CN114385627A - Data analysis method and device based on GIS map and storage medium - Google Patents
Data analysis method and device based on GIS map and storage medium Download PDFInfo
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Abstract
The invention discloses a data analysis method, a data analysis device and a storage medium based on a GIS map. The method comprises the following steps: configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table; configuring multidimensional analysis data, generating a multidimensional analysis data index table based on a thematic index information configuration table, and preparing map thematic area index data; establishing mass points for all position-related objects of a data center by a data preparation method based on mass point categories preset in advance to generate a mass point data table; and dynamically constructing a display area based on the starting of a user, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a data analysis method and device based on a GIS map and a storage medium.
Background
Traditional data analysis often relies on data analysis tools, each type of tool having its own advantages and disadvantages. The application of a Geographic Information System (GIS) map in the fields of navigation and the like is mature, but in the analysis field, the GIS map can visually display areas or mass points, but interactive analysis is difficult; one-house type display effect is good, but only the arrangement space sense of data is insufficient; the multi-dimensional analysis interaction analysis function is strong, but the operation of operators is complicated.
With the increasing of data resources accumulated in the data center, the traditional data analysis often limits the analysis path and exploration imagination of users, and has the defect of being useless.
In the existing data analysis based on the GIS map, the data analysis effect based on the geographic position as the gravity center is more emphasized, and even if a chart such as a thermodynamic diagram, a pie chart, a column chart and the like are combined, the geographic position is used as the key point, so that the intuitive condition of the chart is reflected. For analysis tools for mainly analyzing the essence of data and tracing the reason of the data, such as multi-dimensional analysis, one-family analysis and the like, the operation requirements of operators are high or the displayed data is not intuitive.
In the aspect of area positioning of a GIS map, the positions of the positioned areas are drilled step by step based on well-defined map levels, but provincial areas in China are different, for example, when Beijing drills to the next level to specifically position the urban areas, but drills to the first level in wide areas such as Xinjiang, Tibet and the like, the areas corresponding to the next level of areas cannot be visually seen, and multiple levels are drilled to see the specific urban areas.
Disclosure of Invention
The invention aims to provide a data analysis method, a data analysis device and a storage medium based on a GIS map. The method integrates three types of tools, namely a GIS map, multidimensional analysis and a household type tool, and can support the household type analysis from macro area index analysis penetration to surrounding mass points. The method integrates the related advantages of the GIS map, expands a set of algorithm on the related advantages, and can better serve operators by combining the advantage that the algorithm can highlight the hierarchical region positioning of the data map.
In view of the above, a first aspect of the present invention provides a data analysis method based on a GIS map, which is characterized in that the method includes: step 110: configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table; step 120: configuring multidimensional analysis data, generating a multidimensional analysis data index table based on the thematic index information configuration table, and preparing map thematic area index data; step 130: establishing mass points for all position-related objects of a data center by a data preparation method based on mass point categories preset in advance to generate a mass point data table; step 140: based on the starting of a user, dynamically constructing a display area, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area, the left suspension condition area is related to the multi-dimensional analysis data index table, and the right suspension analysis icon area is related to the mass point data table.
Optionally, with reference to the first aspect, in a possible implementation manner, the method further includes: step 150: when a user first opening instruction is received, the displayed focus area is an area preset by system parameters, and the display level is an optimal display level for positioning the preset area by combining a recommended focus algorithm; when receiving at least one operation of dragging the map, zooming the level or clicking the map drawing surface by the user, recalculating the optimal display area through the recommended focus area algorithm, and repositioning to the optimal display level.
Optionally, with reference to the first aspect, in a possible implementation manner, in the step 110, the data source may be a data table or a view.
Optionally, with reference to the first aspect, in a possible implementation manner, in the step 120, the multidimensional analysis data index table is used for fast multidimensional analysis, and the multidimensional analysis data index table is a three-dimensional cube, where the dimensions of the three-dimensional cube include: topic, time, area; in the step 140, the user can select the special topic, time and area condition from the left hover condition area, and the user can dynamically construct a map area display and multidimensional analysis from the right hover analysis icon area selection condition.
Optionally, with reference to the first aspect, in a possible implementation manner, the recommended focus area algorithm in step 150 specifically includes: step 151: acquiring page basic parameters, wherein the page basic parameters comprise: the maximum value and the minimum value of the longitude are respectively C _ GL and C _ TL, the maximum value and the minimum value of the latitude are respectively C _ GS and C _ TS, the central point of a screen is obtained as C, and an original map level F before the interactive page changes and a current map level R after the interactive page changes are obtained; step 152: acquiring a partition coding list of a to-be-selected partition, wherein the partition coding list of the to-be-selected partition is related to a preset partition level list, and determining whether a partition corresponding to the partition coding list of the to-be-selected partition is a recommended partition according to the page basic parameters; step 153: and calculating the score of the recommended administrative division of the area to be selected according to the area to be selected division coding list.
