DISPLAY CONTROL DEVICE, DISPLAY CONTROL METHOD, AND DISPLAY CONTROL PROGRAM

Abstract

An acquisition unit (22) acquires values corresponding to the number of existing objects to be aggregated, the values being collected per a given unit of time from respective areas obtained by virtually dividing a real space, a calculation unit (24) calculates each of three different indices from the acquired values, and a control unit (26) displays, on a display unit, a visualization image in which pixel values obtained by associating the three calculated indices with three elements of a color space, respectively, are treated as pixel values indicating the areas.

Description

[Technical field]

[0001] The disclosed technology relates to a display control device, a display control method, and a display control program. [Background technology]

[0002] In order to observe and analyze collected data, it is common to simultaneously visualize multiple different indicators related to the data. The most orthodox method of visualization is the box plot, which shows multiple statistical values ​​related to the data in a single figure. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] “Box plot”, [online], Free encyclopedia “Wikipedia”, [Retrieved May 28, 2021], Internet<URL: https: / / ja.wikipedia.org / wiki / %E7%AE%B1%E3%81%B2%E3%81%92%E5%9B%B3> Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the method of using box plots to simultaneously visualize multiple different indicators related to data, when there is a large amount of data to be compared, many box plots will be lined up on the same screen, which can make it difficult for a person to visually recognize, observe, analyze, etc.

[0005] The disclosed technology has been made in consideration of the above points, and aims to visualize multiple indicators simultaneously in a comparable manner for spatially divided data. [Means for solving the problem]

[0006] A first aspect of the present disclosure is a display control device that includes an acquisition unit that acquires a value corresponding to the number of aggregation targets collected in a predetermined time unit for each area virtually divided from a real space, a calculation unit that calculates each of three different indexes from the values ​​acquired by the acquisition unit, and a control unit that displays on a display unit a visualized image in which pixel values ​​corresponding to the three indexes calculated by the calculation unit, respectively, correspond to three elements of a color space, and are used as pixel values ​​indicating the area.

[0007] A second aspect of the present disclosure is a display control method, in which an acquisition unit acquires a value corresponding to the number of aggregation targets collected in a predetermined time unit for each area virtually dividing a real space, a calculation unit calculates each of three different indexes from the values ​​acquired by the acquisition unit, and a control unit displays a visualized image on a display unit in which pixel values ​​corresponding to the three indexes calculated by the calculation unit respectively correspond to three elements of a color space as pixel values ​​indicating the area.

[0008] A third aspect of the present disclosure is a display control program that causes a computer to function as an acquisition unit that acquires values ​​corresponding to the number of aggregation targets collected in predetermined time units for each area virtually divided from real space, a calculation unit that calculates each of three different indexes from the values ​​acquired by the acquisition unit, and a control unit that displays on a display unit a visualized image in which pixel values ​​corresponding to three elements of color space, respectively, are used to indicate the area. Effect of the Invention

[0009] According to the disclosed technology, it is possible to simultaneously visualize multiple indicators for spatially divided data in a comparable manner. [Brief description of the drawings]

[0010] [Figure 1] FIG. 2 is a block diagram showing a hardware configuration of the display control device. [Diagram 2] 2 is a block diagram showing an example of a functional configuration of a display control device. [Diagram 3]FIG. 2 is a schematic diagram showing an example of collected data. [Figure 4] 11A and 11B are diagrams for explaining calculation of an average, a standard deviation, and a time axis reliability. [Diagram 5] 1 is a diagram for explaining hue (H), saturation (S), and value (V). [Figure 6] 1 is a diagram showing the relationship between hue (H) and saturation (S) when the value of lightness (V) is fixed at 100. FIG. [Figure 7] FIG. 13 is a diagram showing an example of a matrix obtained by dividing a mesh. [Figure 8] FIG. 13 is a schematic diagram showing an example of a visualized image in which each cell of a matrix is ​​colored. [Figure 9] This is an example of a display in which visualized images of each mesh are superimposed on a map. [Figure 10] FIG. 11 is a diagram showing an example of a list display of visualized images. [Figure 11] FIG. 13 is a diagram showing another example of a list display of visualized images. [Figure 12] FIG. 11 is a diagram showing an example of switching display of visualized images. [Figure 13] 13 is a flowchart illustrating an example of a display control process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an example of an embodiment of the disclosed technology will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. In addition, the dimensional ratios of the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0012] In this embodiment, an example is described in which a display control device of the disclosed technology is applied to visualize aggregated results regarding traffic volume at each location in order to consider measures to avoid congestion based on data regarding traffic volume.

[0013] Fig. 1 is a block diagram showing a hardware configuration of a display control device 10 according to this embodiment. As shown in Fig. 1, the display control device 10 has a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and a communication I / F (Interface) 17. Each component is connected to each other via a bus 19 so as to be able to communicate with each other.

