JP2003288459A - Method and system for estimating commercial zone demand - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and system for accurately estimating the commercial zone demands of the place where a store is scheduled to be newly opened of a commercial facility or the like by searching a distance factor from a place of residence suitable for reality to the commercial facility. <P>SOLUTION: At predicting the absorption population of a store to be newly opened from the population information of each area R(i) of an area where a store is scheduled to be opened, which is divided into a plurality of areas on map data and the commercial information of a competing store in the area where the store is scheduled to be opened, a store A1 to be opened and the position data A (x1) and A(xj) of a competing store Aj and road network data such as road section data L(m) and road point data P(n) are used to calculate the shortest time distance T(xj, i) from each area R(i) of the area where the store is scheduled to be opened to each road point P(n) of the store A1 to be opened and the competing store Aj so that the absorption population of the store A1 to be opened can be estimated from the shortest time distance T(xj, i) and the population information and the commercial information. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention is a suction statistics of a store, which is carried out in order to examine the location conditions of a planned new store location such as a commercial facility, in consideration of the trade area range, the population within the area, and the influence of competitors. The present invention relates to a trade area demand estimation method and a trade area demand estimation system for estimating quantities and the like.

[0002]

2. Description of the Related Art Conventionally, when planning a new store opening for a commercial facility or the like, in order to consider the location conditions of the planned store opening site,
A trade area is set, and the population and number of households in that trade area are surveyed. In addition, the retail suction rate of the store and the absorption population of the store are considered in consideration of the influence of stores (competitor stores) of other companies in the same industry that have already opened stores in the above-mentioned trade area. We are estimating demand for commercial areas by estimating aspiration statistics. In general, the above-mentioned aspiration statistics are "the probability that a consumer will shop at a commercial facility is proportional to the size of the sales floor area of the commercial facility and inversely proportional to the time / distance to that".
That is, it is calculated by the so-called Huff model. According to the Hough model general formula, the sales floor area of the commercial facility j is S
_{Letting j} be the distance factor (traffic resistance) from the residence i to the commercial facility j, T _ij , the consumer spending ratio P _ij from the residence i to the commercial facility j can be expressed by the following formula. However, λ: traffic characteristic parameter, n: number of commercial facilities

When performing the demand estimation of the commercial area as described above,
As shown in FIG. 5, the map around the candidate site of the planned store A1 is divided into, for example, a mesh of 1 km × 1 km,
Each area is numbered, and a store A1 scheduled to open in the above area and a plurality of competitor stores Aj in the candidate site
(Here, for the sake of simplicity, only A2 and A3 are displayed.) Then, as shown by the broken line in the figure, for example, there is a store A1 scheduled to open from the center point P of the area R (p). The actual distance data to the center point of the area 2034 or the center point of the area 2233 where the competitors A2 and A3 are located is obtained, and this data is used as the distance factor T _{ij from} the residence i (area 2334) to the store A1 ( Alternatively, the ratio of consumer spending P _i1 (or P _i2 , P _i3 ) to the competitors A2 and A3) is calculated (for example, Japanese Patent Laid-Open Nos. 10-307868 and 2001-222613). Issue Bulletin).

However, the above distance factors T _ij
In the calculation method of, for example, as shown in FIG. 5, the actual position of the competitor store A2 is on the north side of the river W, but the center point of the area 2233 where the competitor store A2 is is on the south side of the river W. However, the distance factor T _ij is calculated to be shorter than the actual value, and thus the shopping dispatch ratio P _i2 becomes higher than the actual value. In addition, the competitor A2
Another competitor A on the south side of the above river W in area 2233 where
When there is 3, there is a problem that the distance factors T _i2 and T _i3 from the place of residence i (area 2334) of the competitor store A2 and the competitor store A3 become equal to each other and do not match the actual situation.
Furthermore, since many commercial facilities have a parking lot, consumers often use a car even when they are in a short distance when shopping. Therefore, when the distance factors T _ij are displayed as distance data, it is difficult to understand the actual size of the commercial area.