Optionally, with reference to the first aspect, in a possible implementation manner, the step 152 specifically includes: when the to-be-selected area partition coding list simultaneously meets two conditions, determining that a partition corresponding to the to-be-selected area partition coding list is a recommended partition, and adding the recommended partition into the to-be-selected area, wherein the two conditions comprise: condition 1: if the current map level R is larger than or equal to the original map level F, the partition level numeric area is the area of plus or minus 1 of the current map level; or if the current map level R is smaller than the original map level F, the partition level sub-value range is the range from the current map level to the current level minus 2; condition 2: the maximum value and the minimum value of the division longitude of the preset division level list are aL and aS respectively, the maximum value and the minimum value of the division longitude latitude of the preset division level list are bL and bS respectively, the maximum value and the minimum value of the division longitude of the preset division level list are displayed on a screen, and the maximum value and the minimum value of the latitude are displayed on the display screen.
Optionally, with reference to the first aspect, in a possible implementation manner, the step 153 specifically includes: taking the difference between the maximum value C _ GL and the minimum value C _ TL of the longitude as the length, and taking the maximum value C _ GS and the minimum value C _ TS of the latitude as the width to obtain the area of the screen display area; and calculating the score of the recommended administrative division of the area to be selected according to the area of the screen display area.
Optionally, in combination with the first aspect, in one possible implementation manner,
the calculating the score of the recommended administrative division of the to-be-selected area according to the area of the screen display area specifically comprises the following steps: judging whether the coordinate longitude of the central point of the screen is within the range of the maximum longitude and the minimum longitude of the region or not, and the coordinate latitude of the central point of the screen is within the range of the maximum latitude and the minimum latitude of the region, if so, recording the value a as a set score; the area of the to-be-selected region in the display region is divided by the area of the display region multiplied by 10, the result is b, whether the value b is larger than a set threshold value or not is judged, and if the value b is larger than the set threshold value, the result obtained by subtracting the set gradient score from the value b is assigned to b; obtaining the distances from the center points of all compartments to be selected to the center point of the screen, calculating ranking scores, sorting the compartments according to the sequence from small to large according to the distance sorting, assigning the value of the compartment with the smallest distance from the center point downwards, and gradually reducing the value, wherein the obtained value is marked as c; the resulting weight score was designated as a + b + c.
A second aspect of the present invention provides a GIS map-based data analysis apparatus, comprising: a memory having code stored therein, and a processor configured to execute the code, wherein when the code is executed, the terminal performs the GIS map-based data analysis method as described in any one of the possible implementations of the first aspect to the first aspect.
A third aspect of the present application provides a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform any one of the possible GIS map-based data analysis methods as in the first aspect of the present application.
The invention discloses a data analysis method, a data analysis device and a storage medium based on a GIS map. The method comprises the following steps: step 110: configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table; step 120: configuring multidimensional analysis data, generating a multidimensional analysis data index table based on the thematic index information configuration table, and preparing map thematic area index data; step 130: establishing mass points for all position-related objects of a data center by a data preparation method based on mass point categories preset in advance to generate a mass point data table; step 140: based on the starting of a user, dynamically constructing a display area, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area, the left suspension condition area is related to the multi-dimensional analysis data index table, and the right suspension analysis icon area is related to the mass point data table.
A GIS map-based integrated experience data analysis method is provided, wherein a GIS map, multidimensional analysis and a household type tool are combined together, the macroscopic region index analysis penetrates to the microscopic mass point household type analysis, the interactive operation of the multidimensional analysis, the dragging and zooming of the GIS map and the detailed and intuitive characteristic of the household type are taken, and a user can finish the exploration and analysis of a data center by self by clicking the dragging operation. The map is combined with multi-dimensional analysis to expand the spatial thinking for users. The map is combined with the one-household method, the method of viewing the one-household method from an abstract concrete object is changed into the method of taking the mass points of the map as guidance and exploration, and the concrete one-household information is traced back. The method mainly aims to meet the penetrating monitoring of 'one-pole plug-in-the-end' in business, and from macroscopic special subject indexes, step-by-step penetration of province, city and county areas to detailed mass points is realized.
The area size and the boundary difference of the traditional map are too large, and aiming at the weak point that the traditional GIS map area analysis is difficult to penetrate layer by layer, a focus area is intelligently obtained by integrating methods such as itemizing, weight ratio, ranking score and the like through an algorithm on the positioning of the area of the map. The method solves the problem that the traditional map can only be used for finding regions step by step according to the well-defined levels in the interactive operation of the map by the user.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flowchart of a GIS map-based data analysis method according to the present invention;
FIG. 2 is a schematic diagram of a dynamic build area layout provided by the present invention;
FIG. 3 is a flowchart of a recommended focus area algorithm provided by the present invention;
fig. 4 is a schematic diagram illustrating a calculation of a zoning display range according to the present invention;
FIG. 5 is a diagram illustrating the effect of mass points provided by the present invention;
fig. 6 is a schematic illustration showing an administrative division according to the present invention;
fig. 7 is a schematic illustration showing an administrative division according to the present invention;
fig. 8 is a schematic structural diagram of a GIS map-based data analysis device according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b, a and c, b and c or a and b and c, wherein a, b and c can be single or multiple. It is to be noted that "at least one item" may also be interpreted as "one or more item(s)".