[0014] The CPU 11 is a central processing unit, and executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various arithmetic processing according to the program stored in the ROM 12 or the storage 14. In this embodiment, the ROM 12 or the storage 14 stores a display control program for executing a display control process described later.

[0015] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is composed of storage devices such as a hard disk drive (HDD) or a solid state drive (SSD), and stores various programs including an operating system and various data.

[0016] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs. The display unit 16 is, for example, a liquid crystal display, and displays various information. The display unit 16 may function as the input unit 15 by adopting a touch panel system. The communication I / F 17 is an interface for communicating with other devices. For this communication, for example, a wired communication standard such as Ethernet (registered trademark) or FDDI, or a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used.

[0017] Next, a functional configuration of the display control device 10 will be described. Fig. 2 is a block diagram showing an example of the functional configuration of the display control device 10. As shown in Fig. 2, the display control device 10 includes, as its functional configuration, an acquisition unit 22, a calculation unit 24, and a control unit 26. Each functional configuration is realized by the CPU 11 reading out a display control program stored in the ROM 12 or storage 14, expanding it in the RAM 13, and executing it.

[0018] The acquisition unit 22 acquires a value corresponding to the number of tally targets collected in a predetermined time unit (hereinafter referred to as a "collection unit") for each area obtained by virtually dividing the real space. In this embodiment, the area obtained by virtually dividing the real space is a rectangular area in the real space corresponding to each mesh of a geohash obtained by dividing a map into a grid. The tally targets are vehicles that have passed through a rectangular area in the real space corresponding to each mesh, and the number of tally targets is the number of vehicles for each collection unit that have passed through the rectangular area. The value corresponding to the number may be the actual number of vehicles, or may be a smoothed number, an estimated number, or the like.

[0019] Specifically, the acquisition unit 22 acquires the collected data input to the display control device 10. As shown in FIG. 3, the collected data is data that collects the number of vehicles that pass through a rectangular area in real space corresponding to each mesh in each collection unit (10-minute units in the example of FIG. 3), and is data in which the date and time that specifies the collection unit and the number of vehicles are associated with each mesh. The collected data is collected, for example, by acquiring location information from a connected car, and correlating the acquired location information and acquisition date and time with information (for example, latitude and longitude) that specifies the range of the rectangular area in real space corresponding to the mesh and recording them. The acquisition unit 22 transfers the acquired collected data to the calculation unit 24.

[0020] The calculation unit 24 calculates three different indexes from the collected data delivered from the acquisition unit 22. The calculation unit 24 calculates, as the three indexes, a first statistical value, a second statistical value, and the reliability of at least one of the first statistical value and the second statistical value or the aggregated value itself in the time period. In the present embodiment, as an example, the calculation unit 24 calculates the average μ of the collected data for each of four time axes (described in detail below) as the first statistical value, the standard deviation σ as the second statistical value, and the time axis reliability (described in detail below) as the reliability.

[0021] The time axis is the time granularity when calculating each index. In this embodiment, four time axes are set: the entire period of collected data, each day of the week, each time period, and each day of the week and each time period. Note that this time axis is an example, and other time axes may be set, such as a monthly time axis, for one year's worth of collected data. Therefore, the calculation unit 24 calculates the average μ, standard deviation σ, and time axis reliability ω as shown in FIG. 4 by aggregating the collected data for each aggregation unit of the time axis for each of the four time axes: the entire period, each day of the week, each time period, and each day of the week and each time period.

[0022] When the time axis is the entire period, the aggregation unit is the entire period of the acquired collected data. The repetition cycle is not fixed, but it is effective for detecting frequent increases or decreases in the aggregated values. When the time axis is by day of the week, the aggregation units are each day of the week: Monday, Tuesday, ..., Sunday. When the time axis is by time period, the aggregation units are each time period: time period 1, time period 2, ..., time period N when a day is divided into N time periods. When the time axis is both day of the week and time period, the aggregation units are each day of the week and time period: Monday time period 1, Monday time period 2, ..., Sunday time period N.

[0023] In the example of Fig. 4, when the time axis is by time zone, as an example, 24 time zones are set by dividing one day (from midnight to 24:00) into one-hour intervals, and "midnight" represents the midnight hour (0:00 to 0:59). In this example, as shown in Fig. 4, the time granularity of the aggregation unit for the entire period is the coarsest, and the time granularity becomes gradually finer in the order of day of the week, time zone, and day of the week and time zone.

