Methods for analyzing the structure of regional industrial clusters and industrial cluster score radar charts
Patent Information
- Application Number
- JP2025059453
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2026-10-14
Smart Images
Figure 2026166487000001_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a method for analyzing a regional industrial agglomeration structure and an industrial agglomeration score radar chart, and more particularly to a method for analyzing a regional industrial agglomeration structure for analyzing the industrial agglomeration structure of each administrative division such as a local public entity, and an industrial agglomeration score radar chart.
Background Art
[0002] The "Economic Census" (Non-Patent Document 1) issued by the Ministry of Internal Affairs and Communications presents quantitative data for each region in Japan, such as the number of business establishments, the number of enterprises, and the number of employees by activity status. Various analyses have been conducted using the data of this "Economic Census", and for example, reports on the industrial structure, population dynamics, and human flow of each local public entity have been made.
[0003] As a specific example, for each industry existing in a local public entity that governs one administrative division, a "quantitative composition ratio" that shows the percentage of the industry in all industries existing in the local public entity is calculated and made into a visually recognizable form. For example, the "number of business establishments" as the quantitative data of each industry is confirmed, the industrial composition ratio is obtained based on the number, and this is reported in various forms. That is, the sum of the quantitative composition ratios of each industry in the relevant region is represented in a table, bar graph, line graph, pie chart or the like in a form that totals 100%.
[0004] The types of industries are classified, for example, as 1. Agriculture, Forestry and Fisheries, 2. Mining, Quarrying and Gravel Gathering, 3. Construction, 4. Manufacturing, 5. Electricity, Gas, Heat Supply and Water, 6. Information and Communications, 7. Transport and Postal Services, 8. Wholesale and Retail Trade, 9. Finance and Insurance, 10. Real Estate and Goods Rental and Leasing, 11. Academic Research, Professional and Technical Services, 12. Accommodation and Food and Beverage Services, 13. Living-Related Services and Entertainment Services, 14. Education and Learning Support, 15. Medical Care and Welfare, 16. Compound Services, 17. Services (not elsewhere classified), and the like.
[0005] Figure 6, based on this classification, shows, as an example, the quantitative composition ratio of each industry in Moriya City, Ibaraki Prefecture, calculated based on the number of establishments, and displayed as a radar chart. Looking at wholesale and retail trade, it is 21.1%, which is the value obtained by dividing the number of wholesale and retail trade establishments in Moriya City by the total number of establishments for all industries in Moriya City. In other words, the sum of the composition ratios of each industry is 100%. According to this radar chart of quantitative composition ratios based on the actual quantitative figures for each industry, it can be seen that the quantitative composition ratio of wholesale and retail trade is high in Moriya City, while that of information and communications and manufacturing is low. [Prior art documents] [Patent Documents]
[0006] [Non-Patent Document 1] Economic Census - Statistics Bureau of the Ministry of Internal Affairs and Communications https: / / www.stat.go.jp / data / e-census / index.html [Overview of the project] [Problems that the invention aims to solve]
[0007] The quantitative composition ratios of each industry, as described above, that is, the ratios of actual figures within the region, allow us to clearly understand the quantitative proportion of each industry within each local government. However, these quantitative composition ratios alone do not allow for a realistic assessment of whether each industry is quantitatively deficient or in excess within that local government.
[0008] For example, determining whether there is a shortage of medical and welfare services cannot be accurately judged simply by looking at their quantitative share in relation to other industries within the region. In other words, even if the quantitative share is low compared to other industries, there is a possibility that medical and welfare services exist in sufficient quantities.
[0009] For example, as mentioned above, in the case of Moriya City shown in Figure 6, the quantitative composition ratio of wholesale and retail trade is 21.2%, which is the highest ratio. However, whether the wholesale and retail trade in Moriya City is quantitatively sufficient or excessive cannot be determined without examining other factors.
[0010] Similarly, while education and learning support services account for 7.9% of the total, a smaller quantitative share compared to wholesale and retail trade at 21.2%, it is not immediately clear whether or not there is a quantitative shortage in this sector.
[0011] Furthermore, in order for each local government to accurately define the regional industrial cluster structure, it is necessary to create appropriate budgets to support industries that are actually lacking. However, the current situation reports mentioned above are not useful data for such analysis. The two inventors of this application conduct research on administration and regional industries at a university, and the invention of this application was created as a result of their research.