It is noted that, in the present application, words such as "exemplary" or "for example" are used to mean exemplary, illustrative, or descriptive. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application.
First, a simple introduction is made to technical terms related to the present application:
and GIS: the Geographic Information System (GIS), which is a specific spatial Information System of great importance. The system is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in the whole or partial earth surface (including the atmosphere) space under the support of a computer hardware and software system.
Mass points: the geographic position is a mark point on a map and is also called as a coordinate point, and the mark point is used for specifically positioning the position of a specific spatial level of a certain unit of a certain enterprise.
The type of the mass points: the method is a classification method mode which is customized for the mass points in order to distinguish the mass points with different properties and different properties. Such as: mass points for poverty alleviation.
The special indexes are as follows: the statistical indexes are made for a certain economic or social problem and are used for describing the overall basic condition and the comprehensive quantity of distribution characteristics of each variable. For example, the financial investment of lean funds may be based on the number of regional lean funds used as a topical indicator.
Multidimensional analysis: the invention is one of the advanced statistical analysis methods, which is to put a product or a market phenomenon on more than two-dimensional space coordinates for analysis, and the invention comprises three dimensions: topic index dimension, partition dimension, and time dimension.
A house formula: the data presentation report component can merge and display related elements of enterprises, units, departments and other similar organizations, and can perform classified statistics according to different attribute properties.
The invention aims to establish a data analysis method for providing an integrated experience based on a GIS map, which comprises the steps of firstly defining a thematic index analysis system of the map, multidimensional analysis and one-house sharing, configuring the thematic indexes of the GIS map by combining the index system, and preparing mass point data; secondly, when the user roams the map, an algorithm constructs thematic analysis of the current area according to the area where the user is located, the coordinates of the central point of the screen and other parameters; and finally, switching to dynamically constructing mass point analysis after the interactive parameter combination meets the preset condition.
The invention comprises the following contents: the GIS map-based analysis method is characterized in that three analysis tools, namely a GIS map, multidimensional analysis and one-family analysis, are combined through a series of configuration methods, and the GIS map-based analysis method is provided for users. Secondly, in the analysis method, the optimal display level is intelligently recommended by utilizing an automatic focus area of the algorithm, so that the operation experience of the user is improved.
Specifically, referring to fig. 1, the present invention provides a data analysis method based on a GIS map, including:
s110, configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table.
And configuring thematic indexes. Defining core analysis indexes, distributing index codes and display names, constructing the relation between thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic, wherein the data sources can be data tables or views and are stored in a thematic index information configuration table in a table 1.
| Name of field | Field description |
| ZBGUID | Index coding |
| ZBNAME | Index name |
| IS_BGT | Whether or not to analyze the index in multiple dimensions |
| TABLE_NAME | Data table or view name corresponding to area data |
| SHOW_TEXT | Data query presentation field |
TABLE 1 configuration table of special subject index information
And S120, configuring multidimensional analysis data, generating a multidimensional analysis data index table based on the thematic index information configuration table, and preparing map thematic regional index data.
The method comprises the following steps of preparing map thematic region index data:
step 2.1, configuring multidimensional analysis data, namely generating a multidimensional analysis data index table based on a thematic index information configuration table for rapid multidimensional analysis, wherein the index data table is a three-dimensional cube, and the dimensionality is as follows: topic, time, area, measure: and index values are stored in a table 2-dimensional analysis data index table according to the preparation corresponding data related to the indexes through multi-dimensional analysis, wherein ZGBUID fields in the table and ZGBUID fields in the table 1 are related fields.
| Name of field | Field description |
| DQ | Region partition coding |
| RQ | Date and time |
| ZBGUID | Index coding |
| ZBVALUE | Index value |
TABLE 2 index Table of multidimensional analysis data
Step 2.2, preparing a data table or view corresponding to the thematic data source, wherein the structure of the table or view corresponding to the data must contain the requirements of the table 3, and supplementing the corresponding data table name and the data query display field into the table 1 after preparation.
Table 3 data source structure template corresponding to special subject
By appointing the structure of the data source, the rapid increase or adjustment of the special topic is facilitated. After configuration is completed, the topic identifiers in table 3 can be maintained in the corresponding fields of table 1.
S130, establishing mass points for all objects related to the position of the data center through a data preparation method based on the mass point types preset in advance, and generating a mass point data table.
And preparing index mass point data. Establishing mass points for all objects (such as poverty-relieving points, budget units and the like) related to positions of the data center by a data preparation method based on the mass point classes preset in advance, and generating a mass point data table, wherein the mass point data table structure is shown in table 4.
| Name of field | Field description |
| AD_CODE | Partition coding |
| ZBGUID | Index coding |
| MC | Mass point names |
| LAT | Latitude |
| LNG | Longitude (G) |
| MSXX | Description information, mass point presentation information |
| URL | One-family analysis link |
Table 4 mass point data table structure
And S140, dynamically constructing a display area based on the starting of a user, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area, the left suspension condition area is related to the multi-dimensional analysis data index table, and the right suspension analysis icon area is related to the mass point data table.
