address map information includes information on the number of houses present in the polygons indicating that they are stores and houses to be analyzed.

Here, the GIS server 40 transmits the selection information of the new address map information to the business district analysis server 30 of the user terminal 10, and the business district analysis server 30 receives the new address map information for the analysis target area. If requested, it is returned.

Thereafter, the commercial area analysis server 30 specifies the type and location of the analysis target store according to an input from the user of the user terminal 10 .

Depending on the type and location specification, the business district analysis server 30 additionally inputs a factor determining a linear regression equation to be described later that affects the selection of a store to be analyzed in addition to the information already included in the received new address map information and its coefficient value to the user. It is received from the terminal 10 .

The market analysis server 30 constitutes a linear regression equation. More specifically, the commercial area analysis server 30 receives the factors affecting the selection probability constituting the linear regression equation and the coefficient values thereof from the user terminal 10 .

The commercial area analysis server 30 calculates the selection probability for each building in which stores including the analysis target store included in the analysis target area are located according to a linear regression equation.

Here, the reason why the commercial area analysis server 30 calculates the selection probability is to estimate the number of available population that the analysis target store of the specified location can secure by determining the store visit rate for each population-inducing facility. On the other hand, population-inducing facilities can be classified into residential facilities, business facilities, school facilities, and the like.

Thereafter, the commercial area analysis server 30 compares and displays the available population of the analysis target store with the available population of the nearby competitive store. Accordingly, the user of the user terminal 10 is able to estimate the competitiveness of the analysis target store.

Hereinafter, with reference to FIG. 2 , the process of estimating the number of available population of the commercial district analysis server 30 will be reviewed with a focus on the configuration of the commercial district analysis server 30 .

The business district analysis server 30 in the web-map information algorithm-based commercial district analysis automation system includes an information extraction function module 31, a selection probability calculation module 32, and a commercial district to analyze the web-map information algorithm-based store aptitude. Includes a setting function module 33 , a visit rate application module 34 , a first available population calculation module 35 , a second available population calculation module 36 , a comparison module 37 , and a database 38 . do.

The information extraction function module 31 performs a function of extracting from the new address map information necessary for the linear regression calculation for calculating the selection probability for each building in the analysis target area including the analysis target store.

Specifically, the information extraction function module 31 extracts the information necessary for the linear regression calculation for calculating the selection probability for each individual building in the analysis target area from the new address map information, so that the user of the user terminal 10 is required. make it available for viewing.

Here, information required for linear regression calculation is largely classified into two types.

First, it is the distance information from the individual building, which is the starting point, to the building where the analysis target store is located within the analysis target area, and the distance information is automatically calculated along the road network connecting each building.

The second is attribute information of individual stores in the analysis target area. Examples of the attribute information of individual stores may be the number of floors on which the store is located, area information of each store, and the like.

On the other hand, as an additional function of the information extraction function module 31, input from a user of the user terminal 10 or information not included in the new address map information from a phone book storage medium such as a CD phone book is stored in the new address map information. It can be added to the information table.

Here, in the case of the new address map information, since the representative use of the first floor is expressed as the use of the building, the added information may be the use of stores included in the building in the case of a multifunctional building.

That is, in the case of a multifunctional building, the information extraction function module 31 may extract information on the use of stores on the remaining floors except for the first floor and add it to the information table of the new address map information.

More specifically, the information extraction function module 31 directly displays the use in the building use field of the information table in such a way that, in the case of a single functional building, the use of the building is specified in the building use field (BD_KD) of the information table in the new address map. do.

On the other hand, in the case of a multifunctional building, the information extraction function module 31 may link a separate function table to the building use field, and display all uses and ancillary information of the building in the function table. Here, the accessory information may be the number of storey floors, the area of the store, the number of parking areas owned by the store building, and the like.

The selection probability calculation module 32 performs a function of calculating the selection probability for each individual building including the analysis target store in the analysis target area. The selection probability calculation module 32 receives the factors and coefficient values for calculating the selection probability input by the user of the user terminal 10 from the user terminal 10, and forms a linear regression expression using the factors, By inputting the coefficient values into the linear regression equation, the selection probability for each building (and/or each store) is calculated.

The commercial district setting function module 33 focuses on the stores to be analyzed and the competitive stores whose selection probability for each building in the analysis target area is included in the preset ranking according to the selection probability calculated by the selection probability calculation module 32. It performs the function of setting the commercial area. The commercial district setting function module 33 may display each of the different colors according to the set commercial district information.

On the other hand, the factors that have a major influence on the selection probability are the distance to the store to be analyzed, the number of floors where the store to be analyzed is located, the area of the store to be analyzed, the number of parking spaces owned by the building of the store to be analyzed, etc. Each coefficient has a value for each factor.

