Method for calibrating new business district, storage medium and system

Abstract

The invention relates to the technical field of data processing, in particular to a method for calibrating a new business district, a storage medium and a system. According to the method for calibrating the new business district, the high-density area is recognized through kernel density analysis of POI point location data, the initial business district contour is formed according to the recognized high-density area to confirm the business district range, and compared with a large amount of manual intervention in the existing business district contour defining process, the business district contour is automatically achieved, and the efficiency is high. According to the method, the defects of manual business district delimiting are reduced, the POI store technology is combined with the passenger flow data to delimit the new business district, the score of the POI point location is combined with the passenger flow data of the business district shopping center, the business district score is formed, the business district is graded, and the deviation of calibrating the business district by single POI data is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, storage medium and system for calibrating a new business district. Background Art

[0002] With the rapid development of information technology and the advent of the big data era, urban point of interest (POI) data has gradually become an important tool for studying the characteristics of urban business districts and evaluating the vitality of business districts. POI data, as an important part of urban spatial information, not only records the location and attributes of various facilities in the city, but also implies information on urban spatial structure, population distribution, economic activities, and other aspects. Through in-depth analysis of POI data, we can more accurately grasp the composition, characteristics, and evolution of business districts, and conduct a comprehensive evaluation of the vitality, competitiveness, and development potential of business districts. Although POI data has significant advantages in business district evaluation, it still faces some difficulties. For example, due to the complexity of data collection and processing, POI data may have errors and omissions, resulting in analysis results deviating from the actual situation, especially in the calibration of new business districts. Some emerging formats or small commercial facilities may not be included in the POI database in a timely manner, resulting in the underestimation of the diversity and vitality of the business district, resulting in large deviations in the calibration of new business districts. Therefore, there is an urgent need for an optimization method that can calibrate business districts with existing POI data. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to identify the core competing products in a business district.

[0004] In order to solve the above technical problems, the present invention provides a method for marking a new business district, comprising the following steps executed by a computer program: S1. Calling the multi-format POI point data of the target area, performing kernel density analysis on the obtained POI point data to identify high-density areas, and obtaining the business district scope of all the identified high-density areas; S2. Call the map data of the historical business district library and compare it with these business district ranges. If the overlap between the business district range and all the business districts in the historical business district library is lower than the preset degree, the business district range is used as the new target project; S3. Obtain all POI point data of the target project, identify the target store, calculate the aggregation value of the identified target store, and use the sum of the aggregation values ​​of all target stores as the total POI score; S4. Obtain the passenger flow data within the business district and the passenger flow data of multiple similar business districts, calculate the ratio of the passenger flow data within the current business district to the passenger flow data of similar business districts, and multiply the ratio by 100 as the total passenger flow score; S5. respectively confirm the weights of the POI total score and the passenger flow total score, denoted as W_A and W_B, W_A+W_B=1, calculate the weighted values ​​according to the values ​​of the POI total score and the passenger flow total score and the corresponding weights, and take the sum of all weighted values ​​as the business district score of the current target project. If the business district score reaches a preset threshold, the target project is marked as a new business district.

[0005] Furthermore, in step S1, performing kernel density analysis on the acquired POI point data to identify high-density areas is specifically as follows: Identify the business type of the acquired POI point data, extract all POI point coordinates of the M business types with the largest number of POI points in the target area, and calculate the regional density of each of these M business types within the unit area using the following kernel density analysis KDE formula: ; f(x) represents the density estimation at position x, n is the number of POI points, h is the bandwidth parameter of kernel density analysis, K(·) is the Gaussian kernel function, and x i is the coordinates of the i-th POI point of this business type; Calculate the sum of the regional densities of M business types within the unit area. If the sum of the calculated regional densities reaches a preset saturation threshold, the geographical area corresponding to the unit area is considered to be a high-density area. If multiple adjacent unit area ranges are high-density areas, these unit area ranges are merged into one high-density area.