And dynamically constructing a display area. The configuration is completed in the three steps, and after the user opens the map, the area layout is displayed, see fig. 2. The bottom layer is a GIS map, and the left suspension condition area can select conditions such as special indexes, areas and time; the right suspension analyzes the chart area. And dynamically constructing map area display and multidimensional analysis according to the selection condition. Wherein the area selection can be obtained by interactively operating on the map according to an algorithm. Specifically, please refer to fig. 2 for understanding the layout of the dynamic building region, which is not described herein again.
The focus area is recommended. After the user drags the map, zooms the hierarchy, clicks through and the like, the focal region area of the user under the region hierarchy is automatically identified through a special algorithm.
The algorithm comprises the following steps:
1. and acquiring page basic parameters. And acquiring the maximum and minimum longitudes C _ GL and C _ TL and the maximum and minimum latitudes C _ GS and C _ TS according to the coordinates of the four extreme values of the current screen, acquiring the center point of the screen as C, and acquiring the original map level F before the change of the interactive page and the current map level R after the change of the interactive page.
2. And acquiring a partition coding list of the to-be-selected area. This step relies on a zone level table, see table 5, which is pre-prepared by the system.
| Name of field | Field description |
| QH_CODE | Standard region coding |
| QH_NAME | Standard zone names |
| MAP_LVL | Default map level |
| aL | Maximum value of regional latitude |
| aS | Minimum value of regional latitude |
| bL | Maximum value of regional longitude |
| bS | Minimum value of regional longitude |
| QH_C | Coordinates of central point of area |
TABLE 5 prefabricated zone level table
When the corresponding section in the table simultaneously satisfies the following two conditions:
condition 1:
and if the current map level R is larger than or equal to the in-situ map level F, the division level numeric area is the area of plus or minus 1 of the current stratum level. Or,
and if the current map level R is less than the original map level F, the division level dereferencing range is the range from the current map level to the current level minus 2.
The formula is as follows:
IF(R≥F){(MAP_LVL IN(R-1～R+1)}ELSE IF(R<F){(MAP_LVL IN(R-2～R)}。
it should be noted that the condition 1 is a result based on the operation behavior of the user. When R is larger than F, the zoom-out operation is performed, and when R is smaller than F, the zoom-in operation is performed. The two value ranges are defined according to the specific operation behavior of the user. The selection range can be optimized based on user behavior.
Condition 2:
the system pre-prepares the zone level list with the minimum longitude maximum values aL and aS and the minimum latitude maximum values bL and bS. Satisfying the longitude maximum or the longitude minimum in the display screen, and the latitude maximum and the latitude minimum in the display screen, the formula is as follows:
(C_GL>aL>C_GS||C_GL>aS>C_GS)&&(C_TL>bL>C_TS||C_TL>bS>C_TS)。
the zone is a recommended zone and added into the candidate zone.
3. The algorithm calculates the region. And calculating the score of the recommended administrative division of the area to be selected.
Referring to fig. 3, the specific calculation process is as follows:
firstly, obtaining the area of a screen display area, and taking the difference between the maximum and minimum longitudes of extreme value coordinates of the display area as the length and the difference between the maximum and minimum latitudes as the width to obtain the area C _ MJ of the display area, wherein the formula is as follows: and C _ MJ ═ C _ GL-C _ GS (C _ TL-C _ TS).
Score calculation index:
3.1) scoring method: whether the screen center point is in the current and calculated candidate region. Namely, the to-be-selected partition needs to satisfy: and if the longitude of the coordinate of the center point of the screen is within the maximum and minimum longitude of the region and the latitude of the coordinate of the center point of the screen is within the maximum and minimum latitude of the region, recording the value of a as a set score, and otherwise, recording the value of a as 0 score. For example, the set score may be 3 points. The set score can be valued according to actual requirements, and the value of 3 is only taken as an example of the application, which cannot be taken as a limitation of the application.
The formula is as follows: IF (bL > C > bS & & aL > C > aS) {3} ELSE {0}
3.2) weight method: and (4) dividing the area of the to-be-selected region in the display region by the area of the display region multiplied by 10, and subtracting the result by a fixed gradient score if the result is greater than a set threshold value.
For example, the set threshold may be 7 and the fixed gradient score may be 4. The set threshold and the fixed gradient value can be valued according to actual requirements, the set threshold is only 7, and the fixed gradient value is 4, which is taken as an example and cannot be taken as a limitation of the application.
The area of the to-be-selected region in the display area is equal to length x width.
The length value taking method comprises the following steps:
1.1), calculating the result of subtracting (if the regional longitude maximum is larger than C _ GL, using C _ GL, or else using bL) (if the regional longitude minimum is smaller than C _ GS, using C _ GS, or else using bS);
1.2) if the calculation result is a non-negative number, taking the result as a long value;
1.3), if the result is negative, the value of the length is 0.
The width value taking method comprises the following steps:
2.1), calculating a result of subtracting (if the maximum value of the region latitude is larger than that of C _ TL, using C _ TL, or else using aL), if the minimum value of the region latitude is smaller than that of C _ TS, using C _ TS, or else using aS);
2.2) if the calculation result is a non-negative number, taking the result as a wide value;
2.3), if the result of this calculation is negative, the value of the width is 0.