The present invention has been made in view of the problems of the prior art, and accurately obtains a demand for a commercial area of a new store, such as a commercial facility, by obtaining a realistic distance factor from a residential area to a commercial facility. The purpose is to provide a method and system that can be performed.

[0006]

A method for estimating a demand for a commercial area according to claim 1 of the present invention is to provide population information for each area of a store-scheduled target area divided into a plurality of areas on map data and store-opening. When estimating the aspiration population of stores scheduled to open stores from commercial information of competing stores in the planned target area, the time distance between each store and each area is calculated based on the road information in the map data. It is characterized in that the aspiration statistics such as the aspiration population of stores scheduled to open a store are estimated from the above-mentioned population information and commercial information, so that the distance factor from the place of residence to the commercial facility can be accurately determined. Therefore, it becomes possible to improve the prediction accuracy of the demand for the commercial area.

A method for estimating demand for a commercial area according to claim 2 is as follows:
In the method for estimating the demand for a commercial area according to claim 1, the area is set based on town chome administrative data in order to obtain the population information and the number of households information for each area more accurately. The method for estimating a demand for a commercial area according to claim 3 is characterized in that the time distance is corrected by road information including a size of a road and a congestion state. Can be further improved.

The commercial area demand estimation system according to a fourth aspect of the present invention includes map data including road network information,
Storage means for storing commercial information including store size of competing stores in the planned opening area and map data of the planned opening area are divided into a plurality of areas, and population information for each of the divided areas is extracted. A regional data extraction means, a time distance calculation means for calculating the time distance between each area and a store scheduled to open a store and a competing store from the map data, and a store opened from the calculated time distance and the population information and commercial information A suction statistic estimating means for estimating a suction statistic such as aspiration population of a planned store is provided to estimate a commercial area demand of the store scheduled to open.

The commercial area demand estimation system according to claim 5 uses, as the map data, a map obtained by dividing an area according to the administrative divisions of the town. The commercial area demand estimation system according to claim 6 classifies each of the areas into a plurality of distance areas according to the store to be opened and the area and the time distance calculated by the time distance calculation means, and displays the area means. It is provided so that the above areas are classified and displayed on the map for each distance range. The commercial area demand estimation system according to claim 7 classifies each of the areas into a plurality of demand areas according to the aspiration statistics of a store scheduled to open a store, which is predicted by the aspiration statistics estimation means, and is provided with a display means.
The above areas are classified and displayed according to the demand areas on the map. In order to more clearly grasp the location conditions of the store scheduled to open, the commercial area demand estimation system according to claim 8 also displays the position of the competitor store on the map.

[0010]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a configuration of a commercial area demand estimation system 10 according to the present embodiment. In FIG. 1, reference numeral 1 is a storage means for storing map data and commercial data. The road network data 1a including the data L (m) and the road point data P (n), and the position data A (x of each of the planned store opening A1 and the competitive store Aj (j ≧ 2) in the planned store opening area
Facility attribute data K such as j), name, store size, and ability to attract customers
Stored are commercial data 1b including (xj) and regional polygon data 1c including population information such as total population, number of households, and number of workers for each area R (i) on the map divided in a mesh shape. ing. 2 is area data extraction means for extracting population information for each area of the planned opening area from the area polygon data 1c stored in the storage means 1 and 3 is position data A (of the planned opening shop A1 and competitors Aj xj), road section data L (m), road point data P (n), and the like, the time distance T () between the planned store opening A1 or competitor store Aj and each road point P (n) in the planned store area xi, n), the first time distance calculating means 4 sets a plurality of time distances T _s, and the time distance T (x1, n) of the planned store A1 calculated by the time distance calculating means 3 is calculated. The time T distance area creating means 5 for creating a polygon D including the road point P (n) satisfying T (x1, n) ≦ T _s from the data The facility distance data K (xj) of the planned store A1 and the competitor Aj stored in 1 and the time distance calculation means 3 Suction coefficient calculation means for calculating the suction coefficient E (xj, i) at the planned store A1 and the competitor store Aj by using the issued time distance T (xj, i), and 6 is the suction coefficient calculation means 5. Using the calculated suction coefficient E (xj, i), the suction rate calculation means for calculating the suction rate P (x1, i) for each area R (i) of the planned store A1 that is the study facility, 7 is The suction rate P (x1, i) and each area R (i) of the planned store area extracted by the area data extracting means 2
From the total population, the number of households, the number of workers, etc. for each N (i), the suction statistics M for each area R (i) of the planned store A1
(x1, i) and suction statistic calculation means for calculating the suction statistic M (x1) which is the sum thereof. Further, 8 is an input device for inputting map data and commercial data, 9 is a polygon D of the calculated time T distance zone, and each area R (i) is the suction rate P (x1, i) and suction statistics. An output device including a display 9a for displaying an image classified by the amount M (x1, i) on a display screen and a printer 9b for printing and outputting information on the location condition of the planned store opening A1 displayed above. Is.

