JP6472589B1 - Map data processor - Google Patents

Abstract

The present invention relates to a map data processing device, which is a map data processing device that complements map data of a data missing region in which attribute data is missing in map data including feature attribute data, and searches a plurality of regions. Based on the similar area search unit that acquires similar areas similar to the missing data area and the map data of the similar area, the supplementary data construction that generates the supplemented map data that complements the missing data in the map data of the missing data area A data analysis unit that performs analysis based on the map data including the supplemented map data, a consistency determination unit that determines suitability of the analysis result based on the supplemented map data in the data analysis unit, and consistency determination A data output unit that outputs the supplemented map data when the determination result by the unit is appropriate.

Description

  The present invention relates to a map data processing apparatus, and more particularly to a map data processing apparatus that performs area analysis using map data.

  In business entities such as local governments, retailers and delivery companies, map data is used in various operations such as various planning and business evaluations. For example, a local government makes a city plan by performing regional analysis using map data, and a retailer makes a store opening plan.

  Such map data is composed of a set of features constituting a map such as roads and buildings. And, for the features, geometric information that expresses the shape with coordinate strings such as latitude and longitude, attribute information that shows the characteristics of the features such as road name and construction year, and phase information that shows the positional relationship between the features It consists of

  In order to create detailed map data, it is necessary to investigate the geometry and attributes of individual features in detail, which requires a lot of costs such as time and cost. In general, in order for small-scale local governments and retailers to perform high-service operations using map data, wide-area and detailed map data may be required. However, there is a problem that a part of the map data is lost by creating only the map data of the high priority area.

  In order to supplement the missing data when the map data is missing, for example, in the map information display processing device disclosed in Patent Document 1, detailed map information is displayed when the map is enlarged on the display screen. In order to display an area that does not have an image without deteriorating its appearance, a technique of performing complementary display using map information with a low level of detail (small scale) is disclosed.

  Patent Document 2 discloses a technique for acquiring similar image features from surrounding image information accumulated in the past and supplementing the missing portion when road surface image information necessary for traveling control is missing. Has been.

JP-A-10-293534 Japanese Patent Laid-Open No. 2006-126605

  As described above, Patent Document 1 discloses a technique for complementing a missing portion of map information using map information having a lower level of detail. In Patent Document 2, image information is lost. Is only disclosed by using similar image information in the vicinity, and the technology for supplementing even the attribute information necessary for performing regional analysis using map data is disclosed. Absent.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a map data processing apparatus capable of complementing even attribute information necessary for performing regional analysis or the like.

A map data processing apparatus according to the present invention is a map data processing apparatus that supplements map data in a data missing region in which attribute data is missing in map data including attribute data of a feature, and a plurality of search patterns A plurality of regions using each of the plurality of search patterns, a similar region search unit that acquires similar regions similar to the data missing region for each of the plurality of search patterns, and the plurality of search patterns acquired for each of the plurality of search patterns based on the map data of similar areas, based on the complemented map data complement the missing data in the map data of the data missing area and supplementary data constructing unit that generates each of the map data including each of the complemented map data a data analyzing unit for analyzing Te, each of said complementary generated by the supplementary data constructing unit Said complemented consistency determination unit determines the appropriateness of the map data by comparing only map data with each other, when the determination result by the consistency determination section is appropriate, the data output unit which outputs the complemented map data And.

  According to the above map data processing apparatus, even if there is a data missing area where attribute data used for area analysis or the like is missing, the missing data is supplemented using the map data of the similar area. Can be executed.

It is a block diagram which shows the structure of the map data processing apparatus of Embodiment 1 which concerns on this invention. It is a block diagram which shows the hardware constitutions of the map data processing apparatus of Embodiment 1 which concerns on this invention. It is a figure which shows the structure of map data. It is a figure which shows the kind of geometric shape. It is a figure which shows the structure of the geometric information of a feature. It is a figure which shows the structure of the phase information of a feature. It is a figure which shows the structure of the attribute information of a feature. It is a figure which shows the structure of complementary data. It is a figure which shows missing data. It is a flowchart explaining the whole process of the map data processing apparatus of Embodiment 1 which concerns on this invention. It is a flowchart explaining the similar area search process in a similar area search part. It is a flowchart explaining the supplement data construction process in a supplement data construction part. It is the figure which superimposed the mesh on the map in Embodiment 2 which concerns on this invention. It is a figure explaining preservation | save of the complementary data in Embodiment 3 which concerns on this invention.