After the area of the to-be-selected partition in the display area is calculated, the value of b is calculated:
3.1), b is the area of the to-be-selected area in the display area divided by the area of the display area multiplied by 10;
3.2) judging whether the value of b is larger than 7, and if the value of b is larger than 7, assigning the result obtained by subtracting 4 from the value of b to b.
The specific calculation formula of the calculation step is as follows:
long ═ IF (bL > C _ GL) { C _ GL } ELSE { bL } -IF (bS < C _ GS) { C _ GS } ELSE { bS })
Wide ═ IF (aL > C _ TL) { C _ TL } ELSE { aL } -IF (aS < C _ TS) { C _ TS } ELSE { aS })
b ═ IF (length <0) {0} ELSE { length }. IF (width <0) {0} ELSE { width }/C _ MJ 10
b＝IF(b>7){b-4}
3.3) ranking method: and obtaining the distances from the center points of all the zones to be selected to the center point of the screen and calculating ranking scores. And (4) sorting according to the sequence from small to large according to the distance sorting, assigning the value of the division with the smallest distance from the center point downwards and decreasing, and recording the obtained value as c.
For example, if the value of the partition with the smallest distance from the center point is (5), the values are sequentially assigned downwards, and the result is c by decrementing by 5 ÷ the number of partitions to be selected and multiplying by 2. The value of the section having the smallest distance from the center point is only an example and is not limited to 5. The concrete formula is as follows:
setting the number of the to-be-selected regions as list.length;
internally sort list, by
The values are sorted from small to large, list [ 0]]1, 5, list]Get c ═ 5- (5/list. length. 1), list [2]Get c ═ 5- (5/list. length. times.2), list [ n [ (. sup.) ]]And obtaining c-5- (5/list. length. n). Wherein QH _ C longitude is regionThe longitude of the central point, and the QH _ C latitude is the latitude of the central point of the area; the longitude C is the longitude of the center point of the screen, and the latitude C is the latitude of the center point of the screen.
3.4) weight score equal to a + b + c.
4. And returning the data. And (4) sorting the administrative division with the highest ranking score into a recommended administrative division, and returning the administrative division code of the area to the foreground.
The calculation process is shown in fig. 3, the screen area and the area to be selected are shown in fig. 4 as an example, and in the simplest example, the algorithm calculation process is as follows: firstly, presetting longitude and latitude coordinates of central points and optimal map levels of each region for about 3000 city and county areas in the country; and secondly, obtaining the map levels of all the areas, wherein the levels are in the map display level range of the screen, the longitude and latitude of the central point of each area are in the areas of the four vertex coordinate ranges of the screen, performing partition filtering in the areas and positioning a central partition according to the algorithm weight calculation rule, and combining the obtained partitions with the corresponding proper display levels set in the table and returning the partitions.
S150, when a first opening instruction of a user is received, the displayed focus area is an area preset by system parameters, and the display level is the optimal display level for positioning the preset area by combining a recommended focus algorithm; when receiving at least one operation of dragging the map, zooming the level or clicking the map drawing surface by the user, recalculating the optimal display area through the recommended focus area algorithm, and repositioning to the optimal display level.
And (6) intelligent switching. The device is started for the first time, the focus area is an area set by system parameters, and the area and the optimal display level are positioned by combining an algorithm; under the condition of initialization, the device takes regional thematic display as an entrance, displays the regional thematic and multidimensional analysis data in the step 4 in the graph in the figure 2, the content of the map drawing surface in the figure is used as a data basis based on the relevant configuration in the step 1, the content of the right multidimensional analysis part is used as a display basis based on the relevant configuration in the step 2.1, and the linkage of the two can carry out switching between different index data through an index list of a left side map.
Secondly, when the user operates the device, under the conditions of dragging the map, zooming the hierarchy, clicking the drawing surface of the map and the like, the device recalculates and positions the optimal display area under the hierarchy according to the algorithm content in the step 4, and relocates the optimal display hierarchy of the optimal division, for example, when the user operates a new area, the device calls the algorithm to obtain the current area and the optimal hierarchy, draws the area information, and adjusts the area to the optimal display hierarchy.
Thirdly, when the user calls the algorithm after operating to obtain the division as the last division and the current level is more than the optimal display level by 2 levels, for example, under the condition that a specific village and town county is enlarged, the device automatically switches the display mode according to the current area, the current index and the data in the step 3, so that the user can see the data of the sea volume point under the area and the index, and a user type link (URL) of the point is displayed when the user selects a specific mass point, as shown in fig. 5.
Finally, when the user operates again, when the calling algorithm acquires that the division is not the last-stage division or is smaller than the last-stage division display level plus 2, the device can be automatically switched to the corresponding thematic display module and can be dynamically switched between the two modules.