[0024] Specifically, the calculation unit 24 calculates the overall average μ, standard deviation σ, and time axis reliability ω from the number of vehicles for each collection unit included in the entire period of the collected data. The calculation unit 24 also classifies the number of vehicles for each collection unit included in the entire period of the collected data by day of the week, and calculates the average μ, standard deviation σ, and time axis reliability ω for each day of the week from the number of vehicles for each collection unit on each day of the week. The calculation unit 24 also classifies the number of vehicles for each collection unit included in the entire period of the collected data by time period, and calculates the average μ, standard deviation σ, and time axis reliability ω for each time period from the number of vehicles for each collection unit in each time period. The calculation unit 24 also classifies the number of vehicles for each collection unit included in the entire period of the collected data by day of the week and time period, and calculates the average μ, standard deviation σ, and time axis reliability ω for each day of the week and time period from the number of vehicles for each collection unit on each day of the week and time period.

[0025] The time axis reliability ω is an index that indicates the relative value of the number of counts in all meshes when calculating the average μ and standard deviation σ from the collected data. Here, the number of counts is the number of collection units in which the number of vehicles recorded is equal to or greater than the threshold. As described above, when collecting collected data using the location information of connected cars, in a situation in which the penetration rate of connected cars is insufficient (for example, about 20 to 30%), there may be cases in which sufficient collected data is not collected to grasp the degree of congestion, etc. For example, for mesh A, it is assumed that collected data in which the number of vehicles recorded is equal to or greater than the threshold is acquired in each collection unit from 0:30 to 0:40, 0:40 to 0:50, and 0:50 to 1:00. On the other hand, for mesh B, it is assumed that collected data in which the number of vehicles recorded is equal to or greater than the threshold is acquired only once, from 0:40 to 0:50, among the above three collection units. In this case, in mesh B, it is not possible to determine whether there was actually no congestion in the collection units from 0:30 to 0:40 and 0:50 to 1:00, or whether there was actually congestion but not a single connected car among them. Therefore, in this embodiment, the number of collection units in which a number of vehicles equal to or greater than a threshold is recorded in the collected data is defined as the number of collections, and is used to calculate the time axis reliability.

[0026] A specific example will be described below in which the threshold value is set to 1, that is, the number of collection units in which one or more vehicles are recorded is counted as the number of collections.

[0027] The calculation unit 24 normalizes the counting count for each counting unit of each time axis to calculate the time axis reliability ω. For example, the calculation unit 24 calculates the time axis reliability ω by the following formula (1). ω(T,t)=N(T,t) / maxN(T,t) (1)

[0028] Here, T is the type of time axis (entire period, by day of the week, by time slot, or by day of the week and time slot), and t is the aggregation unit (for example, entire period, Monday, between 1:00, Monday between 1:00, etc.). Therefore, ω(T, t) is the time axis reliability for aggregation unit t of time axis T. N(T, t) is the number of aggregations for aggregation unit t of time axis T in each mesh. Also, maxN(T, t) is the number of aggregations for the mesh with the highest number of aggregations among the number of aggregations for each mesh for aggregation unit t of time axis T. Thus, ω(T, t) in formula (1) is an index that indicates how large the number of aggregations for each mesh for aggregation unit t of time axis T is compared to the number of aggregations for other meshes in aggregation unit t of the same time axis T. For example, for T = time period, t = 13:00, if the number of counts in mesh A is 30 and the number of counts in mesh B, which has the highest number of counts among all meshes, is 500, the value of ω (time period, 13:00) for mesh A is 30 / 500.

[0029] In addition, the number of counts required for the calculated mean μ and standard deviation σ to be reliable varies depending on the time axis, the counting unit, the data collection period, etc. Specifically, when T = day of the week, the counting unit t has 7 levels from Monday to Sunday, and when T = day of the week and time period, the counting unit t has 168 levels (7 x 24), and the number of collection units classified into each level varies greatly. In addition, it is expected that there will be many meshes in which the number of counts varies greatly between late night and daytime, weekdays and holidays, etc. Therefore, as in the above formula (1), the appropriate time axis reliability ω can be calculated by normalizing the number of counts using MaxN(T,t).

[0030] In addition, when acquiring collected data that is not affected by the low penetration rate of connected cars as described above and does not have uncertainty in the collected data itself, other indices may be applied as the time axis reliability. For example, the influence of each time axis described in JP 2016-91040 A (an index using normalized mutual information based on the result prediction accuracy for each time axis) may be applied as the time axis reliability.

[0031] The calculation unit 24 passes the average μ, standard deviation σ, and time axis reliability ω for each aggregation unit of the time axis calculated for each mesh to the control unit 26. Note that, hereinafter, the average and standard deviation for aggregation unit t of the time axis T will also be expressed as μ(T, t) and σ(T, t), respectively, similar to ω(T, t) in the above equation (1).

