WO2024070238A1 - Living environment evaluation system and living environment evaluation method - Google Patents

Abstract

A living environment evaluation system 1 for evaluating a living environment with respect to an indicator specified by a user comprises: a high-resolution data analysis unit 11 which, by using user preference information 132 that holds preference coefficients indicating the user's preference for one or more living environment items that may affect the indicator, analysis instruction information 18 that indicates an analysis range specified by the user, and images that correspond to the analysis range, performs wavelength-based analysis and thereby analyzes characteristics of each of a plurality of regions (meshes) obtained by dividing the analysis range; an indicator calculation unit 13 which calculates an indicator value for the indicator for each region of the analysis range on the basis of the analysis results of the characteristics of the analysis range and the user preference information 132; and a visualization unit 14 which displays and overlays, on a map indicating each region of the analysis range, evaluation results based on the indicator values calculated by the indicator calculation unit 13.

Description

Living environment evaluation system and living environment evaluation method

The present invention relates to a housing environment evaluation system and a housing environment evaluation method, and is suitable for use as a housing environment evaluation system and a housing environment evaluation method that evaluates a housing environment and visually outputs the evaluation result.

　There has long been a demand for technology that quantitatively evaluates living environments. By referring to a quantitative evaluation of living environments based on an individual's values, for example, customers searching for a house can more easily narrow down the areas that match their desired living environment, and real estate agents can reduce the time it takes to conclude a rental or purchase contract. Furthermore, local governments can understand the current state of an area from a quantitative evaluation of the living environment based on a specified perspective, which can be useful in planning facilities, etc.

For example, Patent Document 1 discloses a real estate information output device that outputs real estate information based on an individual's values. Patent Document 2 discloses a living environment evaluation device that evaluates living environments based on an individual's values.

JP 2019-145100 A JP 2017-91422 A

However, the technology disclosed in Patent Document 1 only evaluates individual real estate (houses), and does not evaluate the livability of a given area or utilize the results of that evaluation.

On the other hand, the technology disclosed in Patent Document 2 evaluates the living environment using a model that analyzes open data and calculates indicators related to the living environment of a "region," but since open data is used, it is presumed that the resolution of the area referred to as the "region" is relatively low. In general, the smallest unit of area that can be analyzed using open data is the administrative district unit such as a city, ward, town, or village.

However, actual living environments cannot be expressed by dividing them into administrative districts, but are thought to show continuous characteristics or features near the boundaries of administrative districts. In other words, to express a more realistic indicator of the living environment, analysis at a higher resolution than administrative district units such as cities, wards, towns, and villages is necessary, but the technology disclosed in Patent Document 2 has a low resolution of the range that can be evaluated, and there is a risk that it will not be possible to realize a living environment evaluation that fully meets the needs of users.

The present invention has been made in consideration of the above points, and aims to propose a housing environment evaluation system and method that can visually provide an evaluation of a housing environment that reflects individual preferences with a higher resolution than conventional technology.

In order to solve this problem, the present invention provides a residential environment evaluation system that evaluates a residential environment with respect to an index specified by a user, the residential environment evaluation system comprising: user preference information that holds a preference coefficient indicating the user's preference for one or more residential environment items that may affect the index; analysis instruction information that indicates an analysis range specified by the user as a range for evaluating the residential environment; a high-resolution data analysis unit that performs a wavelength-based analysis using an image that corresponds to the analysis range to analyze the characteristics of the analysis range for each of a plurality of regions into which the analysis range is divided; an index calculation unit that calculates an index value of the index in the analysis range for each of the regions based on the analysis results of the characteristics of the analysis range and the user preference information; and a visualization unit that displays the evaluation results based on the index values calculated by the index calculation unit by superimposing them on a map indicating the analysis range for each of the regions.

　In order to solve the above problem, the present invention provides a method for evaluating a living environment using a living environment evaluation system that evaluates a living environment with respect to an index specified by a user, the living environment evaluation system having user preference information that holds preference coefficients indicating the user's preferences for one or more living environment items that may affect the index, and analysis instruction information that indicates an analysis range specified by the user as a range for evaluating the living environment, the method comprising: a high-resolution data analysis step in which the living environment evaluation system performs a wavelength-based analysis using an image corresponding to the analysis range to analyze the characteristics of the analysis range for each of a plurality of divided areas of the analysis range; an index calculation step in which the living environment evaluation system calculates an index value of the index in the analysis range for each of the areas based on the analysis results of the characteristics of the analysis range in the high-resolution data analysis step and the user preference information; and a visualization step in which the living environment evaluation system displays the evaluation results based on the index values calculated in the index calculation step by superimposing them on a map showing the analysis range for each of the areas.

The present invention makes it possible to visually present an evaluation of the living environment that reflects individual preferences with a higher resolution than conventional technology.

1 is a block diagram showing an example of the configuration of a living environment evaluation system 1 according to a first embodiment of the present invention. 10 is a flowchart showing an example of a processing procedure of an overall process in the first embodiment; FIG. 2 is a diagram showing an example of analysis instruction information 18. 13 is a flowchart illustrating an example of a processing procedure for high-resolution data analysis processing. 13 is a flowchart illustrating an example of a processing procedure for feature classification processing. FIG. 11 is a diagram showing an example of color identification information 113. FIG. 11 is a diagram showing an example of feature information 19. 11 is a flowchart (part 1) illustrating an example of a processing procedure of a low-resolution data analysis process. 13 is a flowchart (part 2) illustrating an example of a processing procedure of a low-resolution data analysis process. FIG. 2 is a diagram showing an example of area information 122. 13 is a flowchart illustrating an example of a processing procedure for index calculation processing. FIG. 2 is a diagram showing an example of user preference information 132. FIG. 11 is a diagram showing an example of association information 133. FIG. 2 is a diagram showing an example of index value information 20. 13 is a flowchart illustrating an example of a processing procedure for visualization processing. FIG. 3 is a diagram showing an example of a living environment evaluation screen 300. FIG. 11 is a block diagram showing an example of the configuration of a living environment evaluation system 10 according to a second embodiment of the 13 is a flowchart illustrating an example of a processing procedure for updating association information. 13 is a flowchart showing an example of a processing procedure for past information display processing;

Below, an embodiment of the present invention will be described in detail with reference to the drawings.

The following description and drawings are illustrative of the present invention, and have been omitted or simplified as appropriate for clarity of explanation. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention. The present invention is not limited to the embodiments, and all application examples that conform to the concept of the present invention are included in the technical scope of the present invention. Those skilled in the art can make various additions and modifications to the present invention within the scope of the present invention. The present invention can also be implemented in various other forms. Unless otherwise specified, each component may be multiple or singular.

In the following explanation, various types of information may be explained using expressions such as "table," "list," and "queue," but the various types of information may also be expressed in other data structures. To indicate independence from data structure, "XX table," "XX list," and so on may be referred to as "XX information." When explaining the content of each piece of information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable.