[0006] Furthermore, in the step S1, the business district scope of each of the identified high-density areas is obtained by constructing a heat map of the number of POI points for each high-density area to identify the commercial activity level of each high-density area, connecting the outermost areas of each high-density area where the commercial activity level reaches a preset value to construct a maximum outer contour, and using the inner area of ​​the maximum outer contour as the business district scope of the high-density area.

[0007] Furthermore, all POI points whose distance from the maximum outer contour is within a preset range are extracted, and the regional influence of each POI point is calculated according to the brand type and commercial value of each POI point. If the regional influence reaches a preset level, the POI point is included in the business district; the commercial value includes the brand popularity of the brand to which the POI point belongs in the target area and the popularity of the shopping mall associated with the POI point.

[0008] Furthermore, in step S2, the overlap degree being lower than a preset degree means that: the overlap degree of the coordinates of the business district range with any business district in the historical business district library is lower than the overlap threshold R1, or the sum of the overlap degrees of the coordinates of the business district range with multiple business districts in the historical business district library is lower than the overlap threshold R2, and the overlap threshold R2 is slightly greater than the overlap threshold R1.

[0009] Furthermore, in step S3, the target stores include business district stores and street shops whose POI point popularity reaches a preset level; the POI point popularity is the sum of the search volume statistics, social media interaction values ​​and public opinion monitoring scores.

[0010] Furthermore, calculating the cluster value of the identified target store means: obtaining the brand index corresponding to the target store, calling the pre-stored expert's score for each brand index, and using the hierarchical analysis method AHP to calculate the comprehensive score of the target store on these brand indicators, which is recorded as the cluster value; The brand indicators include a variety of the following indicators: whether the POI brand is a national chain brand, whether the POI brand is a regional chain brand, whether the POI brand is a local chain brand, the total number of POI brand stores, the proportion of POI brand stores in the same city, the POI brand level, the popularity value of POI brand social software, and the weight of the business format to which the POI belongs.

[0011] Furthermore, in step S4., obtaining passenger flow data within the business district refers to: obtaining the population density of each business district hotspot area, and calculating the expected passenger flow data from each business district hotspot area in combination with the radiation probability of the business district to each business district hotspot area.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method for calibrating a new business district as described above are implemented.

[0013] The present invention also provides a business district marking system, comprising interconnected processors and the computer-readable storage medium as described above.

[0014] The present invention has the following beneficial effects: the method for calibrating a new business district identifies high-density areas through kernel density analysis of POI point data, forms an initial business district outline based on the identified high-density areas to confirm the scope of the business district. Compared with a large amount of manual intervention in the existing business district outline definition process, the business district outline of the present invention is automatically realized, reducing the shortcomings of manual demarcation of business districts, and uses POI store technology combined with passenger flow data to demarcate new business districts, combines the scores of POI points with the passenger flow data of the business district shopping center, forms business district scores and grades the business districts, reducing the deviation of single POI data in calibrating business districts. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a block diagram of the calculation method of the business district score for calibrating the new business district. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below in conjunction with specific implementation methods.

[0017] This embodiment provides a business district marking system, which includes a processor and a computer-readable storage medium connected to each other. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the following is implemented: Figure 1 The method for calibrating a new business district shown includes the following steps.

[0018] S1. Call the multi-format POI point data of the target area, perform kernel density analysis on the obtained POI point data to identify high-density areas, and obtain the business district ranges of all the identified high-density areas.

[0019] The specific process of business district extraction is as follows: collect POI point data and use GIS software (such as ArcGIS Pro) to import the data for geocoding and spatial matching to ensure that each POI point accurately corresponds to the geographic coordinates; then, preprocess the data, including outlier detection, missing value filling, etc., to ensure the accuracy of the analysis; then, conduct statistics on POI points of various business formats in the urban grid - kernel density analysis - high-density area identification.