In addition, depending on the characteristics of the analysis target, the degree of activation of the surrounding commercial zone or the period of existence of the commercial zone may be included.

The visit rate application module 34 performs a function of applying the visit rate to stores having the same type of business for each individual building in the analysis target area.

The visit rate application module 34 extracts usage information and ancillary information, and then applies the visit rate to the stores to be analyzed and the competing stores, which are stores that form the commercial district set by the commercial district setting function module 33 .

On the other hand, the visit rate is collected in the same way as a survey and stored in advance in the database 38 , or is input by a user of a user terminal 10 that can be formed in plurality, and the commercial area analysis server 30 through the communication network 20 After being received as , statistical values are calculated and stored in the database 38 can be used.

The first available population calculation module 35 calculates the size of the available population of the analysis target store in the analysis target area according to the visit ratio output from the visit ratio application module 34 .

The second available population calculation module 36 calculates the size of the available population of the store to be analyzed in the analysis target area according to the visit ratio output from the visit ratio application module 34 and the competing store competing with each other.

The comparison module 37 compares and outputs the size of the available population of the store to be analyzed with the size of the available population of the competing store.

3 to 6 show the information input process of the user terminal 10 of the web-map information algorithm-based commercial area analysis automation system of FIG. 1 and the analysis result output process of the commercial area analysis server 30 are implemented in the user terminal 10 It is a diagram showing the user interface screen.

1 to 6, the commercial area analysis server 30 transmits the analysis result using the input information received from the user terminal 10 and the data stored in the database 38 to the user terminal 10 to the user terminal ( 10) to be output.

Referring to FIG. 6 , the analysis results are largely expressed in two formats.

First, it visually expresses the commercial area of the stores in the same industry, including the stores to be analyzed and their competitors in the area to be analyzed. Second, the size of the available population of the analysis target store and its competitor store is compared and output as an analysis table.

In this example, the type of store to be analyzed is set as a beauty salon as shown in FIG. 4 . The selection of the analysis target building, which is the building in which the analysis target store is located, is possible in two ways. The first selection method of clicking a building on the screen showing the new address map information and the second selection method of inputting the address of the store to be analyzed can be used.

The building to be analyzed selected according to this selection method is indicated by a red circle on the new casting map information of FIG. 4 . That is, according to the selection of the analysis target building, the location and use are displayed.

FIG. 5 is a diagram for explaining a process of inputting a factor to be used in a linear regression equation by a user of the user terminal 10 and a coefficient value thereof.

The user of the user terminal 10 is provided with a screen as shown in FIG. 5 and selects or inputs factors and coefficient values for the linear regression equation.

That is, as described above, since two things are required to determine the linear regression equation, the first decision process of setting the factors constituting the linear regression equation from a number of expected factors and the coefficient values indicating the magnitude of the influence of each factor are set It is formed by a second crystallization process.

In the present invention, the predictive factors can be set in advance through a survey targeting a group of professionals (eg, real estate brokers) who are active in the analysis target area.

More specifically, the user of the user terminal 10 may select factors corresponding to the needs of the region from among the 10 previously given expected factors, and may optionally input a coefficient value for each factor according to the selection.

On the other hand, the composition of the linear regression formula is completed by performing regression analysis for each region and purpose of analysis. is set as a factor that has a major influence on store selection.

The results of the regression analysis show that the distance to the store and the number of store floors act as significant factors among the four factors predicted by the professional population as major factors.

In addition to inputting the factors and coefficients of the linear regression equation, FIG. 5 also shows the input of the visit rate by the user of the user terminal 10 .

Referring to FIG. 5 , as the last step of constructing input information, a visit rate for each population-inducing facility is set.

The visit rate varies depending on the purpose and location of the store, and it is possible to determine the visit rate by conducting a survey for each individual case. can

In FIG. 5 , a process of setting a visit rate can be seen. Residential facilities, business facilities, and school facilities were considered as population-inducing facilities. In FIG. 5 , it is shown that 80% of store visitors departed from residential facilities and 20% from business facilities according to survey results or statistical values according to input from a plurality of user terminals. Therefore, the ratio of visits to residential facilities is set to 1.0, the ratio to visits to business facilities is 0.25, and the ratio of visits to school facilities is set to 0.0.

Referring to FIG. 6 , a user interface screen in which the analysis result is transmitted to the user terminal 10 by the commercial area analysis server 30 and output to the user terminal 10 is shown. The analysis results are output in two ways.

First, it shows the commercial district of stores with specific uses in the analysis target area. As can be seen from the analysis result of FIG. 5 , the commercial area of the store to be analyzed may be expressed in pink, and the commercial areas of the remaining competitive stores may be expressed in different colors (yellow, blue, green, and blue).