In addition, in the following explanation, when describing elements of the same type without distinguishing between them, reference signs or common numbers in reference signs will be used, and when describing elements of the same type with distinction between them, the reference signs of those elements will be used or an ID assigned to those elements will be used instead of the reference signs.

In the following description, the processing performed by executing a program may be described, but the program is executed by at least one processor (e.g., a CPU) to perform a predetermined process using a storage resource (e.g., a memory) and/or an interface device (e.g., a communication port) as appropriate, so the subject of the processing may be the processor. Similarly, the subject of the processing performed by executing a program may be a controller, device, system, computer, node, storage system, storage device, server, management computer, client, or host having a processor. The subject of the processing performed by executing a program (e.g., a processor) may include a hardware circuit that performs part or all of the processing. For example, the subject of the processing performed by executing a program may include a hardware circuit that performs encryption and decryption, or compression and decompression. The processor operates as a functional unit that realizes a specified function by operating according to the program. Devices and systems that include a processor are devices and systems that include these functional units.

The program may be installed in a device such as a computer from a program source. The program source may be, for example, a program distribution server or a non-transitory storage medium readable by a computer. When the program source is a program distribution server, the program distribution server includes a processor (e.g., a CPU) and a non-transitory storage resource, and the storage resource may further store a distribution program and a program to be distributed. Then, the processor of the program distribution server may execute the distribution program, thereby distributing the program to be distributed to other computers. Also, in the following description, two or more programs may be realized as one program, and one program may be realized as two or more programs.

(1) First embodiment (1-1) System configuration Fig. 1 is a block diagram showing an example of the configuration of a living environment evaluation system 1 according to a first embodiment of the present invention. The living environment evaluation system 1 is a computer system that evaluates living environments within an area specified by a user (an individual searching for a house, a real estate agent, a local government official, etc.) taking into consideration the user's preferences, and is connected to a disk 2, a network 3, and an input/output device 4.

Disk 2 is a storage device that stores specific data used in the processing in the living environment evaluation system 1. Disk 2 is, for example, a storage device such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), but may also be an external storage or cloud connected via the network 3. Specific examples of the specific data stored on disk 2 include the original data of satellite images 114, map information 115, and regional information 122 in memory 17 described below. Note that these data may be configured to be obtained from an external device connected via the network 3.

The network 3 is a network that communicatively connects the living environment evaluation system 1 to the outside, and is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network). As mentioned above, instead of the disk 2 storing the specified data, the disk 2 may be configured to be able to acquire the specified data from the Internet or the like connected via the network 3.

The input/output device 4 includes an input device such as a keyboard and mouse that the user uses to operate the living environment evaluation system 1, and an output device such as a display or printer that outputs data output from the living environment evaluation system 1.

The living environment evaluation system 1 is configured with a processor 15, an input/output device 16, and a memory 17.

The processor 15 is a processor that controls the entire living environment evaluation system 1, and is specifically, for example, a CPU (Central Processing Unit). The input/output device 16 is an input/output interface that enables input and output of signals (data) between the living environment evaluation system 1 and its external environment (disk 2, network 3, input/output device 4).

Memory 17 is a storage device that stores programs and data used in the computer of living environment assessment system 1, and is, for example, a RAM (Random Access Memory). The high-resolution data analysis unit 11, low-resolution data analysis unit 12, index calculation unit 13, and visualization unit 14 shown in memory 17 are functional units that realize various functions by the processor 15 executing programs stored in or read from memory 17. Analysis instruction information 18, feature information 19, and index value information 20 shown in memory 17 are data stored in memory 17.

The high-resolution data analysis unit 11 has a function of analyzing the characteristics of an area (range) specified by a user, using "high-resolution data" with a higher resolution than "low-resolution data" represented by area information 122 described below. The high-resolution data analysis unit 11 has a land feature extraction unit 111 and a classification unit 112 as functional units that execute programs, and has color identification information 113, satellite imagery 114, and map information 115 as data used for processing by each of these functional units.

The color identification information 113 is data for identifying the analysis target by color, and a specific example is shown in FIG. 6, which will be described later. The satellite image 114 and map information 115 are data with a higher resolution than the regional information 122, which will be described later, and the details of each are explained in the feature classification process shown in FIG. 5.

The low-resolution data analysis unit 12 has a function of analyzing the characteristics of a region (area) specified by a user using "low-resolution data." The low-resolution data analysis unit 12 has a data mapping unit 121 as a functional unit that executes a program, and has regional information 122 as data used for processing by the data mapping unit 121.

Regional information 122 is low-resolution data for relatively broad "regions" such as administrative districts, and holds data values for each region for a given indicator. Regional information may be any data that indicates the characteristics of a broad "region," and may include, for example, the results of a survey of residents in each region, rankings by region, etc. A specific example of regional information 122 is shown in Figure 10, which will be described later.

The index calculation unit 13 has a function of integrating the analysis results by the high-resolution data analysis unit 11 and the low-resolution data analysis unit 12 to calculate an index value of the living environment that reflects the user's preferences. The index calculation unit 13 has a data integration evaluation unit 131 as a functional unit that executes a program, and has user preference information 132 and association information 133 as data used in processing by the data integration evaluation unit 131.

User preference information 132 is data that holds preference coefficients that indicate the user's preferences for a specific item, and a specific example is shown in FIG. 12, which will be described later. Association information 133 is data that holds association (weighting) coefficients used when calculating the feature quantities of a specific living environment item from the feature quantities held in feature information 19, and a specific example is shown in FIG. 13, which will be described later.

The visualization unit 14 has a function of visualizing the living environment index values calculated by the index calculation unit 13. The visualization unit 14 has a map display unit 141 as a functional unit that provides functions by executing a program. The living environment evaluation screen 300 shown in FIG. 16, which will be described later, is a display screen that the living environment evaluation system 1 provides to the user. The processing results by the map display unit 141 are also reflected in this living environment evaluation screen 300.

Analysis instruction information 18 is data that indicates the analysis range of the living environment specified by the user, and a specific example is shown in FIG. 3, which will be described later. Feature information 19 is data that holds the analysis results by the high-resolution data analysis unit 11 and the low-resolution data analysis unit 12, and a specific example is shown in FIG. 7, which will be described later. Index value information 20 is data that records the index value of the living environment calculated by the index calculation unit 13, and a specific example is shown in FIG. 14, which will be described later.

2 is a flowchart showing an example of a processing procedure of the overall processing in the first embodiment. The overall processing shows an overview of the living environment evaluation processing executed by the living environment evaluation system 1.

Before the overall processing in FIG. 2 is started, the user launches the software of the living environment evaluation system 1 and operates a user interface (UI) provided by the launched software to specify the range in which the living environment is to be analyzed (specifying the analysis range). Specifically, on the living environment evaluation screen 300 in FIG. 16 (described later), the user performs an operation to display the desired analysis range in the map display area 302 before pressing the evaluation execution button 304, thereby specifying the analysis range.