[0020] In step S1, kernel density analysis is performed on the acquired POI point data to identify high-density areas. Specifically: Identify the business type of the acquired POI point data, extract all POI point coordinates of the M business types with the largest number of POI points in the target area, and calculate the regional density of each of these M business types within the unit area using the following Kernel Density Estimation (KDE) formula. ; f(x) represents the density estimation at position x, n is the number of POI points, h is the bandwidth parameter of kernel density analysis, K(·) is the Gaussian kernel function, and x i is the coordinates of the i-th POI point of this business type; Calculate the sum of the regional densities of M business types within a unit area. If the sum of the calculated regional densities reaches the preset saturation threshold, the geographical area corresponding to the unit area is considered to be a high-density area. If multiple adjacent unit areas are high-density areas, these unit areas are merged into one high-density area.

[0021] This embodiment uses the kernel density analysis KDE formula to superimpose the contributions of all kernel functions to obtain an overall density estimate. This superposition method allows each data point to contribute to the final density estimate, but the size of the contribution depends on its distance from other data points and the shape of the kernel function. Therefore, KDE can generate a smoother and more accurate density curve.

[0022] In step S1, the business district scope of each of the identified high-density areas is obtained by constructing a heat map of the number of POI points for each high-density area to identify the commercial activity of each high-density area, connecting the outermost areas of each high-density area whose commercial activity reaches a preset value to construct a maximum outer contour, and taking the inner area of ​​the maximum outer contour as the business district scope of the high-density area. The business district contour of this embodiment is automatically realized, reducing the shortcomings of manually delineating the business district.

[0023] By collecting POI point data, this embodiment can analyze the geographical distribution and density, thereby identifying business district hot spots and heat maps to show the commercial activity levels of different areas, and then identify high-density areas. In addition, by using the edge of the area where the activity level reaches a preset range as the initial business district outline, the scope of the business district can be accurately confirmed.

[0024] Furthermore, this embodiment extracts all POI points within a preset range of the distance from the maximum outer contour, calculates the regional influence of each POI point according to its brand type and commercial value, and includes the POI point in the business district if the regional influence reaches a preset level; the commercial value includes the brand popularity of the brand to which the POI point belongs in the target area and the shopping center popularity associated with the POI point. Specifically, all POI points within the preset range of the edge of the initial business district contour obtained are called, including but not limited to location coordinates, names, classification codes, etc. Brand attribute annotation: For each POI point, clarify the brand it belongs to, and mark whether the brand belongs to a well-known chain or a specific category (such as catering, retail, entertainment, etc.). At the same time, record whether the POI is located in a shopping center, and all shopping center information in the target area, such as shopping center popularity, has been pre-recorded in the parameter table. Calculate the regional influence of each POI point according to its specific brand type and commercial value, and include the POI point in the business district if the regional influence reaches a preset level. The commercial value includes the brand popularity of the brand to which the POI belongs in the current business district and whether the POI is in a shopping mall.

[0025] S2. Call the map data of the historical business district library to compare with these business district ranges. If the overlap degree between a business district range and all the business districts in the historical business district library is lower than a preset degree, the business district range is used as a new target project.

[0026] In step S2, the degree of overlap being lower than a preset degree means that: the degree of coordinate overlap between the business district range and any business district in the historical business district database is lower than the overlap threshold R1, or the sum of the degree of coordinate overlap between the business district range and multiple business districts in the historical business district database is lower than the overlap threshold R2, and the overlap threshold R2 is slightly greater than the overlap threshold R1.

[0027] In order to solve the problem of low differentiation due to similar brand density in adjacent areas, this embodiment also refines the grid, performs multi-scale analysis and introduces auxiliary variables to the adjacent areas. Refine the grid: Divide the study area into smaller grid units to improve spatial resolution. Multi-scale analysis: Draw heat maps at different scales to observe the spatial distribution characteristics of brand density. Introduce auxiliary variables: Combine auxiliary variables such as population density, consumption level, and traffic conditions to conduct a comprehensive analysis of brand density to improve the differentiation of adjacent areas.

[0028] S3. Obtain all POI point data of the target project, identify the target store, calculate the aggregation value of the identified target store, and take the sum of the aggregation values ​​of all target stores as the total POI score.

[0029] In step S3, the target stores include business district stores and street shops whose POI point popularity reaches a preset level; the POI point popularity is the sum of the search volume statistics, social media interaction values ​​and public opinion monitoring scores.