The invention provides a data analysis method, a data analysis device and a storage medium based on a GIS map. The method comprises the following steps: step 110: configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table; step 120: configuring multidimensional analysis data, generating a multidimensional analysis data index table based on the thematic index information configuration table, and preparing map thematic area index data; step 130: establishing mass points for all position-related objects of a data center by a data preparation method based on mass point categories preset in advance to generate a mass point data table; step 140: based on the starting of a user, dynamically constructing a display area, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area, the left suspension condition area is related to the multi-dimensional analysis data index table, and the right suspension analysis icon area is related to the mass point data table.
Further, the present application provides a specific example of lean-induction analysis in the financial field. The method may specifically include:
step 1, configuring thematic indexes and map codes, allocating unique identifiers, index codes and display names, and establishing the indexes as indexes of a multidimensional analysis scheme, such as a table 6.
TABLE 6
And 2, preparing map thematic area index data.
Step 2.1 configure the multidimensional analysis data as in table 7.
| Region partition coding | Date and time | Index coding | Index value |
| 4201 | 2020 | 101 | 1000 |
| 4202 | 2020 | 101 | 2000 |
| 4203 | 2020 | 101 | 3000 |
| …… | …… | …… | …… |
TABLE 7
Step 2.2 prepares the data table corresponding to the topic data source, prepares the corresponding data table Bda _ t _ map _ ztt _ fp and satisfies the field requirements of the data table as shown in table 8.
| Partition coding | Date and time | Zone level | Field 1 | |
| 42 | 2020 | 2 | 1000 | 1000 |
| 4202 | 2020 | 3 | 2000 | 2000 |
| 420201 | 2020 | 4 | 3000 | 3000 |
| …… | …… | …… | …… | …… |
TABLE 8
Step 3 prepares the index mass point data as shown in table 9.
| Partition coding | Index coding | Mass point names | Longitude (G) | Latitude | Description information, mass point presentation information |
| 4201 | 101 | Enterprise 1 | 136.00000 | 27.12300 | And (3) enterprise name: 123; amount of poverty relief: 456 |
| 4201 | 101 | Unit two | 136.12300 | 27.00000 | Unit name: 456; amount of poverty relief: 123 |
| …… | …… | …… | …… | …… | …… |
TABLE 9
And 4, dynamically constructing a display area. And after the configuration steps of the steps 1-3 are completed, opening an operation interface of the device, and starting dynamic construction of area display by using the login authority of the user as the position of the initialization area.
When the user performs a roaming operation, the algorithm starts to perform the calculation of the next step.
1. And acquiring page basic parameters. Obtaining the maximum and minimum coordinates of latitude 45.5 and 42.5, the maximum and minimum longitude 132.333 and 129.667, the screen center point [44.000,131.000], the level 6 before change and the level 7 after change
2. And acquiring a partition coding list of the to-be-selected area. The data content of the query preremaking table meets the following Where condition
MAP_LVL IN(6,7,8)AND(45.5>aL>42.5 OR 45.5>aS>42.5)AND(132.333>bL>129.667 OR 132.333>bS>129.667)
It is assumed that the obtained list of all the to-be-selected partitions satisfying the condition contains 3 elements as follows:
3. the algorithm calculates the region. And calculating the score of the recommended administrative division of the area to be selected. Take the calculation process of BJ city with 7 levels after change as an example:
calculating and acquiring screen display area C _ MJ (45.5-42.5) 132.333-129.667 (7.998)
Score calculation index:
3.1) scoring method: whether the center point of the screen is in the region to be selected.
a＝IF(43.111>44.000>43.100&&130.111>131.000>130.100){3}ELSE{0}＝0
3.2) weight method: the area ratio of the to-be-selected region in the display region is as follows:
long ═ 0.611 ═ 0 (IF (43.111>45.5) {45.5} ELSE {43.111}) - (IF (41.000<42.5) {42.5} ELSE {43.100})
Width ═ width [ IF (130.111>132.333) {132.333} ELSE {130.111}) -
(IF(130.100<129.667){129.667}ELSE{130.100})】＝0.011
b＝IF(0.611<0){0}ELSE{0.611}*IF(0.011<0){0}ELSE{0.011}/7.998*10≈0.008
b＝IF(b>7){b–4}＝0.008
3.3) ranking method: and obtaining the distances from the center points of all the zones to be selected to the center point of the screen and calculating ranking scores.
Taking the first element in the list to obtain the distance between the center points of the elements as follows:
analogically calculate the distances between the center points of all 3 elements, and sort the list from small to large according to the value, taking fig. 6 as an example, to obtain the following ranking and weight score list.
| Ranking | Region of land | Ranking score |
| 1 | City of BJ | 5 |
| 2 | TJ City of | 5–(5/3*1)＝3.333 |
| 3 | HB province | 5–(5/3*2)＝1.666 |
4. And returning the data. And finally, counting the total score between 3.1 and 3.3, taking the administrative division with the highest score as a recommended administrative division, and returning the administrative division code and the optimal display level of the area to the foreground.
The examples of BJ city and HB province are shown in FIG. 6 and FIG. 7. In the case of fig. 6, the map level is 8, although the center point of BJ is closer to the center point of the screen, the algorithm combines multiple weights of the position, area and distance of the center point to perform score calculation, and finally uses HB province as the best display region, and returns with the default display level of HB province, if the user clicks the drill-down trigger algorithm, the device automatically positions the best display layer of the province, otherwise, the display region is switched.