In addition to specifying the analysis range, the user also selects which items about the living environment he or she places importance on and to what extent (specifying user preferences). Specifically, on the living environment evaluation screen 300 in FIG. 16 described below, the user specifies user preferences by performing input operations according to the user's preferences in the questionnaire input area 301 before pressing the evaluation execution button 304.

Then, based on the results of these operations by the user, analysis instruction information 18 is generated and stored in memory 17 when the evaluation execution button 304 on the living environment evaluation screen 300 is pressed.

In the following explanation, the "evaluation specification screen" for specifying the analysis range and user preferences and the "evaluation result screen" for displaying the evaluation results of the living environment are displayed together on a single "living environment evaluation screen 300," but the evaluation specification screen and the evaluation result screen may be realized as separate UIs. Figure 16, which will be described later, shows a specific example of the living environment evaluation screen 300.

FIG. 3 is a diagram showing an example of analysis instruction information 18. In the analysis instruction information 18 shown in FIG. 3, starting latitude 1801, starting longitude 1802, north-south distance 1803, and east-west distance 1804 indicate the analysis range of the living environment specified by the user. In this example, the specified latitude and longitude (starting latitude 1801, starting longitude 1802) is set as the upper left vertex of a rectangle, and the analysis range is the rectangle from that vertex specified by north-south distance 1803 and east-west distance 1804. This analysis range corresponds to the entire range of the map displayed in map display area 302 of living environment evaluation screen 300 shown in FIG. 16.

Mesh width 1805 indicates the mesh width when dividing the analysis range into meshes. In the living environment evaluation system 1, the analysis range is divided into a plurality of areas (meshes) and the characteristics of each area are analyzed. In this example, the mesh is a square, and the length of one side of the square is specified by the mesh width 1805. Specifically, for example, if the mesh width 1805 is set to 100 m as shown in FIG. 3, the analysis range is divided into squares of 100 m on each side. The mesh width 1805 is set to a fixed value by the system, but may be changeable within a specified range or in specified units, for example, by the user. The mesh shape is also not limited to a square, and may be another specific shape.

The analysis object 1806 indicates the type of feature to be analyzed in each mesh in the analysis range. The analysis object 1806 is specified by the system, and color identification information 113 is prepared for each analysis object.

Returning to the explanation of FIG. 2, according to FIG. 2, first, the high-resolution data analysis unit 11 activates the land feature extraction unit 111 to execute the high-resolution data analysis process (step S101). Details of the high-resolution data analysis process will be described later with reference to FIG. 4 etc. By executing the high-resolution data analysis process, the characteristics of the analysis range are analyzed using high-resolution data.

Next, the low-resolution data analysis unit 12 activates the data mapping unit 121 to execute the low-resolution data analysis process (step S102). Details of the low-resolution data analysis process will be described later with reference to FIG. 8 etc. By executing the low-resolution data analysis process, the characteristics of the analysis range are analyzed using the low-resolution data.

Next, the index calculation unit 13 activates the data integration evaluation unit 131 to execute the index calculation process (step S103). Details of the index calculation process will be described later with reference to FIG. 11 etc. By executing the index calculation process, an index value of the living environment is calculated based on the analysis results of the high-resolution data analysis process and the low-resolution data analysis process and the user preferences, and is recorded in the index value information 20.

Finally, the visualization unit 14 starts the map display unit 141 to execute the visualization process (step S104). Details of the visualization process are shown in FIG. 15, which will be described later. By executing the visualization process, the evaluation results of the living environment in the analysis range are visually presented to the user.

Below, we will explain each step of the overall process described above in more detail.

(1-2-1) High-resolution data analysis processing Fig. 4 is a flowchart showing an example of the processing procedure of the high-resolution data analysis processing. The high-resolution data analysis processing shown in Fig. 4 corresponds to the processing of step S101 in Fig. 2, and is executed when the land feature extraction unit 111 is started by the high-resolution data analysis unit 11.

According to FIG. 4, first, the land feature extraction unit 111 acquires the analysis instruction information 18 generated based on the operation by the user (step S201). This "acquire" operation may refer to any of receiving, referencing, reading, or copying the target data, and the same applies to the following explanation.

Next, the land feature extraction unit 111 divides the analysis range specified by the analysis instruction information 18 acquired in step S201 into meshes (step S202). Specifically, the land feature extraction unit 111 divides the analysis range into a rectangular area with the starting point latitude 1801 and starting point longitude 1802 of the analysis instruction information 18 as the upper left vertex, the north-south distance 1803 as the vertical width, and the east-west distance 1804 as the horizontal width, into square areas (meshes) with a mesh width 1805 as a predetermined size. At this time, the north-south distance and east-west distance of the analysis range are rounded up to an integer multiple of the mesh width 1805, so that the analysis range is divided into multiple meshes. The process of dividing the analysis range into meshes is also performed in other processes described later, but all of these are similar to the process in step S202, so detailed explanations will not be repeated.

Next, the land feature extraction unit 111 checks whether steps S204 to S206 have been performed on all meshes generated in step S202 (step S203). If all meshes have been processed (YES in step S203), the high-resolution data analysis process ends. If unprocessed meshes remain (NO in step S203), the process proceeds to step S204. If this is the first time, the process has not yet been performed after step S204, so the process proceeds to step S204 via NO in step S203.

In step S204, the land feature extraction unit 111 selects one of the unprocessed meshes.

Next, the land feature extraction unit 111 activates the classification unit 112 to execute feature classification processing while selecting one by one the analysis targets specified in the analysis target 1806 of the analysis instruction information 18 for the mesh selected in step S204 (step S205). That is, in step S205, the classification unit 112 repeatedly executes feature classification processing while changing the selection of the analysis target specified in the analysis target 1806 for the mesh selected in step S204. Details of the feature classification processing will be described later with reference to FIG. 5.

Next, the land feature extraction unit 111 receives the results of the feature classification process executed in step S205 from the classification unit 112 and writes them into the feature information 19 (step S206). After the process in step S206, the process returns to step S203.

By repeatedly executing the processes of steps S204 to S206 for unprocessed meshes in this manner, all meshes in the analysis range will eventually be processed, and the high-resolution data analysis process will end after a YES in step S203. As a result of this high-resolution data analysis process, feature quantities that indicate the degree to which each mesh divided into the "analysis range" possesses the characteristics of the "analysis target" based on the high-resolution data (satellite image 114 and map information 115) are quantified and recorded in feature information 19.

FIG. 5 is a flowchart showing an example of the processing procedure for feature classification processing. The feature classification processing shown in FIG. 5 is executed when the classification unit 112 is started in step S205 of FIG. 4.