[0032] The control unit 26 displays on the display unit 16 a visualized image in which pixel values ​​indicating each mesh are obtained by corresponding the average μ, standard deviation σ, and time axis reliability ω for each aggregation unit of each time axis for each mesh transferred from the calculation unit 24 to three elements of the color space. For example, the control unit 26 uses the HSV color space to specify pixel values ​​consisting of a hue (H) corresponding to the value of μ(T, t), a saturation (S) corresponding to the value of σ(T, t), and a brightness (V) corresponding to the value of ω(T, t). As shown in FIG. 5, the hue (H), saturation (S), and brightness (V) correspond to three orthogonal axes, respectively, and a pixel value (color) is uniquely determined by determining each value of the hue (H), saturation (S), and brightness (V). Note that the color space to be applied is not limited to the HSV color space, and other color spaces such as YCbCr may be applied.

[0033] Specifically, the control unit 26 assigns the minimum to maximum values ​​of μ(T, t) in all meshes to a predetermined range of hue (H) values. The control unit 26 then linearly interpolates the relationship between the hue (H) values ​​and μ(T, t) values ​​between the minimum and maximum values ​​to identify the hue (H) values ​​corresponding to each μ(T, t). Similarly, the control unit 26 assigns the minimum to maximum values ​​of σ(T, t) in all meshes to a predetermined range of saturation (S) values ​​to identify the saturation (S) values ​​corresponding to each σ(T, t). Similarly, the control unit 26 assigns the minimum to maximum values ​​of ω(T, t) in all meshes to a predetermined range of lightness (V) values ​​to identify the lightness (V) values ​​corresponding to each ω(T, t). The predetermined value ranges for each of the hue (H), saturation (S), and brightness (V) should be set to ranges that ensure the visibility of the colors of each mesh in the displayed visualized image.

[0034] FIG. 6 shows the relationship between hue (H) and saturation (S) when the value of brightness (V) is fixed at 100. In FIG. 6, the difference between the hue (H) and the saturation (S) is represented by the difference in hatching. This also applies to the figures described later, including the difference in brightness (V). For example, as shown in the range indicated by the thick frame in FIG. 6, the range of the hue (H) to be used may be set to 0 to 240, and the range of the saturation (S) to be used may be set to 20 to 100. Also, the range of the brightness (V) to be used may be set to 40 to 100. For example, a slide bar may be displayed on the display unit 16, and the user may operate the slide bar to set this range. Also, the saturation (S) and brightness (V) may be binarized values ​​by threshold processing. Also, the threshold value may be set by the user operating the slide bar.

[0035] For example, the control unit 26 may assign the maximum value of μ(T, t) in all meshes to H=0 and the minimum value to H=240 in the above-mentioned range of hue (H). Also, for example, the control unit 26 may assign the maximum value of σ(T, t) in all meshes to S=20 and the minimum value to S=100 so that the larger the standard deviation, that is, the greater the variation in collected data, the duller it appears. Also, for example, the control unit 26 may assign the minimum value of ω(T, t) in all meshes to V=40 and the maximum value to V=100 so that the lower the time axis reliability, the darker it appears. Hereinafter, the specified pixel value will also be referred to as HSV(T, t).

[0036] The control unit 26 further divides the mesh into a plurality of cells (small regions), and controls the display unit 16 to display a visualized image in which the cells corresponding to the aggregation units of each time axis are colored with a color indicated by a pixel value specified for each aggregation unit of each time axis. Specifically, the control unit 26 divides each mesh into a matrix, and assigns a time axis for each day of the week to one of the rows and columns of the matrix, and a time axis for each time period to the other. The control unit 26 then colors each cell with a color indicated by a pixel value HSV(T,t) according to the time axis corresponding to the row and column to which the cell belongs.

[0037] For example, when a time period is set by dividing a day into three-hour periods, the mesh is divided into cells of an 8-row x 9-column matrix as shown in FIG. 7. In this case, the cell in the first row and first column of the matrix (indicated by the dashed line in FIG. 7) is colored with the color indicated by the pixel value HSV (whole period, whole). The cells in the second to eighth rows of the first column of the matrix (indicated by the dotted line in FIG. 7) are colored with the colors indicated by HSV (day of the week, Monday), HSV (day of the week, Tuesday), ..., and HSV (day of the week, Sunday). The cells in the second to ninth columns of the first row of the matrix (indicated by the dashed-dotted line in FIG. 7) are colored with the colors indicated by HSV (day of the week, 0:00-3:00), HSV (day of the week, 3:00-6:00), ..., and HSV (day of the week, 21:00-24:00). The other cells of the matrix (the areas indicated by the two-dot chain line in Fig. 7) are colored with the colors indicated by the HSV (day of the week and time period, X) specified by the combination X of the day of the week corresponding to the row of the cell and the time period corresponding to the column of the cell. X is Monday and 0-3am, Monday and 3-6am, ..., Tuesday and 0-3am, ..., Sunday and 21-24am.