The scene region scores are as follows:
| ranking | Region of land | Aggregate score | 3.1 step scoring | 3.2 step scoring | 3.3 step scoring |
| 1 | HB province | 9.83 | 3 | 3.5 | 3.33 |
| 2 | City of BJ | 9.3 | 3 | 1.3 | 5 |
| 3 | TJ City of | 2.36 | 0 | 0.7 | 1.66 |
In the case of FIG. 7, the map level is 9, and in contrast to FIG. 6, the best show division is BJ City, and the default show level is returned.
The scene region scores are as follows:
| ranking | Region of land | Aggregate score | 3.1 step scoring | 3.2 step scoring | 3.3 step scoring |
| 1 | HB province | 12.33 | 3 | 6 | 3.33 |
| 2 | City of BJ | 12.5 | 3 | 4.5 | 5 |
| 3 | TJ City of | 5.26 | 0 | 3.6 | 1.66 |
Step 5, the device intelligently displays and switches. Based on the algorithm configured in the combination device, a user can position the current region position and the optimal display region through the system parameter preset region and the algorithm during initialization, check the summarized data content of poverty-relieving special questions in the current region, and can also automatically switch the special question display and the mass point display through the left side of the map if the manual switching is not performed.
When the user drags the map, zooms the hierarchy, clicks the drawing surface of the map and the like, the device recalculates and positions the optimal display area under the hierarchy according to the algorithm content in the step 4, and relocates the optimal display hierarchy of the optimal division, for example, when the user operates a new area, the device calls the algorithm to obtain the current area and the optimal hierarchy, draws the area information, and adjusts the area to the optimal display hierarchy.
When the user calls an algorithm after operation to obtain the division as the last-stage division and the current level is more than 2 levels of the optimal display level, for example, under the condition that a specific village and town county is enlarged, the device automatically switches the display mode according to the current area, the current index and the data in the step 3, so that the user can see the data of the sea volume point under the area and the index, and a user type link (URL) of the point is displayed when the user selects a specific mass point.
The invention has the following effects: an analysis frame is established, three analysis tools of a GIS map, multidimensional analysis and one-family report are integrated, the step-by-step penetration analysis from macro to micro is realized, only data sources of special indexes are needed to be configured, code development is not needed, the working efficiency is improved, and the penetrating monitoring of one-pole plug-in-the-end in business is met.
And in the aspect of regional positioning of the map, a focus area is intelligently obtained by integrating methods such as a total item, a weight ratio, a ranking score and the like through an algorithm. The method solves the problem that the traditional map can only be used for finding regions step by step according to the well-defined levels in the interactive operation of the map by the user.
The GIS map-based data analysis method has the following advantages:
1. and (4) integration. Multidimensional analysis and chart analysis are integrated on the GIS map, and the map analysis mode and content are greatly improved, so that the analysis of 'inserting one pole to the bottom' becomes possible.
2. Configurability. The new special subject index can be realized only by configuration, and the requirement of service expansion is met.
3. High efficiency. When the map is operated to find the region, the focus area is automatically obtained through an algorithm, so that the operation experience of a user is improved.
Fig. 8 is a schematic structural diagram of a GIS map-based data analysis apparatus 200 according to an embodiment of the present disclosure, where the GIS map-based data analysis apparatus 200 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 210 (e.g., one or more processors) and a memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing applications 233 or data 232. Memory 220 and storage medium 230 may be, among other things, transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instruction operations in the GIS map-based data analysis device 200. Still further, processor 210 may be configured to communicate with storage medium 230 to execute a series of instruction operations in storage medium 230 on local government liability risk assessment apparatus 200.
The GIS map based data analysis device 200 may also include one or more power supplies 240, one or more wired or wireless network interfaces 230, one or more input-output interfaces 260, and/or one or more operating systems 231, such as Windows server, Mac OS X, Unix, Linux, FreeBSD, and so forth. Those skilled in the art will appreciate that the GIS map based data analysis apparatus illustrated in fig. 8 does not constitute a limitation of the data processing apparatus and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the GIS map-based data analysis method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (RON), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In the examples provided herein, it is to be understood that the disclosed methods may be practiced otherwise than as specifically described without departing from the spirit and scope of the present application. The present embodiment is an exemplary example only, and should not be taken as limiting, and the specific disclosure should not be taken as limiting the purpose of the application. For example, some features may be omitted, or not performed.
The technical means disclosed in the present application is not limited to the technical means disclosed in the above embodiments, and includes technical means formed by any combination of the above technical features. It should be noted that, for those skilled in the art, without departing from the principle of the present application, several improvements and modifications can be made, and these improvements and modifications are also considered to be within the scope of the present application.