Second, the size of the available population of the analysis target store is compared with the size of the available population of the competitive store of the analysis target store, and it can be displayed as an analysis table as shown in FIG. 5 .

By comparing the size of the available population, this analysis result can be used to estimate the sales level (how well the business will be) of the store to be analyzed. In other words, it cannot be evaluated that a certain amount of sales can be expected, but it is possible to evaluate that any nearby stores can be operated. This allows you to evaluate the suitability of a particular store for a particular use.

On the other hand, the commercial area analysis automation system having the above configuration is not limited to a specific location or a store for a specific purpose, but targets all industries and aims at a system that can be used for all regions.

The automated business district analysis system takes advantage of the fact that sales are determined by the size of the available population in the commercial district.

In other words, the commercial district analysis automation system must analyze the commercial district in order to know the size of the available population in the commercial district. Accordingly, it is necessary to know the size of the population-inducing facilities existing in the commercial area and the rate of visits to specific stores for each population-inducing facility. Therefore, the automated business district analysis system uses a linear regression formula that determines the probability of selecting a store to be analyzed and the visit rate of the store to be analyzed depending on the location and use. Take advantage of the fact that it will vary.

Therefore, the web-map information algorithm-based automated business district analysis system according to the present invention uses a linear regression formula and a visit rate suitable for the location and purpose of the store to be analyzed.

7 is a flowchart illustrating a web-map information algorithm-based commercial area analysis automation system according to an embodiment of the present invention. 1 to 7 , the commercial area analysis server 30 receives new address map information including more than a preset radius including the analysis target store selected by the user of the user terminal 10 from the GIS server 40 . (S10).

Here, the commercial area analysis server 30 may add information not included in the new address map information to the information table of the new address map information. In the case of the new address map information, since the representative use of the first floor is expressed as the use of the building, the added information may be the use of stores included in the building in the case of a multifunctional building.

The business district analysis server 30 may extract the store usage information from a phone book storage medium such as a CD phone book and automatically add it to the information table of the new address map information for the store information for the rest of the purposes.

After the step (S10), the commercial area analysis server 30 specifies the type and location of the analysis target store according to an input from the user of the user terminal 10 (S20).

The commercial area analysis server 30 receives the factors and coefficient values forming a linear regression equation for calculating the selection probability for each building from the user of the user terminal 10 (S30).

Accordingly, the commercial area analysis server 30 forms a linear regression equation using the factors, and calculates a selection probability for each building included in the analysis target area by inputting a coefficient value into the linear regression equation.

After the step (S30), the commercial area analysis server 30 sets the commercial area around the stores to be analyzed in the analysis area, and the competing stores whose selection probability calculated in step (S30) is included in the preset ranking. In the commercial district setting, a Huff Model according to the prior art can be used.

In an embodiment of the present invention, the business district analysis server 30 may display each of the different colors according to the set commercial district distribution.

The commercial area analysis server 30 estimates the number of available population that can be secured by the analysis target store in the specified location by determining the visit rate of the analysis target store and the competitive store for each population-inducing facility (S50).

The commercial area analysis server 30 performs a function of applying the visit rate by extracting the use information of the building for each individual building in the analysis target area.

Accordingly, the commercial area analysis server 30 transmits an analysis table formed by comparing the estimated available population with the available population of a nearby competitive store to the user terminal 10 to display it to the user.

More specifically, the commercial area analysis server 30 performs a function of calculating the size of the available population in each set commercial area, and performs a function of calculating the size of the available population of competing stores in the analysis target area. Thereafter, the commercial area analysis server 30 performs a function of comparing and outputting the size of the available population in the commercial area of the store to be analyzed with the size of the available population in the competitive store in the area to be analyzed.

Accordingly, the user of the user terminal 10 is able to estimate the competitiveness of the store.

The present invention can also be implemented as computer-readable codes on a computer-readable recording medium.

The computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored.

In addition, the computer-readable recording medium is distributed in network-connected computer systems, and computer-readable codes can be stored and executed in a distributed manner. And functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention pertains.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms are used, these are only used in a general sense to easily explain the technical content of the present invention and help the understanding of the present invention. , it is not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

10: user terminal 20: communication network
30: commercial area analysis server 31: information extraction function module
32: selection probability calculation module 33: commercial area setting function module
34: visit ratio application module 35: first available population calculation module
36: second available population calculation module 37: comparison module
38: database 40: GIS server

Claims (12)