<Embodiment 1>
FIG. 1 is a functional block diagram showing the configuration of the map data processing apparatus 1 according to the first embodiment of the present invention. As shown in FIG. 1, the map data processing apparatus 1 includes a similar area search unit 2, a complementary data construction unit 3, a data analysis unit 4, a consistency determination unit 5, and a data output unit 6.

  FIG. 2 is a block diagram illustrating an example of a hardware configuration of the map data processing apparatus 1. The map data processing device 1 shown in FIG. 2 is incorporated in an information terminal device such as a personal computer (personal computer), a smartphone, a mobile phone, and a PDA (Personal Digital Assistant), for example, and each function is performed by hardware of the information terminal device. Realized.

  The external storage device 206 shown in FIG. 2 includes a database such as a map database used in the similar area search unit 2, a data access program for accessing the database, an application program for executing a desired function using the map data, and an application Data used in processing is stored (stored). Programs such as a data access program and an application program are expanded from the external storage device 206 onto the memory 205 and executed by a CPU (Central Processing Unit) 204.

  The application program is a program for realizing various functions executed by the map data processing apparatus 1, for example, a similar area searched by the similar area search unit 2 executed by the similar area search unit 2, and Realize a function to compare the data missing area and evaluate the similarity.

  The data access program is a program for accessing a database and acquiring information necessary for executing an application program from the database. By executing this data access program, the function of the similar area search unit 2 is realized by acquiring necessary map data from the map database managed by the external storage device.

  The external storage device 206 includes a hard disk device (HDD) in which the map data processing device 1 is incorporated, an external storage medium such as a CD (Compact Disc) and a DVD (Digital Versatile Disk) that can be played back by a drive device, and a predetermined input / output interface. It is possible to construct a USB (Universal Serial Bus) memory or the like that can read and write data via the.

  The calculation result by the CPU 204 is output to the display device 202 via the bus and displayed on the screen. The display device 202 is realized by a CRT monitor, a liquid crystal display, a plasma display, or the like.

  When the communication device 203 communicates with an external device (not shown) and receives GPS signals and FM radio waves from a GPS (Global Positioning System) satellite, the communication device 203 is managed by an external server system connected via the Internet or an intranet. Used when communicating with existing external information.

  The input device 201 is a device that receives an operation input from the outside, and is realized by a keyboard, a mouse, an operation switch, and a touch panel incorporated in the display device 202.

  With the hardware configuration described above, each function of the map data processing apparatus 1 of FIG. 1 is realized. For example, the similar area search unit 2, supplementary data construction unit 3, data analysis unit 4, and consistency determination unit 5 in FIG. 1 execute a program expanded on the memory 205 from the external storage device 206 by the CPU 204 in FIG. 2. Is realized.

  The constituent elements of the map data processing apparatus 1 may be configured as separate information terminal apparatuses on a network such as the Internet and an intranet, in addition to the case of being configured by one information terminal apparatus as described above. good. For example, a configuration may be adopted in which the similar area search unit 2 having a high processing load is distributed to a plurality of information terminal devices for processing.

  Here, returning to the description of the block diagram of FIG. 1, each function of the map data processing apparatus 1 will be described. The similar area search unit 2 searches each map data of a plurality of areas, and acquires similar areas having the same characteristics as the data missing area, that is, similar. In order to find a similar area, map data of a data missing area is also used.

  The map data used in the similar area search unit 2 is classified and managed for each type (layer) of features constituting the map, and is divided in units of rectangles or assigned with a spatial index for easy data access. Are managed. The map data is stored in the external storage device 206 shown in FIG.