According to FIG. 5, first, the classification unit 112 obtains the mesh to be processed and the selected analysis target 1806 from the land feature extraction unit 111 (step S301).

Next, the classification unit 112 calculates the range of the mesh obtained in step S301, reads the satellite image and map information including that range from the disk 2 (or from outside via the network 3), and stores them in the memory 17 as the satellite image 114 and map information 115 (step S302).

Satellite image 114 is high-resolution data taken from an artificial satellite of the area for which the living environment is to be evaluated, and may be an aerial image or the like as long as the image has a high resolution. The satellite image or aerial image that is the source of satellite image 114 is not limited to images taken with visible light, and may be imaging data using other wavelengths (e.g., infrared light). By using high-resolution data such as satellite images, it is possible to identify real-world phenomena that are difficult to recognize or infer from a map (map information 115), such as street trees and green spaces within factories.

Map information 115 is general map data that indicates feature information for coordinates, and is used to prevent erroneous detections due to feature classification using satellite imagery 114. The map information on which map information 115 is based is not limited to a specific type of map, but may be any data that indicates "use" for any coordinate within a specified range. Specifically, this "use" may be, for example, a house, commercial facility, road, or factory, and preferably corresponds to the content of high possibility area 1135 of color identification information 113 shown in Figure 6 described below.

Next, the classification unit 112 initializes a pixel counter to 0 (step S303). The pixel counter is a counter used to accumulate the feature amount for each pixel.

Next, the classification unit 112 checks whether the processing of steps S305 to S307 has been performed for all pixels included in the mesh acquired in step S301 (step S304). If all pixels have been processed (YES in step S304), the process proceeds to step S308, which will be described later. If unprocessed pixels remain (NO in step S304), the process proceeds to step S305.

In step S305, the classification unit 112 selects color identification information 113 corresponding to the analysis target acquired in step S301. Specifically, the classification unit 112 selects, from among the color identification information related to the various analysis targets stored in the memory 17, the analysis target 1131 (see FIG. 6) whose name matches the analysis target acquired in step S301. Note that the color identification information related to the various analysis targets may not be stored in the memory 17 but may be stored externally via the disk 2 or the network 3. In this case, the classification unit 112 refers to the disk 2 or the network 3, reads the color identification information corresponding to the analysis target acquired in step S301 from among the color identification information, and stores it in the memory 17 as color identification information 113.

FIG. 6 is a diagram showing an example of color identification information 113. Color identification information 113 is data for identifying an analysis target by color, and is used to determine whether a certain point (pixel) can be the analysis target. The color identification information 113 shown in FIG. 6 has the items of analysis target 1131, red range 1132, green range 1133, blue range 1134, and high possibility area 1135.

Analysis target 1131 indicates the name of the analysis target that can be determined. Specifically, in the case of FIG. 6, analysis target 1131 is "concrete," which corresponds to "concrete" included in analysis target 1806 of analysis instruction information 18 in FIG. 3.

The red range 1132, green range 1133, and blue range 1134 are identification conditions that define the characteristics of the analysis object 1131, and the range of colors that the analysis object 1131 can take is indicated by RGB values. Specifically, in the case of Figure 6, if the RGB values of the color of a certain point (pixel) are all between "110-150," it is determined that the point is likely to be "concrete."

High possibility area 1135 indicates the use estimated for a point (pixel) that has the characteristics of analysis target 1131. Specifically, in the case of FIG. 6, if the color of a certain point (pixel) is determined to be within the above RGB value range and is likely to be concrete, the point is estimated to have a high possibility of being used for one of the following purposes: "residence," "commercial facility," "road," or "factory."

In the color identification information 113, information is set in advance for various analysis targets, including each of the analysis targets 1806 in the analysis instruction information 18. The set information may be changeable mainly by an administrator during operation of the living environment evaluation system 1.

Note that while the color identification information 113 shown in FIG. 6 is analyzed using the RGB values of the color of each pixel, the color analysis method is not limited to this, and various known analysis methods can be used, such as converting the color space of each pixel to an HSV color space before making a judgment. Also, while this embodiment uses color identification information 113 analyzed by "color," this is just one example, and any identification information analyzed by "wavelength" can be used as identification information for determining whether a certain point (pixel) corresponds to the analysis target in this invention. Therefore, the information (satellite image 114) of the point (pixel) to be compared with such identification information is not limited to an image using visible light, and may be, for example, an infrared image, etc.

Returning to the explanation of FIG. 5, after step 305, the classification unit 112 determines whether the color of each pixel in the area image corresponding to the mesh acquired in step S301 in the satellite image 114 acquired in step S302 satisfies the identification condition for the analysis target based on the color identification information 113 acquired in step S305, and increments the pixel counter value according to the determination result (step S306).

Specifically, in step S306, the classification unit 112 determines that the color of each pixel extracted from the satellite image 114 satisfies the identification condition of the analysis target when the color satisfies all of the red range 1132, green range 1133, and blue range 1134 in the color identification information 113. If it is determined that the identification condition of the analysis target is satisfied, the classification unit 112 adds "0.7" to the pixel counter value for each pixel that satisfies the identification condition. On the other hand, the classification unit 112 does not add to the pixel counter value for pixels that are determined not to satisfy the identification condition of the analysis target.

Next, for the pixel whose pixel counter was incremented in step S306, the classification unit 112 determines whether the use of the pixel's position shown in the map information 115 acquired in step S302 is included in the uses indicated in the color identification information 113, and further increments the pixel counter value according to the determination result (step S307).

Specifically, in step S307, for the pixel whose pixel counter was incremented in step S306, the classification unit 112 extracts the use of the pixel's position from the map information 115, and determines whether it matches any of the uses indicated in the high possibility area 1135 of the color identification information 113. If the use matches, the classification unit 112 further increments the pixel counter value by "0.3", and if the use does not match, no additional increment is made.

After processing in step S307, the process returns to step S304.

After that, when all pixels contained in the mesh have been processed, a YES determination is made in step S304. At this time, the classification unit 112 divides the final value of the pixel counter by the total number of pixels contained in the mesh, and returns the calculated value as the result of the feature classification process to step S206 of the land feature extraction unit 111 (step S308), and the feature classification process ends.

According to the above feature classification process, the classification unit 112 uses the satellite image 114, which is high-resolution information, to compare the color of each pixel of the satellite image 114 included in the mesh with the identification conditions of the color identification information 113, thereby enabling accurate numerical quantification of the extent to which the mesh has the characteristics of the specified analysis target. Furthermore, in the feature classification process, taking into consideration the possibility that a color match determination alone may result in an erroneous determination, the high-resolution map information 115 is used to compare the "use" as well, and this is reflected in the analysis value, thereby enabling the mesh to be analyzed with higher accuracy. Note that in the above explanation, the pixel counter addition value in step S306 is set to "0.7", and the pixel counter addition value in step S307 is set to "0.3", and "1.0" is added per pixel when both are added, but the ratio of each addition value may be set arbitrarily depending on the degree of importance placed on the color determination and the use determination, etc.