[0012] This invention has been made in view of the above problems, and its purpose is to provide a method for analyzing the industrial cluster structure of a region and an industrial cluster score radar chart that can provide information on the evaluation of the industrial cluster structure in a region, such as a local government, with as much relative and objective accuracy as possible. [Means for solving the problem]
[0013] To achieve the above objective, the method for analyzing the regional industrial cluster structure described in claim 1 is: The quantitative criteria setting process involves setting quantitative criteria for industries in order to measure the degree of industrial concentration in the area under analysis, A real composition ratio calculation step that calculates the actual quantitative composition ratio of each industry to all industries in the analysis area in accordance with the quantitative criteria set in the quantitative criteria setting step, A comparison region setting step involves selecting one or more comparison regions other than the aforementioned analysis region, to be compared with the quantitative composition ratio of each industry in the aforementioned analysis region, and performing the actual composition ratio calculation step for each industry in the selected comparison region. A process for extracting the largest industry by selecting the industry with the highest quantitative composition ratio among the aforementioned analysis area and comparison area, The method is characterized by comprising: an industrial cluster score setting step, in which the quantitative composition ratio of each of the industries in the area under analysis is divided by the quantitative composition ratio of the industry with the largest composition ratio in each of the aforementioned industries, and the score calculated by further multiplying by 100 is used as the industrial cluster score for each of the aforementioned industries.
[0014] This method allows for a more relative and objective evaluation of the industrial cluster structure, which cannot be recognized from the actual quantitative composition ratios of each industry in a region, such as a local government. First, the industry with the highest quantitative composition ratio is extracted for each industry in the analysis region and the comparison region. For example, if the analysis region and the comparison region are five "cities," the industry with the highest quantitative composition ratio in the city with the highest ratio is extracted, and an industrial cluster score is calculated, which is the relative value of the same industry in the other cities, with the quantitative composition ratio of this industry set to 100. In other words, instead of the actual quantitative composition ratios of each industry in the five cities, an "industrial cluster score" is calculated, which is the degree of relative industrial clustering that takes into account the quantitative composition ratios of each industry in the comparison region.
[0015] In other words, the quantitative composition ratio of a particular industry within a city is not evaluated using the raw numerical value, but rather by an industrial agglomeration score obtained by normalizing it based on the quantitative composition ratio of the largest-constituting industry in that industrial sector. By combining the quantitative composition ratio, which shows the industrial structure within a region as a percentage, with normalization based on the largest-constituting industry, a relative and objective evaluation becomes possible.
[0016] Therefore, even if the quantitative share of a particular industry in a city is small compared to other industries, the figure for that quantitative share may not necessarily be small in relation to other regions, and the calculated industrial agglomeration score may be very high. In other words, even if the actual ratio of the quantitative share in one's own city is small, it can be recognized that the industry in question may have a relative quantitative advantage.
[0017] Conversely, even if the quantitative proportion of a particular industry in a city is large compared to other industries, it may still be small in relation to other regions (resulting in a low industrial agglomeration score).
[0018] Thus, according to the invention of this claim, it becomes possible to make a relative evaluation of each industry in the region under analysis over a wider range, that is, in relation to other designated regions, thereby improving the objectivity of the evaluation of the industrial cluster structure. In other words, the objectivity of the evaluation is enhanced by an analysis that relatively incorporates data from other regions, rather than analyzing only the absolute values of quantitative composition ratios. In today's rapidly changing industrial landscape, it is important to quickly and accurately analyze and grasp the degree of industrial cluster structure through simple calculations, and this invention can meet that need.
[0019] A more complete assessment of industrial cluster structure requires more than just a relative evaluation of quantitative composition ratios; it also necessitates the analysis of various factors such as the environment and geographical characteristics of the region. However, the present invention provides a more relative and rapid way to grasp the degree of industrial cluster structure than previous methods, which is useful.
[0020] The method for analyzing the industrial cluster structure of a region according to claim 2 is the method for analyzing the industrial cluster structure of a region according to claim 1, The quantitative criterion for the aforementioned industries is characterized by the number of establishments in each industry.
[0021] Thereby, by using the number of business establishments as a criterion for measuring the quantitative scale of each industry, the criterion can be grasped objectively and clearly, and the quantitative composition ratio of each industry can be easily calculated.