[0030] Among them, search volume statistics: through the keyword planner tool provided by the search engine, count the number of searches for brand words within a period of time (such as the past 30 days), and use the ratio of this search number to the average number of searches for POI points as the search volume statistics. Social media interaction: monitor the discussion heat about the brand on major social platforms, including the number of likes, comments, and reposts, and use the ratio of this discussion heat to the average discussion heat of POI points as the social media interaction assignment. Public opinion monitoring: use a special brand heat index query tool, such as Zhiwei Shangqing, to regularly track the frequency of brand mentions and emotional tendencies (positive and negative) on the Internet, and use the ratio of the frequency of mentions to the average frequency of POI point mentions as the basic value of the public opinion monitoring score, and multiply the basic value by the emotional tendency weight as the public opinion monitoring score. The specific emotional tendency weight is: 110% for positive and 95% for negative.

[0031] Among them, the POI point heat reaching a preset level means that the POI point heat is higher than the median of the POI point heat in the target area.

[0032] In step S3, calculating the cluster value of the identified target store means: obtaining the brand index corresponding to the target store, calling the pre-stored expert score for each brand index, and using the hierarchical analysis method AHP to calculate the comprehensive score of the target store on these brand indicators, which is recorded as the cluster value.

[0033] The POI data of this embodiment is a valid brand POI after matching with the standard brand library, that is, the brand dimension information after the standard construction can be obtained, so that the expert's score for each brand indicator can be obtained, and the brand indicator weight can be obtained through the hierarchical analysis method AHP, so as to calculate the comprehensive score of the brand. Specifically, the implementation process of calculating the brand score based on the hierarchical analysis method AHP in this embodiment includes the following steps 1 to 3.

[0034] Step 1: Confirm brand indicators The brand score of this embodiment is used to measure the influence and competitiveness of the brand in the market. The confirmed brand indicators include the following eight: whether the POI brand is a national chain brand, whether the POI brand is a regional chain brand, whether the POI brand is a local chain brand, the total number of POI brand stores, the proportion of POI brand stores in the same city, the POI brand level, the popularity value of POI brand social software, and the weight of the business format to which the POI belongs.

[0035] Is the POI brand a national chain brand? This feature is of Boolean type, indicating whether the POI brand has chain stores nationwide.

[0036] Whether the POI brand is a regional chain brand: This feature is also of Boolean type, indicating whether the POI brand has chain stores in a specific region (such as a province or city).

[0037] Whether the POI brand is a local chain brand: a Boolean feature indicating whether the POI brand has chain stores only in a certain place (such as a county).

[0038] Total number of POI brand stores: a numerical feature, indicating the total number of stores owned by the brand nationwide.

[0039] POI brand store ratio in the same city: a numerical feature, indicating the ratio of the number of brand stores in the same city to the total number of stores.

[0040] POI brand level: a numerical or categorical feature indicating the level or grade of a brand.

[0041] POI brand social software popularity value: a numerical feature that indicates the brand’s attention and interaction popularity on social software.

[0042] Weight of the business type to which the POI belongs: a numerical feature that indicates the importance or market share of the business type to which the POI belongs in the entire market.

[0043] Step 2: Construct a judgment matrix The eight brand indicators were compared pairwise using expert ratings to determine their relative importance. Each comparison was based on a consistent rating scale of 1 to 9, with 1 indicating that both elements were equally important and 9 indicating that the former was extremely more important than the latter.

[0044] The judgment matrix of the eight brand indicators is as follows:

[0045] Step 3: Calculate weights and check consistency According to the above judgment matrix, solve its maximum eigenvalue and its corresponding eigenvector. Then normalize the eigenvector to obtain the weight value of each brand indicator. In addition, perform a consistency check to ensure that the consistency of the judgment matrix meets the requirements and the consistency ratio CR is less than 0.1. The above processing is the general process of the hierarchical analysis method AHP, which will not be repeated here.

[0046] After calculation, the following weight distribution is obtained: National chain brands: 0.30 Regional chain brand: 0.20 Local chain brands: 0.10 Total number of stores: 0.15 The proportion of the same city: 0.05 Brand level: 0.05 Social software popularity value: 0.10 Weight of business format: 0.05.