FIG. 7 is a diagram showing an example of feature information 19. Feature information 19 is data that holds the overall analysis results, more specifically, the final analysis results by the high-resolution data analysis unit 11 and the low-resolution data analysis unit 12. Feature information 19 records the analysis results of the analysis target or index for each mesh into which the analysis range is divided. Specifically, feature information 19 shown in FIG. 7 has items of vertical position 1901, horizontal position 1902, first analysis item 1903, and second analysis item 1904.

Vertical position 1901 indicates the vertical position of the mesh, and horizontal position 1902 indicates the horizontal position of the mesh. The combination of vertical position 1901 and horizontal position 1902 identifies the position of the mesh in the analysis target.

The first analysis item 1903 indicates the feature amount for each analysis item as the analysis result of the high-resolution data analysis process for each analysis target in the analysis target 1806 of the analysis instruction information 18. In the case of FIG. 7, "concrete", "greening", and "water" are exemplified as the first analysis item 1903, which correspond to the analysis targets indicated in the analysis target 1806 of the analysis instruction information 18 in FIG. 3. In step S206 of FIG. 4 described above, the result of the feature classification process by the classification unit 112 is written into one of the cells of the first analysis item 1903.

The second analysis item 1904 shows the feature amount for each analysis item as the analysis result of the low-resolution data analysis process for each index in the local information 122. In the case of FIG. 7, "quiet environment" and "convenient shopping area" are shown as examples of the second analysis item 1904, which correspond to the indexes shown in the index 1221 of the local information 122 in FIG. 10, which will be described later. In step S414 in FIG. 9, which will be described later, the analysis result is written into one of the cells of the second analysis item 1904.

Note that, as described above, each of the first analysis item 1903 and the second analysis item 1904 is merely an example, and items with arbitrary names may be configured according to the analysis target 1806 of the analysis instruction information 18 and the index 1221 of the area information 122.

(1-2-2) Low-resolution data analysis processing Fig. 8 and Fig. 9 are flowcharts (part 1, part 2) showing an example of a processing procedure of the low-resolution data analysis processing. The low-resolution data analysis processing shown in Fig. 8 and Fig. 9 corresponds to the processing of step S102 in Fig. 2, and is executed when the data mapping unit 121 is started by the low-resolution data analysis unit 12.

According to Figures 8 and 9, first, the data mapping unit 121 acquires the analysis instruction information 18 generated based on the operation by the user (step S401), and divides the analysis range specified by the analysis instruction information 18 into meshes (step S402).

Then, the data mapping unit 121 checks whether the processing from step S404 onwards has been performed on all meshes generated in step S402 (step S403). If all meshes have been processed (YES in step S403), the low-resolution data analysis process ends. If unprocessed meshes remain (NO in step S403), the process proceeds to step S404. If this is the first time, the processing from step S404 onwards has not yet been performed, so the process proceeds to step S404 via NO in step S403.

In step S404, the data mapping unit 121 selects one of the unprocessed meshes.

Then, the data mapping unit 121 checks whether the processing from step S406 onwards has been performed for the information relating to all indicators included in the regional information 122 (step S405). If the information relating to all indicators in the regional information 122 has been processed (YES in step S405), the process returns to step S403. If unprocessed indicators remain (NO in step S405), the process proceeds to step S406.

FIG. 10 is a diagram showing an example of regional information 122. Regional information 122 is low-resolution data in units of relatively wide-area "regions" such as administrative districts, and holds data values for each region for a given indicator. Regional information 122 is registered in the system in advance.

In the case of FIG. 10, the area information 122 includes, as an example, information on the indicators "quiet environment" and "convenient shopping district." The information on each indicator has the items of indicator 1221, target 1222, data name 1223, and data value 1224.

Indicator 1221 indicates the name of the indicator. Target 1222 indicates the conditions of the target to which the indicator indicated by indicator 1221 (hereinafter, the indicator) is applied. Data name 1223 indicates the specific area name of the area where an index value (data value 1224) has been obtained for the indicator. Data value 1224 indicates the index value of the indicator in the area indicated by data name 1223. In this example, the larger the value of data value 1224, the stronger the characteristics of the indicator.

Returning to the explanation of Figures 8 and 9, in step S406, the data mapping unit 121 selects one piece of regional information related to an unprocessed indicator from the regional information 122. Specifically, for example, in step S406, the data mapping unit 121 selects data related to "quiet environment," which is one of the indicators, from the regional information 122 shown in Figure 10.

Next, the data mapping unit 121 checks whether the processing from step S408 onwards has been performed on all columns in the regional information data selected in step S406 (step S407). In this case, "columns" refers to each column of data names 1223 (which may be considered as each column of data values 1224) such as "Suginami-ku," "Setagaya-ku," and "Nakano-ku" in the example data related to "quiet environment" in regional information 122 in FIG. 10. If all columns have been processed (YES in step S407), the process returns to step S405. If unprocessed columns remain (NO in step S407), the process proceeds to step S408 in FIG. 9.

In step S408, the data mapping unit 121 selects one unprocessed column from the data of the regional information selected in step S406. To be more specific, using the regional information 122 in FIG. 10, for example, the column "Suginami-ku" is selected.

Then, the data mapping unit 121 identifies the geographical range that is the target of the column selected in step S408, based on the data name 1223 and target 1222 of the column (step S409). In the previous specific example, the target range is identified as the "entire area" of "Suginami Ward" (or simply "Suginami Ward").

Next, the data mapping unit 121 checks whether the majority of the meshes selected in step S404 are within the range specified in step S409 (step S410). In the previous specific example, it checks whether the majority of the meshes are "Suginami-ku". If the majority of the meshes are within the specified range (YES in step S410), the mesh is considered to belong to that range and the process proceeds to step S411. If the majority of the meshes are not within the specified range (NO in step S410), the process returns to step S407 in Figure 8.

In this explanation, the condition in step S410 is "the majority of the meshes," but any method that can determine the range corresponding to the meshes will do, and other conditions may also be used, such as "the range identified in step S409 occupies the largest range in the mesh selected in step S404."

In step S411, the data mapping unit 121 determines the data value 1224 of the column selected in step S408 as the value of the analysis result of the low-resolution data analysis for the indicator in that mesh (the indicator selected in step S406). In the previous specific example, the data value "0.72" corresponding to "Suginami-ku" is determined as the value of the analysis result.

Then, in the subsequent steps S412 to S414, the data mapping unit 121 stores the value of the analysis result determined in step S411 in the corresponding cell of the feature information 19. To explain in more detail, the data mapping unit 121 first refers to the feature information 19 and selects a column that corresponds to the mesh (step S412). Next, the data mapping unit 121 selects a cell from the column selected in step S412 that corresponds to the index 1221 of the regional information selected in step S406 (step S413). Finally, the data mapping unit 121 enters the value determined in step S411 into the cell of the feature information 19 selected in step S413.