  Depending on what the map data is intended for, the degree of detail and configuration of the map geometric information and attribute information differ. For example, when performing a simulation to estimate crime-prone areas for crime prevention plans, the ratio of daytime population, distance to the nearest station (distance to the nearest station), time distance to the city center (distance in the city center), road area ratio Attribute information (attribute data) that is not included in a general commercial map is required, such as the ratio of commercial and residential areas and the ownership ratio.

  In the similar area search unit 2, for example, based on map data in which attribute information such as population, area, main industry, and green space ratio is stored, a feature amount is quantitatively expressed using a statistical analysis method, Similarity is determined by the magnitude of the value, and a similar area is acquired. The main analysis method is multivariate analysis. Multivariate analysis is a statistical technique for summarizing and predicting certain events using a plurality of variables that are related to each other, and examples include multiple regression analysis, quantification analysis, and cluster analysis. Multiple regression analysis is a technique for predicting one variable from a plurality of variables expressed quantitatively. In quantification analysis, quantification type 1 and quantification type 2 are used depending on whether the data form of the variable is qualitative or quantitative. The cluster analysis is an analysis method in which groups having different properties are grouped by groups having similar properties to each other using any of the three types of analysis methods.

  Each analysis method has its characteristics, and depending on the data to be used, there is a direction of analysis and unsuitability. The determination of similarity can be determined to be similar if, for example, values such as category scores and sample scores obtained by the respective analysis methods are close.

  Further, the similar area search unit 2 can also search for a similar area using attribute information other than missing attribute data (missing data). That is, in the similar area search unit 2, the user selects a data missing area via a display device 202 (FIG. 2) such as a CRT monitor or a liquid crystal display included in the map data processing apparatus 1, and which map data is used. Settings such as whether to search for similar areas can be made. Selection of the area and map data is performed using the input device 201 (FIG. 2) which is a user interface such as a mouse and a keyboard.

  Further, it is assumed that the similar area search unit 2 searches for similar areas using a plurality of search patterns. In that case, based on the map data of the similar region calculated as a result, the complementary data construction unit 3 generates complementary data (complemented map data) that complements the missing data of the data-missing region. The complementary data is tagged for each search pattern and output from the data output unit 6. An attribute name is used as this tag.

  As an example of a plurality of search patterns, for example, search pattern 1 for calculating a similar area by using three indicators of the number of persons per household, population density, and area floor area ratio, daytime population ratio, time distance to the city center, and home ownership rate are 3 A search pattern 2 for calculating a similar area with one index can be cited. The search pattern 1 is a pattern viewed from the viewpoint of densely populated houses, and the search pattern 2 can be said to be a search pattern viewed from a bed town-like property.

  In such a case, as a tag, for example, by using “house densely populated area” for search pattern 1 and “bed town” for search pattern 2, what kind of viewpoint is used to calculate complementary data? And the user can compare the results of regional analysis for each search pattern.

  The complementary data construction unit 3 generates complementary data based on the map data of the similar area searched by the similar area search unit 2. In the generation of the complementary data, the map data of the data missing region and the map data of the similar region may be compared, and the map data of the similar region may be corrected according to the difference between the two map data. For example, a method of creating complementary data using a difference in similarity obtained by a statistical method used in the similar area search unit 2 can be considered.

That is, if B city is a missing area, and the similar area is calculated using data that is generally maintained nationwide such as the national census, which is general statistical data, the area of A city is 20 km ² , When the area is 40 km ² , it is assumed that city A is calculated as a similar area from the viewpoint of the growth potential of the city. In this case, it is assumed that A city is 80% similar to B city. For example, if an index of annual road construction total extension is used as an indirect index indicating the growth potential of a city, if city A is 50 km and that of city B is missing, the area of city B is 2 of city A. Therefore, simply adding the annual total road construction in B city by area ratio will be 100 km. However, since the similarity of City A is 80%, it is assumed that there is an error of 20% for 100% of similarity, and the total length of road construction in City B is complemented as 80km to 120km (± 20km). Can do.

  In addition, a method of deriving complementary data from the ratio of map data of both regions using similar map data and data missing regions can be considered. For example, in a set of explanatory variables expressing the crime occurrence rate, it is also conceivable to calculate a similar region using only the indices possessed by both A city and B city. In this case as well, the same is true for deriving complementary data from the ratio indicating similarity.