[0038] As shown in Fig. 4, when the time unit of the hourly aggregation is a time period divided into one hour, and as shown in Fig. 7, when the time period of the matrix is ​​divided into three hours, HSV(T,t) can be determined from the average of μ(T,t), σ(T,t), and ω(T,t) for the three hours. Fig. 8 shows an example of a matrix in which each cell is colored with the color indicated by the corresponding HSV(T,t). In Fig. 8, the boundaries of the areas on the matrix corresponding to different time axes are indicated by thick lines. The same applies to Figs. 10 to 12 described later.

[0039] The control unit 26 may display the visualized image for each mesh by superimposing it on the position of each mesh on the map, as shown in FIG. 9. The control unit 26 may display the visualized image for each mesh extracted based on a predetermined condition by arranging them in a list, as shown in FIG. 10 and FIG. 11, without being limited to the case of superimposing the visualized image on the map. The control unit 26 may display a visualized image for each mesh extracted based on a predetermined condition by arranging them in a list, as shown in FIG. 10 and FIG. 11. The control unit 26 may display a visualized image representing only the average, a visualized image representing the average and the standard deviation, a visualized image representing the average and the time axis reliability, and a visualized image representing the average, the standard deviation, and the time axis reliability, by switching between the visualized images. For example, when displaying the visualized image representing only the average, the control unit 26 may color each cell with a color represented by a hue (H) value specified from μ(T, t) and fixed saturation (S) and value (V) values ​​(for example, the maximum values ​​in a predetermined range).

[0040] Next, the operation of the display control device 10 will be described. Fig. 13 is a flowchart showing the flow of display control processing by the display control device 10. The display control processing is performed by the CPU 11 reading out a display control program from the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it. The display control processing is an example of a display control method of the disclosed technology.

[0041] In step S10, the CPU 11 functions as the acquisition unit 22 to acquire collected data input to the display control device 10, and transfers the acquired collected data to the calculation unit .

[0042] Next, in step S12, the CPU 11, as the calculation unit 24, aggregates the collected data for each mesh in the aggregation unit t of each time axis for the entire period of the collected data, for each day of the week, for each time period, and for each day of the week and time period. Thereby, the CPU 11, as the calculation unit 24, calculates each of the average μ(T,t), standard deviation σ(T,t), and time axis reliability ω(T,t). The calculation unit 24 passes each of the calculated μ(T,t), σ(T,t), and ω(T,t) to the control unit 26.

[0043] Next, in step S14, the CPU 11, as the control unit 26, specifies pixel values ​​HSV(T, t) that correspond μ(T, t), σ(T, t), and ω(T, t) for each mesh passed from the calculation unit 24 to hue (H), saturation (S), and value (V). Then, the CPU 11, as the control unit 26, divides each mesh into a matrix, and corresponds one of the rows and columns of the matrix to a time axis for each day of the week, and the other to a time axis for each time period. Then, the CPU 11, as the control unit 26, displays on the display unit 16 a visualized image in which each cell is colored with a color indicated by the pixel value HSV(T, t) according to the time axis corresponding to the row and column to which each cell belongs, and the display control process ends.

[0044] As described above, the display control device according to the present embodiment acquires collected data corresponding to the number of aggregation targets collected in a predetermined time unit for each area obtained by virtually dividing a real space, and calculates each of three different indices from the collected data. Then, the display control device displays a visualized image on the display unit in which pixel values ​​corresponding to the three calculated indices respectively correspond to three elements of a color space as pixel values ​​indicating the area. This makes it possible to simultaneously visualize multiple indices for spatially divided data in a comparable manner.

[0045] The display control device also uses the average, standard deviation, and the reliability of the aggregation as three indices, and displays them as one visualized image. By displaying the reliability of the aggregation, it becomes easier to decide whether to take measures based on the aggregation results so far, or to further collect and aggregate data, etc.

[0046] The display control device also expresses the results of the aggregation for each of the different time axes in one visualized image. This allows the trend of the time-series data to be understood from one visualized image without switching the screen. Furthermore, by making the different time axes correspond to the rows and columns of the matrix, the granularity of the aggregation can be made finer than when box plots are compared side by side, and the content of the expression of the aggregation results is enriched. Note that if the granularity of the aggregation is made too fine, for example, to one minute, the aggregated results will lack readability. In this embodiment, the aggregated results are handled for a certain period of time, and instead, the reliability and standard deviation based on the number of aggregations are expressed, so that the trend of each time axis of the collected data can be easily understood from one visualized image.