After processing in step S414, the process returns to step S407 in FIG. 8. If there are unprocessed columns in the regional information for the index selected in step S406 (NO in step S407), the process from step S408 onwards is repeated for the other unprocessed columns.

By carrying out the processing of steps S401 to S414 as described above, low-resolution data (regional information 122) is assigned to the fine meshes used in the high-resolution data analysis, and ultimately, for all meshes in the analysis range, the extent to which they possess the characteristics of each index held in the regional information 122 (low-resolution data) is quantified and recorded in the characteristic information 19.

(1-2-3) Index Calculation Process Fig. 11 is a flowchart showing an example of the procedure of the index calculation process. The index calculation process shown in Fig. 11 corresponds to the process of step S103 in Fig. 2, and is executed when the data integration evaluation part 131 is started by the index calculation part 13.

According to FIG. 11, first, the data integration evaluation unit 131 acquires user preference information 132 generated based on operations by the user (step S501). The user preference information 132 is automatically generated according to the contents of the input operations when the user performs input operations according to his/her preferences (for example, an operation to select the importance level for each item) in the questionnaire input area 301 on the living environment evaluation screen 300 and then presses the evaluation execution button 304.

FIG. 12 is a diagram showing an example of user preference information 132. The user preference information 132 shown in FIG. 12 has items of living environment items 1321 and importance 1322. The living environment items 1321 indicate items of the living environment that may affect the index, and are set in advance by the service provider, the system administrator, etc. With reference to the living environment evaluation screen 300 shown in FIG. 16 described later, it can be seen that the selection items in the questionnaire input area 301 correspond to the living environment items 1321 of the user preference information 132. The importance 1322 indicates the degree of preference (preference coefficient) selected by the user (user) for the item indicated by the living environment item 1321. In this example, the importance 1322 is selected from five levels, 1 to 5, and the selected value is set as the value of the preference coefficient.

Returning to the explanation of FIG. 11, following step S501, the data integration evaluation unit 131 refers to the analysis instruction information 18 and divides the analysis range specified in the analysis instruction information 18 into meshes (step S502). The analysis range specified in the analysis instruction information 18 corresponds to the area displayed in the map display area 302 of the living environment evaluation screen 300 shown in FIG. 16.

Next, the data integration evaluation unit 131 checks whether the processing from step S504 onwards has been performed on all meshes generated in step S502 (step S503). If all meshes have been processed (YES in step S503), the index calculation process ends. If unprocessed meshes remain (NO in step S503), the process proceeds to step S504.

In step S504, the data integration evaluation unit 131 selects one of the unprocessed meshes and obtains its features from the feature information 19.

Next, the data integration evaluation unit 131 checks whether associations for all indices included in the association information 133 for the mesh selected in step S504 have been processed (step S505). If all indices have been processed (YES in step S505), the process returns to step S503. If unprocessed indices remain (NO in step S505), the process proceeds to step S506.

13 is a diagram showing an example of the association information 133. The association information 133 is data that holds an association (weighting) coefficient when calculating the feature amount of a specified living environment item from the feature amount held in the feature information 19. The association information 133 shown in FIG. 13 has items of living environment item 1331 and analysis item 1332 for each index. The living environment item 1331 corresponds to the living environment item 1321 of the user preference information 132 shown in FIG. 12. The analysis item 1332 corresponds to the first analysis item 1903 and the second analysis item 1904 in the feature information 19 shown in FIG. 7. The association information 133 holds an association (weighting) coefficient for each cell for the combination of the living environment item 1331 and the analysis item 1332.

Returning to the explanation of FIG. 11, in step S506, the data integration evaluation unit 131 selects one piece of association information related to an unprocessed index from the association information 133, and internally creates a table in which the importance 1322 of the user preference information 132 is the column heading, and the feature information obtained from the feature information 19 in step S504 is the row heading.

Next, the data integration evaluation unit 131 calculates each cell of the table created in step S506 by multiplying the product of the header matrices by the coefficient indicated in the same location in the association information (step S507).

Then, the data integration evaluation unit 131 writes the sum of the values of each cell calculated in step S507 into the index value information 20 as the index calculation result (index value) of the index in that mesh (step S508).

In other words, in the process of steps S506 to S508, when calculating an index value of a certain index based on the feature values of each analysis item analyzed in the high-resolution data analysis process and the low-resolution data analysis process (feature values of feature information 19), the feature values of multiple living environment items that affect the index are calculated by weighting the feature values of each analysis item using the coefficients of association information 133, and the degree of preference by the user (importance of user preference information 132) is reflected in the calculation of the feature values of each living environment item, and the finally calculated feature values of each living environment item are summed up and stored in index value information 20 as the index value of the index.

FIG. 14 is a diagram showing an example of index value information 20. Index value information 20 is data that holds index values of each index related to the living environment for each mesh, and in the case of FIG. 14, is composed of items of vertical position 2001, horizontal position 2002, and index item 2003. Vertical position 2001 and horizontal position 2002 indicate the position of the mesh, and index item 2003 indicates the index value of the index for each indicator related to the living environment.

After step S508, the process returns to step S505 and repeats steps S506 to S508 until there are no more unprocessed indicators.

By carrying out the processing of steps S501 to S508 as described above, index values are calculated for each indicator related to the living environment and recorded in the index value information 20.

(1-2-4) Visualization Processing Fig. 15 is a flowchart showing an example of the processing procedure of the visualization processing. The visualization processing shown in Fig. 15 corresponds to the processing of step S104 in Fig. 2, and is executed when the map display unit 141 is started by the visualization unit 14.

Before explaining Figure 15, we will explain the configuration of the living environment evaluation screen 300 that is displayed to the user.

FIG. 16 is a diagram showing an example of a living environment evaluation screen 300. This is a display screen provided to the user by the living environment evaluation system 1, and in this explanation, it is continuously displayed from when the user inputs preferences before the evaluation of the living environment begins, to when the results are displayed after the evaluation of the living environment is completed.

The living environment evaluation screen 300 has a display configuration of a questionnaire input area 301, a map display area 302, an index selection box 303, and an evaluation execution button 304.

The questionnaire input area 301 is an area that displays a questionnaire that the user is asked to answer before starting the evaluation. The questionnaire input area 301 displays multiple living environment items that affect the index to be evaluated, and the importance of each living environment item is selected according to the user's preferences. User preference information 132 is generated according to the contents of the input to this questionnaire input area 301.

The map display area 302 is the area where the map is displayed. The map area displayed in the map display area 302 by the user's operation before the evaluation begins becomes the analysis range for the living environment evaluation. After the evaluation is performed, the map area displayed in the map display area 302 is divided into meshes, and a display based on the index value of the index value information 20 is performed for each mesh as the output of the evaluation result for the index selected in the index selection box 303.