[0047] Step 4: Calculate Brand Score Based on the expert evaluation of each brand indicator, combined with the above weights, the brand's comprehensive score is calculated: the sum of the scores of each indicator multiplied by its corresponding weight. This is used as the aggregation value of the target store.

[0048] S4. Obtain the passenger flow data within the business district and the passenger flow data of multiple similar business districts, calculate the ratio of the passenger flow data within the current business district to the passenger flow data of similar business districts, and multiply the ratio by 100 as the total passenger flow score. The total passenger flow score can also be obtained by analyzing the passenger flow value of the project using the entropy weight method.

[0049] In step S4., obtaining the passenger flow data within the business district refers to: obtaining the population density of each business district hotspot area, and calculating the expected passenger flow data from each business district hotspot area in combination with the radiation probability of the business district to each business district hotspot area.

[0050] The arithmetic square root of the ratio of the number of connecting paths between hot spots in the current business district to the average number of connecting paths in the target area is used as the passenger flow weight, and the radiation probability is the average passenger flow / average population density of the target area multiplied by the passenger flow weight. The population density of each hot spot in the business district is obtained, which includes the residential population density and the working population density, and then the population density of a hot spot in a business district is multiplied by the radiation probability of the business district to the hot spot in the business district to calculate the expected passenger flow data from the hot spot in the business district.

[0051] The theoretical heat map of this business district is generated based on the expected passenger flow data of each hot spot area of ​​the business district. Specifically, each hot spot area of ​​the business district is marked with different depths of color according to the expected passenger flow data, thereby generating the theoretical heat map of this business district. The larger the expected passenger flow data of a hot spot area of ​​a business district, the darker the color marked on the hot spot area of ​​the business district.

[0052] S5. Confirm the weights of the POI total score and the passenger flow total score respectively, denoted as W_A and W_B, W_A+W_B=1, calculate the weighted values ​​according to the values ​​of the POI total score and the passenger flow total score and the corresponding weights, and take the sum of all weighted values ​​as the business district score of the current target project. If the business district score reaches the preset threshold, the target project is marked as a new business district. This embodiment pre-stores the business district level corresponding to the business district score: city level / regional level / community level, and searches for the corresponding business district level according to the business district score and outputs it. This embodiment combines the score of the POI point with the passenger flow data of the shopping center in the business district to form a business district score and classify the business district, thereby reducing the deviation of the single POI data in marking the business district.

[0053] The method for calibrating a new business district in this embodiment uses kernel density analysis to process multi-format POI point data, automatically identifies high-density areas and determines the scope of the business district. This process reduces the subjectivity and inconsistency that may occur when defining the business district manually in the traditional way. In addition, the target store aggregation value calculation and passenger flow data analysis are combined to comprehensively score, which not only reflects the concentration of commercial activities through the POI total score, but also reflects the actual flow of people through the passenger flow total score, thereby achieving a comprehensive and objective evaluation of the business district. Therefore, compared with traditional methods that rely only on single-dimensional data, this composite evaluation strategy can more accurately reflect the true value and development potential of the business district, effectively reduce the risk of misjudgment, and provide solid data support for business decisions.

[0054] The above is only an implementation method of the invention, and does not limit the scope of patent protection. Those skilled in the art can make non-substantial changes or substitutions based on the invention, which still fall within the scope of patent protection.

Claims

1. A method for calibrating a new business district, characterized in that: The method comprises the following steps executed by a computer program: S1. Calling the multi-format POI point data of the target area, performing kernel density analysis on the obtained POI point data to identify high-density areas, and obtaining the business district scope of all the identified high-density areas; S2. Calling the map data of the historical business district library to compare with these business district ranges, if there is a business district range and the degree of overlap between all business districts in the historical business district library is lower than the preset degree, then the business district range is used as the new target project; S3. Obtain all POI point data of the target project, identify the target store, calculate the aggregation value of the identified target store, and use the sum of the aggregation values ​​of all target stores as the total POI score; S4. Obtain the passenger flow data within the business district and the passenger flow data of multiple similar business districts, calculate the ratio of the passenger flow data within the current business district to the passenger flow data of similar business districts, and multiply the ratio by 100 as the total passenger flow score; S5. respectively confirm the weights of the POI total score and the passenger flow total score, denoted as W_A and W_B, W_A+W_B=1, calculate the weighted values ​​according to the values ​​of the POI total score and the passenger flow total score and the corresponding weights, and take the sum of all weighted values ​​as the business district score of the current target project. If the business district score reaches a preset threshold, the target project is marked as a new business district.