[0047] For example, the effect of this embodiment will be specifically described in the case of visualizing the aggregated results of traffic volume for each location as in this embodiment. In a visualized image in which a hue is simply assigned to the average, it is not possible to distinguish whether the traffic volume in that time period is always high, even if the average is large, or whether there are times when the traffic volume is high and times when it is low, due to the large variation. For example, if the average is 100 vehicles, it is not possible to distinguish whether there are always 100 vehicles, or whether there are times when there is 1 vehicle or 1,000 vehicles in the same time period, based on the average alone. By assigning saturation to the standard deviation to represent the variation in data as in this embodiment, the standard deviation can be represented in one visualized image together with the average. This makes it easier to determine whether to apply congestion avoidance measures or to collect and aggregate additional data.

[0048] Furthermore, compared to when the average and standard deviation are calculated from an abundant amount of data (number of tabulations), when they are calculated from a small amount of data (for example, only 2 to 3 tabulations), the reliability of the average and standard deviation is low. By assigning brightness to the reliability as in this embodiment, the reliability can be expressed in one visualized image together with the average and standard deviation. This makes it possible to determine that additional data needs to be collected and tabulated when the reliability of the tabulation results is low.

[0049] More specifically, for locations where the average is large, the standard deviation is small, and the reliability is high, i.e., locations where highly certain results are obtained that the location is always congested, a decision can be made to take regular congestion avoidance measures such as increasing the number of lanes. Also, for locations where the average is large, the standard deviation is large, and the reliability is high, additional data is collected and collected with a finer time granularity to identify the congested time period. Then, for the identified time period, a decision can be made to take temporary congestion avoidance measures such as adjusting the time when traffic lights switch or proposing a detour route to avoid congestion in route guidance by a navigation system. If the standard deviation is large even when the granularity is finer, it is possible to take measures such as only proposing temporary congestion avoidance measures such as a detour route, and not increasing the number of lanes or adjusting the traffic lights. Also, if the reliability is low, a decision can be made to collect and collect additional data until sufficient reliability is obtained.

[0050] Furthermore, as shown in FIG. 9, when a visualized image is displayed in association with a mesh on a map, visualized images for a plurality of meshes can be compared while taking into account their positional relationships in real space.

[0051] For example, Figure 9 visualizes the trends in each mesh created in Daiba, Minato Ward, Tokyo, approximately 150m square, on a map. It can be observed that congestion only occurs at certain times in meshes away from the intersections on the main roads surrounded by solid lines. On the other hand, in meshes near the intersections of the main roads surrounded by dotted lines, near the single road in the upper left connecting Daiba and Toyosu, and near the foot of the bridge connecting Daiba and Tatsumi in the upper right, it can be observed that vehicles tend to concentrate and become very crowded at many times of the day. By simultaneously observing the map and the time trends of traffic volume changes for each mesh, it is possible to observe the influence of geographical conditions such as a single road, an intersection, and the foot of a bridge on congestion trends. In addition, in areas where multiple adjacent meshes are simultaneously surrounded by dotted or solid lines, it can be observed that meshes that are geographically close to each other have similar congestion trends.

[0052] In addition, as shown in Figures 10 and 11, when visual images for multiple meshes extracted based on some conditions are displayed in a list, it is possible to easily compare visual images for multiple related meshes.

[0053] For example, FIG. 10 shows a list of meshes (condition 1) that are congested only during a specific time period or day of the week. The meshes corresponding to the visualized images included in FIG. 10 are meshes on the map shown in FIG. 9 that are framed with a thick solid line. From the display example of FIG. 10, for example, it is possible to confirm information such as that the places where the average is large from late night to morning on a specific day of the week, i.e., the places where congestion occurs are often far from intersections. Also, FIG. 11 shows a list of meshes (condition 2) that are congested all the time, i.e., the places where the average for the entire period is equal to or greater than a predetermined value. The meshes corresponding to the visualized images included in FIG. 11 are meshes on the map shown in FIG. 9 that are framed with a dashed line. From the display example of FIG. 11, for example, a place with a large standard deviation or a place with a low time axis reliability can be specified as a place that requires further detailed aggregation and analysis. Also, a place with a large average, a small standard deviation, and a high time axis reliability can be specified as a place where regular congestion avoidance measures need to be taken.

[0054] In addition, in Fig. 10 and Fig. 11, visualized images connected by a line indicate that the distance between the visualized images in the corresponding real space is close. In the case where the distance in the real space is close, more specifically, where the corresponding visualized images are similar between adjacent meshes on the map, it is possible to thin out the aggregation, such as by performing aggregation only for one of the meshes in the future. By thinning out the aggregation, it is possible to realize high speed and low cost of the entire processing. In addition, for example, when the aggregation of one of two meshes is thinned out, it may be considered that the same aggregation result is obtained for the two meshes. In this case, when the visualized image is superimposed on the map, the visualized image may be displayed in one of the two meshes, or may be displayed in the middle position between the two meshes.