The index selection box 303 is a checkbox that allows the user to select an index for evaluating the living environment. The evaluation execution button 304 is a button for instructing the living environment evaluation system 1 to execute a living environment evaluation. The user checks the index that the user wishes to check from among the indexes displayed in the index selection box 303 (in the case of FIG. 16, "nature" or "living convenience") and presses the evaluation execution button 304, which executes a living environment evaluation for the above indexes for the analysis range displayed in the map display area 302.

Returning to FIG. 15, the visualization process will be described. According to FIG. 15, first, the map display unit 141 displays a map of the analysis range in the map display area 302 of the living environment evaluation screen 300 based on the analysis instruction information 18 (step S601). In the case of the living environment evaluation screen 300 described in FIG. 16, since the analysis range is selected by the user before the evaluation begins, it is not necessary to display it again in step S601.

Next, the map display unit 141 acquires the index selected by the user in the index selection box 303 on the living environment evaluation screen 300 (step S602).

Next, the map display unit 141 divides the map displayed in step S601 into meshes according to the mesh width 1805 of the analysis instruction information 18 (step S603).

Then, the map display unit 141 checks whether the processing from step S605 onwards has been performed on all meshes generated in step S603 (step S604). If all meshes have been processed (YES in step S604), the visualization process ends. If unprocessed meshes remain (NO in step S604), the process proceeds to step S605.

In step S605, the map display unit 141 selects one of the unprocessed meshes.

Next, the map display unit 141 refers to the index value information 20 and obtains the index value corresponding to the combination of the index obtained in step S602 and the mesh selected in step S605 (step S606).

Then, the map display unit 141 depicts the index value acquired in step S606 on the mesh of the map display area 302 in a color that has transparency and a density according to the index value (step S607). In the case of FIG. 16, the mesh of the map display area 302 is depicted in a color that increases in density as the index value increases.

After step S607, the process returns to step S604, where the map display unit 141 repeats the process for each mesh, and when the rendering process is completed for all meshes (YES in step S604), the visualization process is terminated.

The map on which the evaluation results are superimposed and displayed in this way on the map display area 302 of the living environment evaluation screen 300 can show the degree of the living environment index in a form with geographical continuity, with the meshes divided regardless of the boundaries of administrative division units. Note that the map on which the evaluation results are superimposed is not limited to general maps that show geographic information. For example, when a hazard map is used as an example of a map specialized for a specific purpose, the risk information shown by the hazard map and the evaluation results of the living environment can be presented simultaneously.

As described above, the living environment assessment system 1 according to this embodiment can analyze the features of each mesh area into which the analysis range specified by the user is divided based on the low-resolution data (regional information 122) and high-resolution data (satellite image 114), and calculate and display the index values of the living environment indexes specified by the user based on the features, while reflecting the user's preferences.

In addition, when analyzing the features of each mesh area, the residential environment evaluation system 1 according to this embodiment can prevent erroneous detection by using the map information 115, which is high-resolution data.

In addition, the residential environment assessment system 1 according to this embodiment uses high-resolution data such as satellite images 114, making it possible to assess the residential environment in a more detailed manner and with a higher resolution than conventional technology, reflecting individual preferences. By using such assessment results, it is possible to obtain information that cannot be known from simple map information alone, such as the amount of greenery and the possibility of noise from roads and factories. It is also possible to quantitatively estimate the residential environment.

(2) Second embodiment Fig. 17 is a block diagram showing an example of the configuration of a living environment assessment system 10 according to a second embodiment of the present invention. The living environment assessment system 10 according to the second embodiment has a configuration in which a correction unit 21 and an analysis unit 22 are added to the living environment assessment system 1 according to the first embodiment shown in Fig. 1, and a description of the configuration and processing thereof common to the living environment assessment system 1 will be omitted.

The correction unit 21 has a function of updating the association information 133 based on the intuitive indications of the user. The correction unit 21 has an association information update unit 211 as a functional unit that executes a program.

When the residential environment evaluation system 10 displays the evaluation results of a residential environment for a certain index, the user may feel that the evaluation results deviate from his or her own sense. To accommodate such situations, the residential environment evaluation system 10 is configured to allow the user to use a specified user interface (UI) to specify the index that deviates from his or her sense and the details of the deviation. To enable the above specification, for example, an area in which an index can be selected and an area in which information indicating the details of the deviation from the selected index (e.g., too high, too low) can be added to the residential environment evaluation screen 300. Specific illustrations are omitted.

Then, when the user specifies an index that deviates from the user's own sense and the details of the deviation in the above-mentioned specified UI, the correction unit 21 starts the association information update unit 211 and executes the association information update process. The details of the association information update process will be described later with reference to FIG. 18.

The analysis unit 22 has a function of displaying past living environment evaluation results in response to a request from a user. The analysis unit 22 has a past information display unit 221 as a functional unit that executes a program.

When a user performs an operation on a specific UI to request the display of past information with past dates, the analysis unit 22 starts the past information display unit 221 and executes the past information display process. Details of the past information display process will be described later with reference to FIG. 19.

18 is a flowchart showing an example of the procedure of the association information update process, which is executed when the association information update unit 211 is started.

According to FIG. 18, first, the association information update unit 211 acquires the index that is input by the user and that deviates from the user's sense, and information indicating the nature of the deviation (too high or too low) (step S701).

Next, the association information update unit 211 refers to the association information 133 and selects the index obtained in step S701 (step S702).

Then, the association information update unit 211 checks whether the deviation acquired in step S701 is "too high" compared to the user's own feeling (step S703). If the deviation is "too high" (YES in step S703), the process proceeds to step S704, and if the deviation is "too low" (NO in step S703), the process proceeds to step S705.

In step S704, the association information update unit 211 decreases all of the coefficients of the indices selected in step S702 by a predetermined amount (5% in this example). On the other hand, in step S705, the association information update unit 211 increases all of the coefficients of the indices selected in step S702 by a predetermined amount (5% in this example).

Then, after steps S704 and S705, the association information update unit 211 starts the data integration evaluation unit 131 and the map display unit 141, and executes the index calculation process shown in FIG. 11 and the visualization process shown in FIG. 15 again (step S706).

By carrying out the processing of steps S701 to S706 as described above, the index value of the indicated index is recalculated using the association information 133 that has been updated to approximate the user's intuition (sense), and the evaluation result of that index is displayed based on the recalculated index value. In this way, the redisplayed evaluation result is closer to the user's intuition, and the living environment evaluation system 10 according to the second embodiment can increase the user's satisfaction.

In the above-mentioned association information update process, the living environment evaluation system 10 (association information update unit 211) changes the association coefficient of the index by a predetermined amount (for example, 5%) depending on the deviation of the index input by the user (too high/too low), but various variations in the amount of change in the association coefficient can be adopted. Specifically, for example, the user can input not only the deviation (too high/too low) but also the degree of deviation, and the amount of change in the association coefficient can be varied depending on the degree.