[0022] The method for analyzing a regional industrial agglomeration structure according to claim 3 is the method for analyzing a regional industrial agglomeration structure according to claim 1 or 2, wherein the analysis target region is a local public entity, and the comparison target region is another public entity belonging to a wider administrative district to which the local public entity belongs.
[0023] According to this configuration, for example, when a single city is the analysis target, it is possible to calculate an industrial agglomeration score in a relative relationship with other cities belonging to the prefecture to which the city belongs, enabling objective evaluation within the unique environment of the prefecture that is the higher-level region.
[0024] The method for analyzing a regional industrial agglomeration structure according to claim 4 is the method for analyzing a regional industrial agglomeration structure according to claim 1 or 2, wherein a region located in a geographical environment close to the analysis target region is selected as the comparison target region.
[0025] According to this, in an industrial structure that is influenced by geographical environment, it becomes possible to relatively recognize the characteristics of the industrial structures of the respective regions. For example, it is conceivable to select local public entities that are both located in coastal areas, or local public entities that are both located in mountainous areas.
[0026] The method for analyzing a regional industrial agglomeration structure according to claim 5 is the method for analyzing a regional industrial agglomeration structure according to claim 1 or 2, wherein the analysis target region is a local public entity, the comparison target region is a country, and the quantitative composition ratio of the country in the real composition ratio calculation step is used as a reference value for the local public entities belonging to the country.
[0027] This configuration allows for the widest possible comparison area within the country, enabling the calculation of relative industrial cluster scores over a broader range, and thereby improving the objectivity of the evaluation of industrial cluster structure based on the analysis.
[0028] The industrial cluster score radar chart described in claim 6 is Multiple axes radiating outwards from the center, with numerical values assigned to the direction of extension, Each of the multiple axes has an assigned industry name, The industrial cluster score calculated by performing the method for analyzing the industrial cluster structure of a region according to claim 1 or 2 is plotted for each corresponding industrial axis. The plot is characterized by being created by connecting the points with lines.
[0029] This allows for a visual understanding of which industries hold a relatively dominant position in each industrial sector. In other words, it makes it easier to understand the relatively significant industrial cluster structure in each region, and becomes a useful resource for regional administration regarding industrial cluster structure.
[0030] The industrial cluster score radar chart according to claim 7 is the industrial cluster score radar chart according to claim 6, In addition to the plot of the industrial cluster score and the lines, the quantitative composition ratio of each industry is plotted on each axis, and these plots are connected by lines.
[0031] This makes it possible to instantly compare the quantitative composition ratio of each industry in a region with the industrial agglomeration score, enabling a rapid evaluation of the relative industrial agglomeration structure based on the industrial agglomeration score. [Effects of the Invention]
[0032] According to the present invention's method for analyzing the regional industrial cluster structure and the industrial cluster score radar chart, the quantitative composition ratio of each industry in a single region can be grasped and evaluated not by simply looking at the actual figures, but by an industrial cluster score calculated in relation to other regions. Furthermore, it is possible to quickly calculate and evaluate based on a simple formula, which can contribute to the analysis of the rapidly changing industrial structure of each region. This will enable the prompt provision of more objectively meaningful data in policy-making forums concerning industrial cluster structure, such as those of local governments. [Brief explanation of the drawing]
[0033] [Figure 1] A flowchart illustrating an embodiment of the method for analyzing the regional industrial cluster structure of the present invention is shown. [Figure 2] This shows the industrial cluster score radar chart for Moriya City, Ibaraki Prefecture. [Figure 3] (a) and (b) show the industrial cluster score radar chart and the quantitative composition ratio radar chart, respectively, for Tokashiki Village, Okinawa Prefecture. [Figure 4] (a) and (b) show the industrial cluster score radar chart and the quantitative composition ratio radar chart, respectively, for Chiyoda Ward, Tokyo. [Figure 5] (a) and (b) show the industrial cluster score radar chart and the quantitative composition ratio radar chart, respectively, for Niikappu Town, Hokkaido. [Figure 6] This shows a radar chart of the quantitative composition ratio of each industrial sector in Moriya City. [Modes for carrying out the invention]
[0034] Hereinafter, embodiments of the method for analyzing the regional industrial cluster structure of the present invention will be described in detail with reference to the drawings. Figure 1 is a flowchart of the method for analyzing the regional industrial cluster structure according to the embodiment. First, the necessary data is obtained using the "Economic Census" published by the Ministry of Internal Affairs and Communications (Step S1, Economic Census acquisition process). The contents of the "Economic Census" can be obtained by accessing websites, etc. The Economic Census Activity Survey includes information such as the number of establishments by industrial sub-classification.