Next, a method of estimating and displaying the aspirating population, which is the aspiration statistical amount M (x1) of the planned store opening A1, using the above-mentioned commercial area demand estimation system will be described with reference to the flowchart of FIG. It is assumed that the storage means 1 has already stored the road network data 1a, the commercial data 1b, and the regional polygon data 1c. First,
The input device 8 selects and displays the map data of the planned store opening area on the display screen of the display 9a, and displays the position of the planned store opening A1 on the map (step S1). Next, as shown in FIG. 3, 5 k centered on the position of the planned store A1 (black circle in the center of the figure)
Concentric circles of m, 10 km, and 20 km are displayed, and the value of statistical data such as the total population, the number of households, and the number of gatherers within each distance area included in the area polygon data 1c of the planned store opening area is displayed by the area data extraction unit 2. Is displayed (step S2). Since the map data used in this example includes 1 km mesh data of all over Japan, each area R of the planned store area corresponding to each mesh on the above screen
For example, (i) can be color-coded and displayed according to the size of the total population or the like, or this screen can be output from the printer 9b (the same figure shows the distribution of the total population). In addition,
It is also possible to display the population distribution at the alternative location by changing the position of the planned store A1 and performing the same operation. As a result, one of the location conditions is 5 km, 10 km or 20 km centered on the location of the store A1 to be opened.
It is possible to accurately grasp the population distribution within the km area. At this time, if the positions of a plurality of competing stores Aj are also displayed on the map, the influence of the competing stores can be taken into consideration in the location.

After the setting of the planned location of the planned store A1 is completed, the time distance calculation means 3 stores the position data A (x1) of the planned store A1 stored in the storage means 1, and
Using the road network data 1a such as the road section data L (m) and the road point data P (n), the shortest time distance T ₀ from the store planned store A1 to each road point P (n) in the store planned area (x1, n) is calculated (step S3). In the present example, the shortest time distance T ₀ (x1, n) is set as the time for moving from the road point data P (n) to the position A (x1) of the planned store A1 using a car. At this time, the travel time is not simply a value obtained by dividing the total travel distance by an arbitrary average vehicle speed, but an actual road network data 1a such as the size of the road, the speed limit, or the congestion degree of the road is used. Calculate the travel time commensurate with the road conditions. Next, the time T distance area creating means 4
From the calculated shortest time distance T ₀ (x1, n),
A polygon D that includes a road point P (n) satisfying T ₀ (x1, n) ≦ T _s and that indicates a time T range is created (step S4).
Here, the T _s is a plurality configurable, for example, as shown in FIG. 4, the time distance from the opening will store A1 10 minutes,
A polygon D showing a time T distance range of 20 minutes and 40 minutes,
Each of the areas R (i) can be overlapped and displayed on the display screen which is color-coded according to the size of the total population. As a result, 5 points centered on the location of the planned store A1
In addition to the population distribution within km, 10 km or 20 km and the location information of competitor Aj, the size of the area where the time distance from planned store A1 is, for example, 10 minutes, 20 minutes, 40 minutes is clearly displayed. Therefore, it is possible to accurately grasp the population information including the traffic information at the planned site.