  The data analysis unit 4 performs regional analysis such as simulation and statistical analysis using the map data stored in the external storage device 206 including the complementary data generated by the complementary data construction unit 3. The user decides what kind of analysis is performed, and the data analysis unit 4 executes the analysis decided by the user. At this time, the user can execute the analysis without being aware of the fact that the map data includes the data missing region and using the complementary data complementing the data missing region.

  The analysis result in the data analysis unit 4 is presented to the user via a display device 202 such as a CRT monitor or a liquid crystal display provided in the map data processing device 1.

  The consistency determination unit 5 determines whether the analysis result obtained by the data analysis unit 4 is appropriate. If the supplementary data is not generated correctly, it is assumed that the analysis result in the data analysis unit 4 is significantly different from the expected one, and the suitability of the analysis result is determined based on the difference between the analysis result and the assumed value that is assumed in advance. judge. For example, if the crime rate in City A determined based on the supplementary data is 100 times the crime rate in City B adjacent to City A, which is the expected value, the analysis result is determined to be inappropriate. Is done.

  In addition, when only map data for a certain year is missing, if the complementary data is different from the trend of the map data for the previous and subsequent years, it can be determined that the complementary data has not been generated correctly.

  Note that the suitability of complementary data may be determined instead of the suitability of analysis results. Whether or not the complementary data is correct can be determined, for example, by comparing complementary data obtained by a method of searching for similar regions using a plurality of search patterns. For example, if the deviation of the complement data is within ± 10%, it can be determined that the complement has been correctly completed. It is also conceivable that the user determines the analysis result and improves the complementation accuracy based on the tendency.

  The data output unit 6 has a function of outputting the complementary data generated by the complementary data construction unit 3 and determined to be correct by the consistency determination unit 5 to a storage medium such as the external storage device 206. By outputting the complementary data in this way, it can be reused when it becomes necessary to use the complementary data again. The complementary data is stored in an existing map database with a flag indicating that it is complementary data.

  Further, by storing the complementary data and the map data in the external storage device 206 that is separate from the map data processing device 1, the complementary data and the map data can be used in an analyzer other than the map data processing device 1. .

<Map data>
FIG. 3 is a diagram illustrating an example of the configuration of map data. The map data is managed using a relational database and a file system. Below, the structure of map data is demonstrated by taking the map database 20 (henceforth "map DB20") which manages map data using a relational database among these as an example.

  The map database DB 20 manages map data based on the data structure shown below. That is, the map DB 20 is a set of features 21 that are constituent elements of map data, and the features 21 are composed of a combination of geometric information 31, attribute information 41, and phase information 51. The geometric information 31 is information in which position information based on coordinates such as latitude and longitude for expressing the shape of a feature is arranged. The attribute information 41 is information indicating the feature characteristics. The phase information 51 is information indicating the connection between features.

  These pieces of information can be combined. For example, the phase information 51 and the attribute information 41 are used to indicate a road network, and the geometric information 31 and the attribute information 41 are used in combination to indicate a road shape. In the combination of the geometric information 31 and the phase information 51, each information is managed as an array and is associated with the attribute information 41, respectively. Hereinafter, the association between the geometric information 31 and the attribute information 41 will be described. The same applies to the association between the phase information 51 and the attribute information 41.

  FIG. 4 is a diagram illustrating an example of main geometric shape types (geometric type 31 a) indicated in the geometric information 31. The geometric type 31a includes, for example, point (point) data representing representative points of city names and place names, polyline (line) data representing lines such as rivers, bus routes, contour lines, etc. , Polygon (surface) data representing closed spaces such as parks and lakes. As attribute information 41 expressing these features, for example, in the case of roads, road types such as national roads, prefectural roads, municipal roads, road widths, road names, and the like are defined.

  FIG. 5 is a diagram illustrating an example of the configuration of the geometric information 31 of a plurality of features. As shown in FIG. 5, the geometric information 31 manages not only the geometric type 31a but also the arrangement of coordinate points (coordinate arrangement) constituting the feature shape, the feature ID for uniquely identifying the feature, and the like. May be. A coordinate point is expressed by numerical information defined by latitude, longitude, and a plane rectangular coordinate system. Depending on the characteristics of the map data (map information), the arrangement of coordinate values may be encrypted or compressed from the viewpoint of security.