In the web-map information algorithm-based commercial area analysis automation system, the first step of the commercial area analysis server calculating the selection probability of each store in the analysis target area including the analysis target store, and the commercial area analysis server, the analysis target store and a second step of estimating the number of available populations according to the visit rate for each population-inducing facility for each of the competing stores in the predetermined range of the selection probability, wherein the first step is that the commercial area analysis server is Receiving new address map information with a radius greater than or equal to a preset radius including the analysis target store selected by the user from a GIS server, the commercial area analysis server, the analysis target store is located from the user terminal according to the user's selection receiving the type and location of a building and specifying it on the new address map information; Web-map information algorithm-based commercial area analysis automation system, comprising the steps of: receiving, and the commercial area analysis server receiving the coefficient values of the factors and calculating the selection probability by forming a linear regression equation. The method according to claim 1, wherein in the second step, the commercial area analysis server, the commercial area constituting the analysis target store in the analysis target area according to the selection probability, and the selection probability for competitive stores included in a preset ranking A setting step of setting up a commercial district and an estimation step of estimating, by the commercial district analysis server, the number of available population for each of the commercial districts according to the visit rate for each population-inducing facility for the store to be analyzed and the competitive store A web-map information algorithm-based commercial area analysis automation system. The web according to claim 2, further comprising the step of comparing and displaying, by the commercial area analysis server, the number of available population of the analysis target store and the available population of the competitive store, which is performed after the estimation step - Automated market analysis system based on map information algorithm. [Claim 3] The system according to claim 2, wherein the rate of visitation is set by classifying the population-inducing facilities into residential facilities, business facilities, and school facilities. The method according to claim 1, wherein the factor is set as the distance to the store to be analyzed, the number of floors on which the store to be analyzed is located, the area of the store to be analyzed, and the number of parking spaces possessed by the building of the store to be analyzed. A web-map information algorithm-based commercial area analysis automation system. A first step in which the user terminal transmits the selection of new address map information with a radius greater than or equal to a preset radius including the store to be analyzed to the commercial area analysis server, and receives the new address map information from the GIS server by the commercial area analysis server , a second step in which the user terminal transmits the type and location of the building in which the analysis target store is located to the commercial area analysis server according to the user's input to specify it on the new address map information, the user terminal, A third to complete the setting of commercial districts for the business district constituting the analysis target store according to the selection probability calculation affecting the selection by building in the new address map information by the commercial district analysis server and the competing stores of the analysis target store When the estimation of the number of available population for each of the commercial districts is completed, the analysis target store and the A web-map information algorithm-based commercial district analysis automation system, comprising a fourth step of receiving and outputting the number of available populations of competing stores from the commercial district analysis server. The business district analysis server of claim 6, wherein in the fourth step, the user terminal receives an analysis table comparing the number of available population of the analysis target store and the available population of the competitive store and set commercial district information Web-map information algorithm-based commercial area analysis automation system, characterized in that it is received from and displayed. In a commercial area analysis automation system in which a user terminal and a commercial area analysis server are connected through a communication network, the commercial area analysis server includes a selection probability calculation module for calculating the selection probability of each store in the analysis target area including the store store for analysis. New address map information over a preset radius including an available population calculation module for estimating the number of available population according to the visit rate for each population-inducing facility for the analysis target store, which is the highest level store, and the analysis target store selected by the user of the user terminal When receiving from the GIS server, according to the user's selection, the type and location of the building in which the analysis target store is located is received from the user terminal, and an information extraction function module for specifying it on the new address map information; The selection probability calculation module receives, from the user terminal, a factor and a coefficient value affecting the selection of each building in the new address map information according to the user's input, and forms a linear regression equation according to the factor to form a linear regression A web-map information algorithm-based commercial area analysis automation system, characterized in that the selection probability is calculated by inputting the coefficient value into an equation. The method according to claim 8, wherein the business district analysis server, according to the selection probability, the business district constituting the analysis target store in the analysis target area, and the business district setting function for setting the business district for the competitive stores included in the selection probability in a preset ranking Web-map information algorithm-based commercial area analysis automation system, characterized in that it further comprises a module. The method according to claim 9, wherein the available population calculation module, a first available population calculation module for estimating the number of available population for a commercial area formed according to the visit ratio for each population-inducing facility to the analysis target store, and the population for the competing store A web-map information algorithm-based commercial area analysis automation system, characterized in that it comprises a second available population calculation module for estimating the number of available population for the commercial area formed according to the visit rate for each inducement facility. 8, 9. The web-map information algorithm-based commercial area analysis according to any one of claims 10 to 10, wherein the visit rate is set by classifying the population-inducing facilities into residential facilities, business facilities, and school facilities. automation system. 8. The method of claim 10, wherein the factors include a distance to the store to be analyzed, the number of floors on which the store to be analyzed is located, the area of the store to be analyzed, and parking owned by the building of the store to be analyzed. Web-map information algorithm-based commercial area analysis automation system, characterized in that it is set by the number of sides.

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