[0055] That is, in the visualized image displayed by this embodiment, it is possible to perform clustering of the tabulated results while incorporating human knowledge, such as showing that places close to each other have similar traffic volume trends. In addition, for the clustering of the mechanically performed tabulated results, it is possible to expect an accountability effect for the results, such as showing that a single road connecting places with high traffic volume is intermittently congested and therefore congestion countermeasures are necessary, by showing both the similarity of statistical information and geographical trends. In addition, unlike the results spit out by machine learning as a black box, it is possible to find characteristics such as places with similar traffic volume trends being close to each other and being about the same distance from an intersection. This increases the persuasiveness of measures according to the content determined based on the visualized image, making it easier to obtain the agreement of people who are not familiar with machine learning or who do not trust machine learning.

[0056] FIG. 12 is a display example of a visualized image of a mesh with a large standard deviation in the midnight to morning time slot and a large time axis reliability for each day of the week. As described above, FIG. 12 shows, from the left, a visualized image showing only the average, a visualized image showing the average and the standard deviation, a visualized image showing the average and the time axis reliability, and a visualized image showing the average, the standard deviation, and the time axis reliability. Even in cells with the same average number of vehicles and colored with a color of almost the same hue (H) (e.g., green), the saturation (S) is small in the part with a large standard deviation, and it is displayed dull. In addition, it shows that the variation in the number of vehicles is small in the afternoon to night time slot, and there are locally congested time slots. In addition, it shows that the variation in the number of vehicles is large in the midnight to morning time slot from Monday to Saturday, and there is no congestion at all on Sunday, and it can be seen that the degree of congestion is significantly different between Sunday and other days of the week.

[0057] Based on the information that can be understood from the visualized image as described above, decisions can be made, for example, to take regular congestion avoidance measures, to take temporary congestion avoidance measures only during busy hours, or to collect and compile additional data.

[0058] Furthermore, the comparison of visualized images is not limited to comparing locations (meshes) with each other, but may display visualized images of the same location, for example, comparing visualized images before and after the application of congestion avoidance measures.

[0059] As a conventional technique, for example, there is a technique in which different indices are associated with different layers and are displayed on the same screen while expressing the time-wise trends with animation, such as the 3D map function in Microsoft Excel (registered trademark). In such a conventional technique, since the indices are not associated with each other, it is necessary to check each indices by switching layers, etc., and it lacks visibility. In addition, when displaying the time-wise trends of data with animation, there are problems such as difficulty in finding an interesting moment in the animation and pausing it, and difficulty in comparing values ​​at different times. In contrast, in the display control device according to the present embodiment, the relationship between the indices and the time-wise trends of the indices are displayed on the same screen, so that the relationship between the indices and the time-wise trends of the indices can be grasped without switching layers or pausing the animation.

[0060] There is also a visualization method in which a color is assigned to each piece of data to distinguish them, and icons representing each piece of data are displayed on the same screen using the assigned color. For example, as shown in Reference 1 below, there is a technology that assigns the feature values ​​of songs to three axes in a color space and expresses each of the multiple songs included in a playlist with a color. Also, as shown in Reference 2 below, there is a technology that visually expresses the progress of events that are trending on social media by assigning a hue to each event.

[0061] Reference 1: Tomomi Uota, Takayuki Ito, “GRAPE: Visualizing music by playlist using gradient images”, DEIM Forum 2012 C3-2, 2012 Reference 2: Kazuo Misue, "Visualization of Social Data with Time Information," Visualization Information, Vol. 36 No. 141, April 2016

[0062] The above-mentioned conventional techniques are visualization methods that can increase the variety of icons representing each data, making it easier to distinguish each data. However, these visualization methods do not take into consideration the simultaneous comparison of multiple indicators and their time trends for spatially divided data. In contrast, the display control device according to the present embodiment makes it possible to simultaneously compare multiple indicators and their time trends for spatially divided data.

[0063] In the above embodiment, the disclosed technology is applied to a use case in which the aggregated results of traffic volume for each location are visualized. However, the disclosed technology can be applied to other use cases. For example, the disclosed technology can be applied to a use case in which demographic statistics for each location are visualized and used for urban planning and congestion prediction, and energy consumption is visualized and used for air conditioning optimization. In addition, the disclosed technology can be applied to any other object that can handle the number of each area, that is, density. For example, the disclosed technology can be applied to a case in which the flow rate of fish in each area of ​​the ocean, the number of features in each area, the amount of data transmitted in a network, etc. are targeted, and multiple indicators that are the aggregated results of these are visualized on the same screen.

[0064] In the above embodiment, operations such as enlarging, reducing, and translating the visualized image may be accepted. Also, a clustering result of visualized images having similar tendencies may be displayed. The clustering of visualized images may use the similarity of pixel values ​​HSV(T, t) included in the visualized images, or may use a model in which a user assigns a label to each visualized image and causes it to learn. Also, for a selected mesh, a visualized image as in the above embodiment may be displayed, and an animation depiction of the change in the number of vehicles in a collection unit (10 minutes in the example of the above embodiment) may be displayed.