[0035] Next, in the quantitative criteria setting process (step S2), quantitative criteria for industries are set to measure the degree of industrial clustering in the region under analysis. In this embodiment, the number of establishments for each industry in each prefecture and each city / town / village was adopted. This allows for the use of an objective and clearly understandable criterion, the number of establishments, and makes it easy to calculate the quantitative composition ratio of each industry.
[0036] Next, in the actual composition ratio calculation process (step S3), the actual quantitative composition ratios of each industry in each region under analysis are calculated according to the number of establishments obtained in the quantitative standard setting process. For example, in a certain "city" under analysis, the quantitative composition ratio of agriculture, forestry, and fisheries is obtained by dividing the number of agricultural and fisheries establishments by the total number of establishments for all industries in that city. Therefore, in a certain city, the sum of the quantitative composition ratios of each industry equals 1, or 100%.
[0037] Next, in the comparison area selection process (step S4), one or more comparison areas are selected to compare with the area under analysis. Then, for each of the selected comparison areas, the actual composition ratio calculation process in step 3 is performed. For example, if the comparison areas are multiple municipalities, the quantitative composition ratio of each industry in those municipalities is calculated.
[0038] Next, in the process of selecting the industry with the highest quantitative share (step S5), the industry with the highest quantitative share among the analysis area and the comparison area is selected as the industry with the highest quantitative share. Through this process, the industry with the highest quantitative share for each industry is identified. In other words, for a certain industry, such as healthcare and welfare, among several cities, the healthcare and welfare industry in the city with the highest quantitative share is selected as the industry with the highest quantitative share, and the numerical value of that quantitative share is used as the standard.
[0039] Next, in the industrial cluster score setting process (step S6), the quantitative composition ratio of the same industry in the analysis region and the comparison region is divided by the quantitative composition ratio of the industry with the largest composition ratio for each industry, and then multiplied by 100 to calculate the relative degree of clustering of the same industry in each analysis region, i.e., the industrial cluster score S. In other words, the quantitative composition ratio of the industry with the largest composition ratio is set to 100, and the quantitative composition ratio of the same industry in the target region is converted to obtain the industrial cluster score S.
[0040] The method for calculating the industrial cluster score S described above can be expressed specifically by the following formula: S=X / M×100 (Formula 1) M represents the quantitative share of the largest industry in each sector, and X represents the quantitative share of the same industry in the analyzed and comparison regions.
[0041] The industrial cluster score S allows for a more relative and objective evaluation of the industrial cluster structure, which cannot be perceived from the actual quantitative proportions of each industry in a region, such as a local government. In other words, by applying a simple formula, an industrial cluster score S, which is an evaluation value in relation to other cities in a given city, can be obtained for each industry. If the industrial cluster score S is 100 or close to 100, it can be determined that the local government has a quantitative advantage in that industrial sector when viewed relatively.
[0042] Next, as an additional configuration in this embodiment, the process of creating an industrial cluster score radar chart (step S7) will be described. First, the names of each industrial sector are listed in a predetermined order in a polygonal shape, and the calculated industrial cluster score S for each industry in the area to be analyzed is plotted for each listed industry. This process is carried out for each local government to be analyzed. This makes it possible to visually understand which industries have a relatively advantageous quantitative position in that local government. In other words, it becomes easier to recognize the evaluation results of meaningful industrial cluster structures for each region, and the industrial cluster score radar chart becomes a useful resource for local administration regarding industrial cluster structures.
[0043] Next, we will show an example of calculating the industrial cluster score S for a specific local government and a comparison area using the method for analyzing the regional industrial cluster structure according to the above embodiment.
[0044] Figure 2 is an industrial cluster score radar chart for Moriya City, Ibaraki Prefecture. In this embodiment, Moriya City is the area under analysis, while a national baseline is set as the comparison area. In other words, a relative analysis is being conducted among local governments nationwide.
[0045] First, the names of the 17 industrial sectors mentioned above are listed in order in a polygonal shape, and the industrial cluster score S calculated by (Equation 1) is plotted. Figure 6, as mentioned above, plots the quantitative composition ratio of each industry in Moriya City onto the listed industries in a polygonal shape, and the sum of the quantitative composition ratios of each industry is 100%.