  FIG. 6 is a diagram illustrating an example of the configuration of the phase information 51. As shown in FIG. 6, the phase information 51 is information indicating the connection between features, and is expressed by, for example, the node 1 and the link 1. The phase information 51 manages network data such as a road network and a railway network. A node is a node that constitutes a network such as an intersection, and expresses a start point, an end point, and the like. A link is an element such as a line that connects nodes.

  FIG. 7 is a diagram illustrating an example of a table structure 61 that stores attribute information of a plurality of features. The table structure 61 includes, for example, a feature ID that uniquely identifies a target feature, and a plurality of types of attribute information that indicates the features of the feature. For example, when the feature is a road, as the attribute information, a road type such as a national road, a prefectural road, a municipal road, a road width, a road name, and the like are defined. The attribute value type includes various information that characterizes the feature such as a numerical value, a character string, and an image. A set of attribute information for a plurality of features is an attribute information list.

  FIG. 8 is a diagram illustrating an example of a table structure of attribute information of map data in which complementary data is stored. As shown in FIG. 8, the table structure 71 is composed of attribute information of map data of an area including a data missing area and a complementary data flag indicating whether or not it is complementary data. For example, ID2 is a data missing region where the attribute information Attr5 is missing, and the attribute information Attr5 is complementary data generated by the complementary data construction unit 3, and a complementary data flag indicating that it is complementary data is displayed. True. On the other hand, ID1 is not a data missing area where attribute information Attr5 is missing, but holds Attr5, so the complementary data flag is False.

  The table structure 71 shown in FIG. 8 is managed in the map database 20 included in the map data processing apparatus 1. Thus, the hardware cost can be suppressed by managing the complementary data in the map database 20.

FIG. 9 is a diagram illustrating an example of missing data. In FIG. 9, A city, B city, and C city are shown as similar regions 81 calculated by the similar region search unit 2. The data items in the figure are used for crime crime simulation, assuming a city near Tokyo, the number of people per household, population density, daytime population ratio, nearest station distance, city center time distance, road area ratio, commercial area ratio, district building coverage, area volume ratio, home ownership rate, a public corporation rate and explanatory variables, such as private rented index (x _n), crime rates is the dependent variable (the dependent variable) based on these (y) calculate.

  Here, the daytime population ratio is defined by the ratio of daytime population to the population (daytime population / population), and the nearest station distance is defined by the distance to the station starting from the center of the town (city), and the city center time. The distance is defined by the time required for the train from the Yamanote Line to the nearest station, the road area ratio is defined by the ratio of the road area to the town area (road area / town area), and the commercial and residential area ratio is relative to the residential land area. It is defined by the ratio of commercial land area (commercial land area / residential land area). The district building coverage ratio is defined by the ratio of the building area to the town area (building area / town area), and the district floor area ratio is the ratio of the building area x building floors to the town area ((building area x building floors) / machicho area) It is prescribed by.

A relational expression between the crime occurrence rate (y) and the explanatory variable (x _n ) is expressed by the following mathematical formula (1).

y = x ₁ β ₁ + x ₂ β ₂ + x ₃ β ₃ + x ₄ β ₄ + x ₅ β ₅ + x ₆ β ₆ +... + x _n β _n (1)
The above formula (1) is a regression formula of multiple regression analysis used in multivariate analysis, and uses the number of persons per household, population density, daytime population ratio, etc. shown in FIG. 9 as explanatory variables (x _n ). The coefficient (β _n ) of each explanatory variable is a partial regression coefficient.

  As shown in FIG. 9, the city A has all the explanatory variables necessary for calculating the crime occurrence rate, and has all the data. On the other hand, in B city, the road area ratio, ownership ratio, public corporation ratio, and private rental house ratio are marked with x, and these data are missing. In C city, the ratio of commercial and residential areas, area coverage ratio, and area volume ratio are marked with x, and these data are missing.