19 is a flowchart showing an example of a procedure for the past information display process, which is executed when the past information display unit 221 is started.

As shown in FIG. 19, first, the past information display unit 221 obtains the past date input by the user in an operation requesting the display of past information (step S801).

Then, the past information display unit 221 updates the satellite image 114, the map information 115, and the area information 122 to the date closest to the date obtained in step S201 (step S802). To be able to update to past data in step S802, it is advisable to timestamp the data on which the satellite image 114 is based, the data on which the map information 115 is based, and the area information 122, and store the past data for a predetermined period of time on disk 2, etc.

Next, the past information display unit 221 re-executes the living environment evaluation process in the processing order shown in FIG. 2 using the data updated in step S802 (step S803).

By carrying out the processing of steps S801 to S803 as described above, the residential environment evaluation results for the time closest to the past date specified by the user are displayed on the residential environment evaluation screen 300, allowing the user to check the chronological changes in residential environment evaluation in the same analysis range (differences between day and night, differences from 10 years ago, etc.). In addition, the current residential environment evaluation results and past residential environment evaluation results may be displayed side by side. Thus, with the residential environment evaluation system 10 according to the second embodiment, if the user is a local government official or the like, the characteristics of the area can be quantitatively evaluated, providing a basis for planning policies regarding the placement of public facilities and parks, etc.

In addition, the residential environment assessment system 10 according to the second embodiment has the same configuration and functions as the residential environment assessment system 1 according to the first embodiment, and therefore can achieve the same effects as the first embodiment.

REFERENCE SIGNS LIST 1, 10 Residential environment evaluation system 2 Disk 3 Network 4 Input/output device 11 High-resolution data analysis unit 12 Low-resolution data analysis unit 13 Index calculation unit 14 Visualization unit 15 Processor 16 Input/output device 17 Memory 18 Analysis instruction information 19 Characteristics information 20 Index value information 21 Correction unit 22 Analysis unit 111 Land feature extraction unit 112 Classification unit 113 Color identification information 114 Satellite image 115 Map information 121 Data mapping unit 122 Regional information 131 Data integration evaluation unit 132 User preference information 133 Association information 141 Map display unit 211 Association information update unit 221 Past information display unit 300 Residential environment evaluation screen

Claims (9)

1. A living environment evaluation system for evaluating a living environment based on an index specified by a user,
   User preference information that holds preference coefficients that indicate user preferences for one or more living environment items that may affect the index;
   Analysis instruction information indicating an analysis range designated by a user as a range for evaluating the living environment;
   a high-resolution data analysis unit that performs a wavelength-based analysis using an image corresponding to the analysis range, thereby analyzing the characteristics of the analysis range for each of a plurality of regions obtained by dividing the analysis range;
   an index calculation unit that calculates an index value of the index in the analysis range for each of the regions based on an analysis result of the characteristics of the analysis range and the user preference information;
   a visualization unit that displays an evaluation result based on the index value calculated by the index calculation unit on a map showing the analysis range for each of the regions;
   A living environment evaluation system comprising:
2. regional information that holds, for each of the indices, a feature amount in each of the regions that is wider than the area into which the analysis range is divided;
   a low-resolution data analysis unit that analyzes, for each of the regions, characteristics of the analysis range based on the feature amount indicated by the regional information for the region to which the region belongs, and adds the analysis result to the analysis result of the characteristics of the analysis range by the high-resolution data analysis unit;
   The living environment evaluation system according to claim 1, further comprising:
3. the high-resolution data analysis unit calculates, as an analysis result of the characteristics of the analysis range, feature amounts of a plurality of first analysis items that may affect the characteristics of the analysis range for each of the regions by analysis using the image;
   the low-resolution data analysis unit calculates, as an analysis result of the characteristics of the analysis range, a feature amount of one or more second analysis items that may affect the characteristics of the analysis range for each of the regions by an analysis using the regional information;
   The index calculation unit is
   a weighting coefficient for calculating a feature amount of each of the living environment items from the feature amount of each of the first and second analysis items, the weighting coefficient being stored for each of the indexes;
   calculating, for each of the areas, a feature value of each of the living environment items related to the index by reflecting the weighting coefficient and the preference coefficient in the feature value of each of the analysis items;
   3. The living environment evaluation system according to claim 2, further comprising: calculating, for each of said areas, an index value of said index in said area from said calculated feature amount of each of said living environment items.
4. The high-resolution data analysis unit
   holding map information indicating a use for any coordinate within said area;
   The living environment evaluation system according to claim 3, characterized in that in the analysis using the image, a wavelength analysis is performed on the image corresponding to the area on a pixel-by-pixel basis, and if the result of the wavelength analysis is suitable for the purpose indicated by the map information, a final analysis value of the wavelength analysis of the pixel is increased, and a characteristic amount of the first analysis item in the area is calculated based on the final analysis value of each pixel.
5. a user interface that allows a user to input the index and the content of the deviation when the evaluation result displayed by the visualization unit for the index designated by the user deviates from the user's sense;
   The living environment evaluation system of claim 3, further comprising a correction unit that acquires the index and the content of the deviation inputted through the user interface and corrects the value of the weighting coefficient of the index stored in the association information in accordance with the content of the deviation.
6. a storage unit that stores the image and the map including the analysis range in a time series manner;
   2. The living environment evaluation system according to claim 1, further comprising: a past information display unit that, when a time in the past is designated by a user, causes the high-resolution data analysis unit, the index calculation unit, and the visualization unit to re-execute processing using the image and the map stored in the memory unit that are close to the designated time.
7. 2. The living environment evaluation system according to claim 1, wherein the wavelength analysis using the image by the high resolution data analysis unit is image analysis for identifying colors.
8. 2. The living environment evaluation system according to claim 1, wherein the image is a satellite image or an aerial image.
9. A method for evaluating a living environment using a living environment evaluation system that evaluates a living environment based on an index specified by a user, comprising:
   The living environment evaluation system includes:
   User preference information that holds preference coefficients that indicate user preferences for one or more living environment items that may affect the index;
   Analysis instruction information indicating an analysis range designated by a user as a range for evaluating the living environment;
   having
   a high-resolution data analysis step in which the living environment evaluation system performs a wavelength-based analysis using an image corresponding to the analysis range, thereby analyzing characteristics of the analysis range for each of a plurality of areas obtained by dividing the analysis range;
   an index calculation step in which the living environment evaluation system calculates an index value of the index in the analysis range for each of the regions based on the analysis result of the characteristics of the analysis range in the high-resolution data analysis step and the user preference information;
   a visualization step in which the living environment evaluation system displays the evaluation result based on the index value calculated in the index calculation step on a map showing the analysis range for each of the areas;
   A living environment evaluation method comprising:
   

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