[0046] As can be seen from the industrial cluster score radar chart, Moriya City's industrial cluster score S for education and learning support is 96.2, and its industrial cluster score for wholesale and retail trade is 48.5. In contrast, the quantitative composition ratios of each industry in the radar chart in Figure 6 are 7.9% and 21.1%, respectively, showing a significant difference in their evaluations.
[0047] From this, the quantitative share of the education and learning support industry is smaller than that of the wholesale and retail industries. In other words, based on the number of establishments in Moriya City, the quantitative share is rated lower than that of the wholesale and retail industries. However, when compared with the industrial cluster score S calculated in relation to other regions, the education and learning support industry scores a high 96.2, indicating that its relative industrial advantage is not necessarily low.
[0048] On the other hand, the industrial agglomeration score S for wholesale and retail trade is 48.5, which is lower for wholesale and retail trade than for education and learning support, reversing the results of the quantitative composition ratio comparison.
[0049] In other words, education and learning support businesses, which are small in terms of quantitative real numbers, can be recognized as having a quantitative advantage over wholesale and retail businesses in relative terms.
[0050] Next, Figure 3(a) is a radar chart of the industrial cluster score for Tokashiki Village in Okinawa Prefecture, and Figure 3(b) is a radar chart of the quantitative composition ratio. In this embodiment as well, the comparison area is based on a national baseline value.
[0051] As can be seen from the chart, the industrial cluster score S for lifestyle-related services and entertainment in Tokashiki Village is 100.0. This means that the quantitative share of lifestyle-related services and entertainment within Tokashiki Village (26.3%) was the highest at the national level.
[0052] Furthermore, the industrial cluster score S for the accommodation and food service industries is 52.0, and from Figure 3(b), it can be understood that its quantitative composition ratio is 35.5%. It is recognized that the relationship between "lifestyle-related services and entertainment" and "accommodation and food service industries" in Tokashiki Village differs in evaluation between comparison based on quantitative composition ratio and analysis based on the industrial cluster score S.
[0053] From these evaluation results, Even if the quantitative composition ratio is large in absolute terms, it can be recognized that it does not necessarily mean a relative quantitative advantage. Specifically, when formulating policies regarding industrial structure, the fact that "lifestyle-related services and entertainment" has the highest industrial cluster score S in the country does not mean that it is insufficient; rather, it is possible to consider whether it is in an excess. On the other hand, for "accommodation and food service industries," the industrial cluster score S indicates that it is in an average quantitative situation.
[0054] Figure 4(a) is a radar chart of the industrial cluster score for Chiyoda Ward, Tokyo, and Figure 4(b) is a radar chart of the quantitative composition ratio. In this embodiment as well, the comparison area is the same as that for the entire country.
[0055] In Chiyoda Ward, the industrial cluster scores S for the three sectors—"Information and Communications," "Finance and Insurance," and "Scientific Research, Professional and Technical Services"—are all 100.0. As mentioned above, this means that these three sectors had the highest quantitative share (10.3%, 5.9%, and 15.3%) at the national level.
[0056] In Chiyoda Ward, the highest quantitative share is wholesale and retail trade at 22.7%, while the quantitative shares of information and communications, finance, insurance, and scientific research, professional and technical services are all lower. However, the industrial cluster score S for each is 100.0, indicating that, when viewed nationwide, their quantitative shares are by no means small and represent a competitive advantage. In other words, although their quantitative shares are not particularly high compared to other industries within Chiyoda Ward, three industries are recognized as having a competitive advantage when viewed relatively nationwide. This is difficult to recognize solely from the quantitative shares within the ward, and the industrial cluster score S, calculated from a relative perspective, can be said to be a more objectively significant and useful piece of data.
[0057] Figure 5(a) is a radar chart of the industrial cluster score for Niikappu Town, Hokkaido, and Figure 5(b) is a radar chart of the quantitative composition ratio. In this embodiment as well, the comparison area is the same as that for the entire country.