  In this case, in city B, in order to calculate the crime rate from the above formula (1), the supplementary data is used to supplement the missing road area rate, ownership rate, public corporation rate and private rent rate data. The construction unit 3 creates complementary data based on the data of A city. Also, for data missing in C city, complementary data is similarly created based on the data of A city.

  As described above, B city and C city have missing data, but the similar region search processing in the similar region search unit 2 does not calculate a similar region using all given explanatory variables. First, it decides from which viewpoint the similar area is calculated, selects an explanatory variable suitable as an index for judging similarity, and uses it to calculate the similar area, and is missing in B city and C city Even if there is data, if they are not used, the similar area search process can be executed. For example, if the number of people per household, population density, daytime population ratio, etc. are selected, there is no problem in judging similarity.

  Next, the overall processing of the map data processing apparatus 1 will be described using the flowchart shown in FIG. When the map data processing apparatus 1 starts operation, first, the similar area search unit 2 executes a similar area search and outputs a similar area (step S1).

  Next, the complementary data construction unit 3 selects a specific similar region from the similar regions output from the similar region search unit 2, and creates complementary data that complements the missing data of the data missing region (step S2).

  Next, in the data analysis unit 4, the region analysis selected by the user is executed using the complementary data created by the complementary data construction unit 3 (step S3).

  Next, the consistency determination unit 5 determines whether or not there is consistency as a result of the area analysis in the data missing area executed by the data analysis unit 4 (step S4). If the result of the regional analysis is significantly different from the surrounding region, or is far from the expected, the result of the analysis is inconsistent (No) and the construction of the supplementary data in step S2 is executed again. Use the area analysis in step S3 again. On the other hand, if the analysis results are consistent (Yes), the process proceeds to step S5.

  In step S5, in order to use the complementary data again, the complementary data generated in step S3 is output to the external storage device 206 such as an HDD, and the series of processing ends.

  FIG. 11 is a flowchart showing in detail the similar area search processing in the similar area search unit 2 in step S1 shown in FIG.

  When the similar area search process is started, first, a search target range including a data missing area and searching for a similar area for complementing the missing data is set (step S21).

  In the present embodiment, it is assumed that the user selects based on a pre-defined administrative area such as a prefecture or a city, and sets it as a search target range for a similar area search. Thereby, the analysis according to the administrative district can be performed.

  Moreover, the processing cost concerning a search can be restrained because a user designates the search object range of a similar area.

  Next, the user sets a data missing area that is included in the search target range set in step S21 and for which complementary data is to be created (step S22). The data missing area is assumed to be a previously defined area such as the administrative district.

  Next, in the data missing area set in step S22, the user sets missing data that is a target for creating complementary data (step S23). A plurality of missing data may be set here. This process may be executed before step S31 in FIG.

  Next, the user sets area data to be used for searching for a similar area having similar characteristics to the data missing area that is the complement target set in step S22, from the target area for searching for the similar area set in step S21. (Step S24). The regional data used here refers to, for example, geographical features, land use, rivers, weather, area, etc. that indicate the geographical characteristics of the region, as well as the population, number of houses, past disaster damage, and number of crimes that show social characteristics. Data.

  These are statistical information generally maintained and highly versatile. By using these to search for similar regions, it is possible to calculate similar regions based on the characteristics of each region.

  In addition, you may calculate a similar area using the data used by the analysis performed by the data analysis part 4 instead of the general statistical information mentioned above. In this case, since the data to be used for analysis is determined in advance, it is possible to save the setting work as in the case of using general statistical information.

  Next, based on the regional data set for calculating the similar region set in step S24, the similar region search unit 2 uses the multivariate analysis such as multiple regression analysis, quantification analysis, and cluster analysis. A similar area search is executed using a statistical method characterized by analysis (step S25). The similar area search is executed based on a quantitative value (similarity) indicating similarity.

  Next, based on the similarity calculated in step S25, the similar region search unit 2 arranges and outputs similar regions in descending order of similarity (step S26). In this case, the number of similar regions to be output can be designated. A similarity value is also output.