[0065] In the above embodiment, the display control process executed by the CPU by reading the software (program) may be executed by various processors other than the CPU. In this case, examples of the processor include a PLD (Programmable Logic Device) such as an FPGA (Field-Programmable Gate Array) whose circuit configuration can be changed after manufacture, and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) which is a processor having a circuit configuration designed exclusively for executing a specific process. The display control process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, and a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0066] In addition, in each of the above embodiments, the display control processing program is described as being pre-stored (installed) in the ROM 12 or the storage 14, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0067] Regarding the above embodiment, the following supplementary notes are further disclosed.

[0068] (Additional note 1) Memory, at least one processor coupled to the memory; Including, The processor, A value corresponding to the number of objects to be counted, collected in a predetermined time unit for each area obtained by virtually dividing the real space, is obtained; Calculating each of three different indices from the obtained values; A visualized image is displayed on a display unit in which pixel values ​​corresponding to the three calculated indices and the three elements of the color space are used as pixel values ​​indicating the area. A display control device configured as follows.

[0069] (Additional note 2) A non-transitory recording medium storing a program executable by a computer to execute a display control process, The display control process includes: A value corresponding to the number of objects to be counted, collected in a predetermined time unit for each area obtained by virtually dividing the real space, is obtained; Calculating each of three different indices from the obtained values; A visualized image is displayed on a display unit in which pixel values ​​corresponding to the three calculated indices and the three elements of the color space are used as pixel values ​​indicating the area. Non-transitory recording media, including [Explanation of symbols]

[0070] 10 Display control device 11 CPU 12 ROM 13 RAM 14. Storage 15 Input section 16 Display 17 Communication I / F 19 Bus 22 Acquisition Department 24 Calculation section 26 Control Unit

Claims

1. an acquisition unit that acquires a value corresponding to the number of aggregation targets that are collected in a predetermined time unit for each area that is obtained by virtually dividing a real space; a calculation unit that calculates a first statistical value, a second statistical value, and a reliability of at least one of the first statistical value and the second statistical value, as three different indexes from the value acquired by the acquisition unit; a control unit that displays on a display unit a visualized image in which pixel values ​​obtained by corresponding the three indices calculated by the calculation unit to three elements of a color space are used as pixel values ​​indicating the region; and A display control device comprising:

2. The control unit sets the pixel value to a pixel value having a hue corresponding to the first statistical value, a saturation corresponding to the second statistical value, and a brightness corresponding to the reliability. The display control device according to claim 1 .

3. The display control device according to claim 2 , wherein the control unit increases the brightness as the reliability increases.

4. 4. The display control device according to claim 2, wherein the reliability is an index that indicates a relative value of the number of times the number of times collected in the predetermined time unit is counted.

5. The calculation unit calculates the three indexes for each of the granularities by aggregating the values ​​for each of a plurality of different granularities, The control unit further divides the region into a plurality of small regions, and displays pixel values ​​corresponding to the three indexes for each of the granularities as pixel values ​​indicating the small regions corresponding to each of the granularities. The display control device according to any one of claims 1 to 4.

6. the calculation unit calculates the three indicators for each granularity by aggregating the values ​​for the entire period in which the numbers were collected, for each day of the week, for each time period, and for each day of the week and time period; The display control device according to claim 5, wherein the control unit divides the region into small regions in a matrix shape, corresponds the day of the week to one of the rows and columns of the matrix and the time period to the other, and displays pixel values ​​corresponding to the three indicators of granularity according to the row and column to which the small region belongs as pixel values ​​indicating the small region.

7. The acquisition unit acquires a value corresponding to the number of aggregation targets present, the value being collected in a predetermined time unit for each area obtained by virtually dividing a real space; a calculation unit calculates a first statistical value, a second statistical value, and a reliability of at least one of the first statistical value and the second statistical value, as three different indexes from the value acquired by the acquisition unit; A control unit displays a visualized image on a display unit in which pixel values ​​obtained by corresponding the three indices calculated by the calculation unit to three elements of a color space are used as pixel values ​​indicating the region. Display control method.

8. Computer, an acquisition unit that acquires a value corresponding to the number of aggregation targets present, the value being collected in a predetermined time unit for each area obtained by virtually dividing a real space; a calculation unit that calculates a first statistical value, a second statistical value, and a reliability of at least one of the first statistical value and the second statistical value, as three different indexes from the value acquired by the acquisition unit; and A control unit for displaying on a display unit a visualized image in which pixel values ​​obtained by corresponding the three indices calculated by the calculation unit to three elements of a color space are used as pixel values ​​indicating the region. A display control program that functions as a

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