[0058] In Niikappu Town, the radar chart in Figure 5(a) shows that the industrial agglomeration score S for "agriculture, forestry, and fisheries" is 100.0. Furthermore, in the quantitative composition ratio shown in Figure 5(b), agriculture, forestry, and fisheries also have the highest percentage at 32.1%. Therefore, this is an example of how "agriculture, forestry, and fisheries," which has the highest quantitative composition ratio compared to other industries, also has a high industrial agglomeration score S, demonstrating its national dominance. This suggests that, relatively speaking, the industrial policies promoting "agriculture, forestry, and fisheries" have yielded positive results.
[0059] As explained above, the Industrial Agglomeration S score allows for the recognition of the relative degree of industrial agglomeration in a region, providing objectively meaningful data for local government and other regional industrial policymakers. Moreover, the data provided here offers a perspective not previously available, can be prepared with relatively simple steps, and is highly cost-effective.
[0060] Although not shown in the diagram, it is also preferable to plot the quantitative composition ratio of each region for each industry listed in a polygonal shape on the industrial cluster score radar chart and overlay them. In this case, both data can be easily viewed even when overlaid by showing the industrial cluster score S as a solid line and the quantitative composition ratio as a dashed line, without using solid fills. This makes it possible to instantly compare the quantitative composition ratio of each industry in a single region with the industrial cluster score S at a glance.
[0061] Furthermore, while the comparison area in each of the above embodiments was the national government, it is not limited to this. It is also possible to select multiple local governments located in relatively similar geographical environments and set them as the comparison area. For example, by selecting local governments located in coastal areas, it becomes possible to evaluate local governments in similar geographical environments relatively.
[0062] Furthermore, limiting the scope to a specific region and comparing local governments within that region can also be a meaningful evaluation. For example, it is possible to select several local governments of similar size and population within Hokkaido, making it possible to create objective and meaningful evaluation data for the industrial cluster structure in industrial policy across the entire Hokkaido region.
[0063] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of the invention. For example, although the radar chart shows 17 classifications of industry types, they may be classified in more detail, or fewer industry types may be set.
Claims
1. The quantitative criteria setting process involves setting quantitative criteria for industries to measure the degree of industrial clustering in the region under analysis, and A real-value composition ratio calculation step that calculates the actual quantitative composition ratio of each industry in the region under analysis according to the quantitative criteria set in the quantitative criteria setting step, A comparison region setting step involves selecting one or more comparison regions other than the region under analysis, for which the quantitative composition ratio of each industry in the region under analysis will be compared, and performing the actual composition ratio calculation step for each of the selected comparison regions. A process for extracting the largest industry by selecting the industry in the region with the highest quantitative composition rate among the regions to be analyzed and the comparison regions, as the largest industry by composition rate. A method for analyzing the industrial agglomeration structure of a region, comprising: an industrial agglomeration score setting step, in which a score is calculated by dividing the quantitative composition ratio of the industry with the largest composition ratio for each of the aforementioned industries by the quantitative composition ratio of the same industry in the region under analysis, and then multiplying by 100, and the score is used as the industrial agglomeration score for each of the aforementioned industries.
2. The method for analyzing the industrial cluster structure of a region according to claim 1, characterized in that the quantitative criterion for the aforementioned industries is the number of establishments in each industry.
3. The region being analyzed is a local government. The method for analyzing the industrial cluster structure of a region according to claim 1 or 2, characterized in that the comparison area is another local government belonging to a broader geographical area to which the aforementioned local government belongs.
4. The method for analyzing the industrial cluster structure of a region according to claim 4 is the method for analyzing the industrial cluster structure of a region according to claim 1 or 2, A method for analyzing the industrial cluster structure of a region, characterized in that the comparison region is selected from a region located in a geographical environment similar to the region under analysis.
5. The region being analyzed is a local government. The aforementioned comparison area is a country. The method for analyzing the industrial cluster structure of a region according to claim 1 or 2, characterized in that the quantitative composition ratio for the entire country in the calculation step of the actual composition ratio is set to a standard value for local public entities belonging to the national government.
6. Multiple axes radiating outwards from the center, with numerical values assigned to the direction of extension, Each of the multiple axes has an assigned industry name, The industrial cluster score calculated by performing the method for analyzing the industrial cluster structure of a region according to claim 1 or 2 is plotted for each corresponding industrial axis. An industrial cluster score radar chart characterized in that the plots are connected by lines.
7. The industrial cluster score radar chart according to claim 6, characterized in that, in addition to the plot of the industrial cluster score and the lines, the quantitative composition ratio of each industry is plotted on each axis, and the plots are connected by lines.