In step S5, the time distance calculating means 3 calculates the shortest time distance T (xj, i) from each area R (i) to the store planned store A1 and the competitor store Aj. The method of calculating the shortest time distance T (xj, i) is similar to the above-described shortest time distance T ₀ (x1, n), that is, the position A ( x1), or
It is time to move to the position A (xj) of the competitor store Aj. Also in this case, the travel time in consideration of the actual road condition is calculated by using the road network data 1a such as the size and speed limit of the road or the congestion degree of the road. As the road point P (n), it is preferable to use the road point P (n) with the highest frequency of use among the road points P (n) in each area R (i). In the present embodiment, the distance factor (traffic resistance) is replaced with actual distance data as in the conventional case,
For example, as shown in FIG. 5, the shortest time distances T (1, p), T (2, p), T (3, p)
The shortest time distance T (xj,
Since i), the distance factor can be accurately obtained. Further, as shown in FIG. 5, the center point P of the area R (p) is
And the distance to the center point of the area where competitor A2 is located is significantly different from the distance from the center of area R (p) to competitor A2, or multiple different competitors within the same area R (p) Even when there are A2 and A3, the distance factor from the area R (p) to the competitor stores A2 and A3 can be accurately obtained. In addition, the shortest time distance T (x1, i) of the planned store A1
May use the shortest time distance T ₀ (x1, n) calculated in step S3 as it is, instead of recalculating as described above. For details, see each area R in the target area
Of (i), the area R (i) whose center of gravity is within the polygon D is searched, and the shortest time distance T ₀ (x1, i) between the area R (i) and the planned store A1 is set to the shortest time. Let the distance be T (x1, i).

In step S6, the suction coefficient calculation means 5 causes the facility attribute data K (xj) of the planned store opening A1 and competitor store Aj stored in the storage means 1 and the above-mentioned time distance T.
(xj, i) is used to calculate the suction coefficient E (xj, i) at the planned store A1 and the competitor store Aj for each area R (i). The suction coefficient E (xj, i) is generally E (xj, i) = f (K
(xj)) · f (T (xj, i)), but in this example, “the probability that a consumer purchases a product at a store is proportional to the sales floor area S of the commercial facility, Inversely proportional to the square of the time distance of
1 and the suction coefficient E (xj, i) of the competitor Aj, E (xj, i) = S
It is calculated by _j / (T (xj, i)) ² .

Next, the suction rate calculation means 6 uses the suction coefficient E (xj, i) calculated by the suction coefficient calculation means 5 to use each area R (i) of the planned store A1 which is a study facility. The suction rate P (x1, i) for each is calculated by the following formula (step S7). However, k is the number of stores including the planned store A1. Figure 6
Is a diagram showing an example of a display screen in which each area R (i) is color-coded and displayed according to the size of the suction rate P (x1, i) calculated for each area R (i). As there are many competitors in the western area of the planned store opening, it can be quantitatively confirmed that the retail suction population cannot be expected very much.

Finally, the suction statistic calculating means 7 causes the suction rate P (x1, i) and the total population information N for each area R (i) of the planned store area extracted by the area data extracting means 2. From (i), the suction population M (x1), which is one of the suction statistics of the planned store A1, is calculated by the following equation (step S
8). FIG. 7 is a diagram showing an example of a display screen (demand area display screen) in which each area R (i) is color-coded and displayed according to the size of the total suction population M (x1, i) for each area R (i). With this, it is possible to accurately grasp the demand area, which is the distribution of the population attracting retailers in the planned store area. Therefore, by using this as basic data such as sales forecast and parking lot plan, it is possible to accurately estimate the demand for the commercial area of the planned new store location such as a commercial facility.

It should be noted that, in the above-mentioned embodiment, 1 in all over Japan.
Although the map data provided with the km mesh data is used, the map data obtained by dividing the area by the town / chome administrative division as shown in FIG. 8 may be used. According to such a division method, each area becomes an area corresponding to a residential area, a shopping district, etc., so that it is possible to more effectively use population information such as the total population, the number of households, and the number of employees. ,
The trade area demand can be estimated more accurately. Further, in the above example, an example of obtaining a suction population using the total population of each area R (i) in the planned store area extracted by the regional data extraction unit 2 as the suction statistic M (x1) of the planned store A1 The number of households, the number of workers, etc. are used as the population information N (i) of the regional polygon data, and the number of suction households, the number of suction workers, etc. are obtained and displayed or printed out. It is also possible to do so. In addition, planned opening point A1
Depending on the type of business, the consumer group may differ greatly depending on the age group and gender, so it is preferable to use more detailed statistical data as the population information N (i) of the regional polygon data. For example, if a car dealership is opened, the age is 1 rather than the suctioned population is calculated from the total population.
It is more practical to determine the aspirated population of boys aged 8 to 35.