The method, the device and the storage medium for analyzing data based on the GIS map provided by the embodiment of the present application are introduced in detail, a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiment is only used to help understanding the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A GIS map-based data analysis method, characterized in that the method comprises:
step 110: configuring thematic indexes, defining core analysis indexes, distributing index codes and display names, constructing the relation between the thematic indexes and a multi-dimensional analysis scheme, constructing data source identifications corresponding to data of map thematic, and setting data sources corresponding to the thematic to generate a thematic index information configuration table;
step 120: configuring multidimensional analysis data, generating a multidimensional analysis data index table based on the thematic index information configuration table, and preparing map thematic area index data;
step 130: establishing mass points for all position-related objects of a data center by a data preparation method based on mass point categories preset in advance to generate a mass point data table;
step 140: based on the starting of a user, dynamically constructing a display area, wherein the bottom layer of the display area is a Geographic Information System (GIS) map, a left suspension condition area and a right suspension analysis icon area, the left suspension condition area is related to the multi-dimensional analysis data index table, and the right suspension analysis icon area is related to the mass point data table.
2. The GIS map-based data analysis method of claim 1, further comprising:
step 150: when a user first opening instruction is received, the displayed focus area is an area preset by system parameters, and the display level is an optimal display level for positioning the preset area by combining a recommended focus algorithm; when receiving at least one operation of dragging the map, zooming the level or clicking the map drawing surface by the user, recalculating the optimal display area through the recommended focus area algorithm, and repositioning to the optimal display level.
3. The GIS map-based data analysis method of claim 1, wherein, in the step 110,
the data source may be a data table or view.
4. The GIS map based data analysis method of claim 1, wherein in step 120,
the multi-dimensional analysis data index table is used for rapid multi-dimensional analysis, and the multi-dimensional analysis data index table is a three-dimensional cube, wherein the dimensions of the three-dimensional cube comprise: topic, time, area;
in the step 140, the process is carried out,
the user can select special subjects, time and area conditions from the left side floating condition area, and the user can dynamically construct map area display and multidimensional analysis from the right side floating analysis icon area selection conditions.
5. The GIS map-based data analysis method of claim 2, wherein the recommended focus area algorithm in step 150 specifically comprises:
step 151: acquiring page basic parameters, wherein the page basic parameters comprise: the maximum value and the minimum value of the longitude are respectively C _ GL and C _ TL, the maximum value and the minimum value of the latitude are respectively C _ GS and C _ TS, the central point of a screen is obtained as C, and an original map level F before the interactive page changes and a current map level R after the interactive page changes are obtained;
step 152: acquiring a partition coding list of a to-be-selected partition, wherein the partition coding list of the to-be-selected partition is related to a preset partition level list, and determining whether a partition corresponding to the partition coding list of the to-be-selected partition is a recommended partition according to the page basic parameters;
step 153: and calculating the score of the recommended administrative division of the area to be selected according to the area to be selected division coding list.
6. The GIS map-based data analysis method of claim 5, wherein said step 152 specifically comprises:
when the to-be-selected area partition coding list simultaneously meets two conditions, determining that a partition corresponding to the to-be-selected area partition coding list is a recommended partition, and adding the recommended partition into the to-be-selected area, wherein the two conditions comprise:
condition 1: if the current map level R is larger than or equal to the original map level F, the partition level numeric area is the area of plus or minus 1 of the current map level; or if the current map level R is smaller than the original map level F, the partition level sub-value range is the range from the current map level to the current level minus 2;
condition 2: the maximum value and the minimum value of the division longitude of the preset division level list are aL and aS respectively, the maximum value and the minimum value of the division longitude latitude of the preset division level list are bL and bS respectively, the maximum value and the minimum value of the division longitude of the preset division level list are displayed on a screen, and the maximum value and the minimum value of the latitude are displayed on the display screen.
7. The GIS map-based data analysis method according to claim 6, wherein the step 153 specifically comprises:
taking the difference between the maximum value C _ GL and the minimum value C _ TL of the longitude as the length, and taking the maximum value C _ GS and the minimum value C _ TS of the latitude as the width to obtain the area of the screen display area;
and calculating the score of the recommended administrative division of the area to be selected according to the area of the screen display area.
8. The GIS map-based data analysis method according to claim 7, wherein the calculating the score of the recommended administrative division of the area to be selected according to the area of the screen display area specifically comprises:
judging whether the coordinate longitude of the central point of the screen is within the range of the maximum longitude and the minimum longitude of the region or not, and the coordinate latitude of the central point of the screen is within the range of the maximum latitude and the minimum latitude of the region, if so, recording the value a as a set score;
the area of the to-be-selected region in the display region is divided by the area of the display region multiplied by 10, the result is b, whether the value b is larger than a set threshold value or not is judged, and if the value b is larger than the set threshold value, the result obtained by subtracting the set gradient score from the value b is assigned to b;
obtaining the distances from the center points of all compartments to be selected to the center point of the screen, calculating ranking scores, sorting the compartments according to the sequence from small to large according to the distance sorting, assigning the value of the compartment with the smallest distance from the center point downwards, and gradually reducing the value, wherein the obtained value is marked as c;
the resulting weight score was designated as a + b + c.
9. A GIS map-based data analysis apparatus, the apparatus comprising: a memory having code stored therein, and a processor configured to execute the code, the terminal performing the GIS map-based data analysis method of any one of claims 1 to 8 when the code is executed.
10. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the GIS map-based data analysis method of any one of claims 1 to 8.
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