2. The method for calibrating a new business district according to claim 1, characterized in that: In step S1, the kernel density analysis of the acquired POI point data to identify the high-density area is specifically as follows: Identify the business type of the acquired POI point data, extract all POI point coordinates of the M business types with the largest number of POI points in the target area, and calculate the regional density of each of these M business types within the unit area using the following kernel density analysis KDE formula: ； f(x) represents the density estimation at position x, n is the number of POI points, h is the bandwidth parameter of kernel density analysis, K(·) is the Gaussian kernel function, and x i is the coordinates of the i-th POI point of this business type; Calculate the sum of the regional densities of M business types within the unit area. If the sum of the calculated regional densities reaches a preset saturation threshold, the geographical area corresponding to the unit area is considered to be a high-density area. If multiple adjacent unit area ranges are high-density areas, these unit area ranges are merged into one high-density area.

3. The method for calibrating a new business district according to claim 1, characterized in that: In the step S1, the business district scope of each of the identified high-density areas is obtained specifically by constructing a heat map of the number of POI points for each high-density area to identify the commercial activity level of each high-density area, connecting the outermost areas of each high-density area whose commercial activity level reaches a preset value to construct a maximum outer contour, and taking the inner area of ​​the maximum outer contour as the business district scope of the high-density area.

4. The method for calibrating a new business district according to claim 3, characterized in that: All POI points whose distance from the maximum outer contour is within a preset range are extracted, and the regional influence of each POI point is calculated according to the brand type and commercial value of each POI point. If the regional influence reaches a preset level, the POI point is included in the business district; the commercial value includes the brand popularity of the brand to which the POI point belongs in the target area and the popularity of the shopping mall associated with the POI point.

5. The method for calibrating a new business district according to claim 1, characterized in that: In step S2, the overlap degree being lower than the preset degree means that the coordinate overlap degree between the business district range and any business district in the historical business district database is lower than the overlap threshold R1, or the sum of the coordinate overlap degrees between the business district range and multiple business districts in the historical business district database is lower than the overlap threshold R2, and the overlap threshold R2 is slightly greater than the overlap threshold R1.

6. The method for calibrating a new business district according to claim 1, characterized in that: In step S3, the target stores include business district stores and street shops whose POI point popularity reaches a preset level; the POI point popularity is the sum of the search volume statistics, social media interaction values ​​and public opinion monitoring scores.

7. The method for calibrating a new business district according to claim 6, characterized in that: Calculating the cluster value of the identified target store means: obtaining the brand index corresponding to the target store, calling the pre-stored expert's score for each brand index, and using the hierarchical analysis method AHP to calculate the comprehensive score of the target store on these brand indicators, which is recorded as the cluster value; The brand indicators include a variety of the following indicators: whether the POI brand is a national chain brand, whether the POI brand is a regional chain brand, whether the POI brand is a local chain brand, the total number of POI brand stores, the proportion of POI brand stores in the same city, the POI brand level, the popularity value of POI brand social software, and the weight of the business format to which the POI belongs.

8. The method for calibrating a new business district according to claim 1, characterized in that: In step S4, obtaining the passenger flow data within the business district refers to: obtaining the population density of each business district hotspot area, and calculating the expected passenger flow data from each business district hotspot area in combination with the radiation probability of the business district to each business district hotspot area.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method for marking a new business district as described in any one of claims 1 to 8 are implemented.

10. The business district identification system is characterized by: The method comprises a processor and a computer-readable storage medium as claimed in claim 9, which are connected to each other.

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