[0018]

As described above, according to the present invention,
When estimating the attracted population of stores scheduled to open from the population information for each area of the planned store opening divided into multiple areas on the map data and the commercial information of competing stores in the target area for store opening Since the time distance between each store and each area is calculated based on the road information of the map data, and the suction statistics of the store scheduled to open are estimated from this time distance and the above-mentioned population information and commercial information. It is possible to accurately grasp the retail suction population, etc. in the area where the store will be opened. Therefore, by using this as basic data for sales forecasts, parking lot plans, etc., it is possible to accurately estimate the demand for the commercial area of the planned new store location of a commercial facility, etc., and improve the accuracy of forecasting the commercial area demand. it can.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of a commercial area demand estimation system according to an embodiment of the present invention.

FIG. 2 is a flowchart showing a commercial area demand estimation method according to the present embodiment.

FIG. 3 is an example of a display screen showing a distance area and a commercial area population of a planned store.

FIG. 4 is an example of a display screen showing a time T distance range of a planned store.

FIG. 5 is a diagram showing a method of calculating a time distance according to the present embodiment.

FIG. 6 is an example of a display screen in which the suction rate for each area is color-coded.

FIG. 7 is an example of a display screen in which the suctioned population for each area is color-coded.

FIG. 8 is a diagram showing another example of the area dividing method according to the present invention.

[Explanation of symbols]

1 storage means, 1a road network data, 1b
Commercial data, 1c area polygon data, 2 area data extraction means, 3 hour distance calculation means, 4 hour T distance area creation means, 5 suction coefficient calculation means, 6 suction rate calculation means, 7 suction statistic calculation means, 8 input device , 9 output devices, 9a display, 9b printer, 10 commercial area demand estimation system.

Claims (8)

[Claims] 1. A suction population of stores scheduled to open a store based on population information for each area of the store planned region divided into a plurality of areas on the map data and commercial information of competing stores in the region planned to open a store. When estimating, calculate the time distance between each store and each area based on the road information in the map data,
A method for estimating demand for a commercial area, characterized in that aspiration statistics of a store scheduled to open a store is estimated from the time distance and the population information and the commercial information. 2. The method for estimating demand for a commercial area according to claim 1, wherein the area is set based on town chome administrative data. 3. The method of estimating demand for a commercial area according to claim 1 or 2, wherein the time distance is corrected by road information including road size and congestion. 4. Storage means for storing map data including road network information, commercial information including store sizes of competing stores in the target store area, and map data for the target store area in a plurality of areas. Area data extraction means that divides and extracts the population information for each of the divided areas, and time distance calculation means that calculates the time distance between each area and the store scheduled to open and the competing stores from the map data, A commercial area demand estimation system comprising: an aspiration statistics amount estimating means for estimating an aspiration statistics amount of a store scheduled to open a store from the calculated time distance and the population information and the commercial information. 5. The commercial area demand estimation system according to claim 4, wherein a map obtained by dividing an area into administrative divisions is used as the map data. 6. The area is classified into a plurality of distance zones according to the store to be opened and the area and the time distance calculated by the time distance calculating means, and a display means is provided to display the area on the map. 6. The commercial area demand estimation system according to claim 4 or 5, wherein the items are classified and displayed for each distance range. 7. The areas are classified into a plurality of demand areas according to the aspiration statistics of the stores scheduled to open a store estimated by the aspiration statistics estimation means, and a display means is provided to demand the areas on the map. The commercial area demand estimation system according to claim 4 or 5, wherein the system is classified and displayed for each area. 8. The commercial area demand estimation system according to claim 4, wherein the locations of competing stores are also displayed on the map.