  FIG. 12 is a flowchart showing in detail the complementary data construction process in the complementary data construction unit 3 in step S2 shown in FIG.

  When the supplemental data construction process is started, first, the user sets a similar region that is a supplement source for constructing supplementary data for the data missing region from the similar regions searched by the similar region search unit 2 (step S31). . For this reason, the search result in the similar area search unit 2 is presented to the user via the display device 202 (FIG. 2). The user selects a similar area with the input device 201 (FIG. 2).

  This similar area is premised on having data corresponding to missing data in the missing data area. If the corresponding data is missing even in the similar area, another similar area having the corresponding data is set.

  In addition, since the user can also see the similarity value, the user can intentionally select a similar area as a complement source. When the similarity value is low, it is possible to make a determination such as re-searching for a similar area using another area data.

  Since similar regions are output from the similar region search unit 2 in descending order of similarity, the similar region output first by the supplementary data construction unit 3 may be automatically selected as a similar region. In this case, if the similar area that is output first does not have data corresponding to the missing data in the data missing area, the similar area that is output second may be selected.

  Next, based on the map data of the similar area set in step S31, the complementary data construction unit 3 generates complementary data for the data missing area (step S32). At this time, a coefficient in consideration of the regional difference between the similar area and the data missing area is calculated, and the coefficient is multiplied by the data to be complemented in the similar area, thereby generating complementary data for the data missing area. This coefficient can be calculated by using regional data correlated with the missing data, such as a population ratio and an area ratio between the missing data region and a similar region.

  Data can be complemented quantitatively by generating complementary data using coefficients calculated using regional data correlated with missing data.

  Next, the complementary data construction unit 3 performs processing for correcting the complementary data generated in step 32 (step S33). For example, if the year of creation of data of a similar area that is a complement source is old, it is necessary to correct the data to the current data. When it is necessary to correct the complementary data in this way, it is executed in this step. For example, in the case of population density, if the increase factor (or decrease factor) of the population in the corresponding region is known, the supplemental data can be corrected by calculating the population density in the similar region that is the source of the supplement ( Or, the current data is corrected based on the reduction coefficient.

  Next, the complementary data corrected in step 33 is temporarily stored (cached) in the memory 205 (FIG. 2) provided in the map data processing apparatus 1 (step S34). The cached complementary data is used for data analysis in the data analysis unit 4.

  In this way, the supplementary data construction unit 3 corrects the supplementary data to obtain detailed map data necessary for advanced regional analysis, which is used in city planning and tourism planning. Simple simulation. Further, since the supplementary data is given a flag that clearly indicates that it is complementary data, the current state of the region can be grasped by looking at the supplementary data itself. In addition, it can be determined from the analysis results whether missing data should be maintained. In addition, by knowing similar areas, you can refer to the measures adopted there.

<Embodiment 2>
In the first embodiment described above, the setting of the search target range for performing similar region search and the data missing region has been described as being set by the user based on pre-defined administrative districts such as prefectures and municipalities. However, the user may specify and set an arbitrary range. For example, the user may set an arbitrary range using the input device 201 (FIG. 2) such as a mouse.

  FIG. 13 shows an example in which one rectangular mesh 101 of meshes superimposed on a map is designated as an arbitrary range. The hatched mesh 101 in FIG. 13 is the designated data missing region, and the region on which the mesh is superimposed is the designated search target range for performing the similar region search.

  In order to complement the data of the mesh 101, using the region data of the search target range in which the meshes of other locations are superimposed, according to the processing flow of the first embodiment described with reference to FIGS. Perform similar area search and construct complementary data. For this purpose, it is necessary to aggregate regional data for each mesh. In order to aggregate the regional data for each mesh, the regional data possessed by a predefined administrative district or the like covered with the mesh is used.

  When a plurality of administrative districts straddle the same mesh as in the mesh 101 of FIG. 13, the area data of the administrative district straddling the same is corrected according to the proportion of the mesh. For example, city A, city B and city C span the mesh, occupying 60%, 30% and 10% respectively, and the population of city A, city B and city C is 50,000, When the number is 30,000 and 20,000, the population of the mesh 101 is 410,000, which is a total value of 50,000 × 60%, 30,000 × 30%, and 20,000 × 10%. In this way, the map data of each mesh is totaled to obtain the attribute value possessed by the mesh. The map data in mesh units is managed using the map database 20 shown in the first embodiment.

  In this way, by setting the range where the similar region search and the data missing region are set as arbitrary ranges, a more detailed regional analysis becomes possible. For example, it is possible to grasp the state of the region in more detail by analyzing the population-concentrated districts etc. by separating them with a mesh.

<Embodiment 3>
In Embodiment 1 demonstrated above, the supplementary data of the data missing area which the data output part 6 of the map data processing apparatus 1 outputs are supplementary data in the map database 20 which the map data processing apparatus 1 comprises. However, as shown in FIG. 14, a map database 23 composed only of complementary data is provided separately from the map database 20 (first database). A (second database) may be provided and managed there.

  In this case, the map database 23 that manages the complementary data has a table structure 71 that stores the complementary data, and the ID of the table and the ID of the table structure 61 that stores the attribute information of the map database 20 are respectively provided. With the structure indicating the same feature, the map database 20 and the map database 23 storing the complementary data can be linked using the ID as a key.

  By separately providing the map database 23 for managing the supplementary data, it is not necessary to modify the existing map database 20, so that maintainability is improved.

  Although the present invention has been described in detail, the above description is illustrative in all aspects, and the present invention is not limited thereto. It is understood that countless variations that are not illustrated can be envisaged without departing from the scope of the present invention.

  It should be noted that the present invention can be freely combined with each other within the scope of the invention, and each embodiment can be appropriately modified or omitted.

Claims (10)

In the map data including the attribute data of the feature, the map data processing device for complementing the map data of the data missing region where the attribute data is missing,
A similar region search unit that searches a plurality of regions using each of the plurality of search patterns and acquires a similar region similar to the data missing region for each of the plurality of search patterns ;
Based on the map data of the similar region obtained for each of the plurality of search patterns, and complement data constructing unit for generating respective complemented map data complement the missing data in the map data of the data loss area,
A data analysis unit that performs analysis based on the map data including each of the supplemented map data;
A consistency determination unit that determines whether or not the supplemented map data is appropriate by comparing the supplemented map data generated by the supplemental data construction unit ;
A map data processing apparatus comprising: a data output unit that outputs the supplemented map data when the determination result by the consistency determination unit is appropriate. The similar area search unit
The area data indicating the geographical characteristics and social characteristics of areas other than the missing data included in the map data of the data missing areas is processed by a statistical analysis method to obtain the similar areas. The map data processing apparatus according to 1. The similar area search unit
The map data processing apparatus according to claim 1, wherein the acquired similar area is output together with a similarity indicating a degree of similarity with the data missing area. The complementary data construction unit
A coefficient based on a regional difference between the similar area and the data missing area is calculated based on data having a high correlation with the missing data among the map data of the similar area, and the coefficient is calculated from the map data of the similar area. The map data processing apparatus according to claim 1, wherein the missing data is complemented by multiplying the map data as a complement source. Before Symbol data output section,
The map data processing apparatus according to claim 1, wherein each of the complemented map data generated by the complement data construction unit is output with a tag representing a search pattern used for the search among the plurality of search patterns. The similar area search unit
Wherein among the map data used in the analysis by the data analysis unit, and processed by statistical analysis techniques to map data other than the missing data to obtain the similar regions, the map data processing apparatus according to claim 1, wherein . The similar area search unit
The map data processing apparatus according to claim 1, wherein the similar area is searched in a search target range designated by a user. The search target range is:
8. The map data processing apparatus according to claim 7, wherein the map data processing apparatus is designated according to a pre-defined administrative district. The search target range is:
The map data processing apparatus according to claim 7, wherein the map data processing apparatus is designated according to a mesh set by a user. The supplemented map data is
When the second database different from the first database for storing the map data is managed, and the identifier of the table structure in the first database is the same as the identifier of the table structure in the second database The map data processing apparatus according to claim 1, wherein the map data has a structure indicating that they are the same feature .

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