CN112182125A - Business gathering area boundary identification system - Google Patents
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Abstract
The invention discloses a business aggregation area boundary identification system which comprises a building information acquisition module, a target building screening module, a density map generation module, an aggregation area preliminary vector boundary generation module, an aggregation area boundary identification module and a database, wherein the building information acquisition module is used for acquiring the information of a building; by utilizing the weighted kernel density algorithm and the natural fracture method, the invention solves the problems of large subjectivity, difficulty in objective quantification and the like in the boundary demarcation of the business gathering area in the prior art, reduces the degree of human intervention, reduces constraint conditions, and can effectively improve the objectivity and scientificity of boundary identification.
Description
Technical Field
The invention relates to the technical field of space boundary demarcation, in particular to a business gathering area boundary identification system.
Background
Business cluster boundary identification is a technique for quantitatively analyzing and identifying business cluster boundaries based on relevant data and systems. At present, many experts and scholars try to quantitatively identify and define the boundaries of business gathering areas at home and abroad, and although some systems for defining the boundaries of business gathering areas have been proposed, the systems have defects: (1) most systems require a lot of data and are difficult to acquire; (2) a certain space evaluation unit is needed to influence index calculation and result stability; (3) the objective rule of the data itself is not really taken as a basis, many steps in the specific implementation process need to be intervened by human experience, and the objectivity is poor.
Disclosure of Invention
The invention aims to provide a business aggregation area boundary identification system which comprises a building information acquisition module, a target building screening module, a density map generation module, an aggregation area preliminary vector boundary generation module, an aggregation area boundary identification module and a database.
The building information acquisition module acquires a building contour vector planar pattern spot and attribute information in a region to be detected and respectively sends the pattern spot and the attribute information to the target building screening module, the gathering region preliminary vector boundary generation module and the gathering region boundary identification module.
The attribute information of the building outline vector surface pattern spot comprises the usage of the building, the floor area of the building, the name of the building and the number of building layers.
And the target building screening module screens out the contour vector planar pattern spots of the target building according to the building application in the attribute information.
The target building is a commercial use building.
And the target building screening module screens out all contour vector planar pattern spots of the target building according to the attribute information and extracts the coordinate data of the center point of the target building material.
The step of extracting the coordinate data of the center point of the target building material by the target building screening module is as follows: and acquiring the contour vector planar image spots of the target building in a Python environment, and performing vector element format conversion to obtain the geometric center points of the vector planar elements. The geometric center point is the target building material center point.
And the target building screening module sends the coordinate data of the center point of the target building material to the density map generating module.
The density map generation module divides the area to be detected into a plurality of detection units and calculates the density value of the target building in each detection unit.
The value of the density of the target building in the x detection unit f (x)
As follows:
in the formula (I), the compound is shown in the specification,
is a nuclear weight value, hnIs the bandwidth. x-xiTo estimate point x to sample xiThe distance of (c). w is aiIs the total area of the ith target building, i.e. sample xiN is the total number of samples.
And the density map generation module generates a kernel density map Hmap according to the target building density value in each detection unit and sends the kernel density map Hmap to the aggregation area preliminary vector boundary generation module.
And the gathering area preliminary vector boundary generating module is used for processing the density map Hmap by using an optimal natural fracture method to generate a preliminary vector boundary range of the gathering area of the target building and sending the preliminary vector boundary range to the gathering area boundary identifying module.
The step of generating the preliminary vector boundary range of the target building gathering area by the gathering area preliminary vector boundary generating module is as follows:
1) and processing the density map Hmap by using an optimal natural fracture method to obtain a nuclear density fracture value, and dividing the density map Hmap into 2 clustering regions. Wherein, the difference between the 2 clustering regions is the largest, and the difference inside each clustering region is the smallest.
2) And taking the area with the nuclear density value being more than or equal to the nuclear density fracture value as the boundary area of the target building critical grid.
3) And performing space data structure conversion on the critical grid boundary area of the target building by using a double-boundary search algorithm to obtain a preliminary vector boundary range of the target building gathering area.
The method for converting the space data structure of the critical grid boundary area of the target building by using the double-boundary search algorithm comprises the following steps:
3.1) extracting boundary points and nodes, comprising the following steps: and (3) scanning the critical grid boundary region of the target building along the row direction and the column direction by using a 2 x2 grid array as a window sequence, if 4 grids in the window have only 2 different changes, marking the four grids as boundary points, and reserving all polygon original numbers of each grid. If the 4 grids in the window have more than 3 different numbers, the nodes are identified.
3.2) searching the boundary line and recording 2 polygon numbers of the boundary point group as the left and right polygons of the corresponding boundary line.
3.3) if 3 continuous points exist on one boundary line, deleting the intermediate redundant points.
And the aggregation area boundary identification module optimizes the preliminary vector boundary range of the target building aggregation area by using an optimal natural fracture method, and identifies the boundary range of the target building aggregation area.
The steps of identifying the boundary range of the target building gathering area are as follows:
1) and performing natural fracture on the preliminary vector boundary range according to the area of the pattern spots by using an optimal natural fracture method to obtain a natural fracture value, and dividing the preliminary vector boundary range into 2 clustering regions. Wherein, the difference between the 2 clustering regions is the largest, and the difference inside each clustering region is the smallest.
2) And taking the area with the nuclear density value being greater than or equal to the natural fracture value as the boundary range of the target building gathering area.
The database stores data of a building information acquisition module, a target building screening module, a density map generation module, a gathering area preliminary vector boundary generation module and a gathering area boundary identification module.
The method has the advantages that the method solves the problems of high subjectivity, difficulty in objective quantification and the like in the boundary determination of the business gathering area in the prior art by utilizing a weighted kernel density algorithm and a natural fracture method, reduces the degree of human intervention, reduces constraint conditions and can effectively improve the objectivity and scientificity of boundary identification.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a business aggregation area boundary identification system according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of nine-district building data of main city of Chongqing city disclosed in the embodiment of the present application;
FIG. 3 is a schematic diagram of a core point acquisition result of a commercial building according to an embodiment of the present disclosure;
FIG. 3(a) is a schematic diagram of a commercial building;
FIG. 3(b) is a schematic diagram of centroid point data results;
FIG. 4 is a schematic illustration of a weighted nuclear density analysis and purification disclosed in an embodiment of the present application;
FIG. 4(a) is the result of weighted nuclear density analysis;
FIG. 4(b) shows the result of the nuclear density purification process;
FIG. 5 is a schematic diagram of a business aggregation area boundary extraction result disclosed in an embodiment of the present application;
FIG. 5(a) is a schematic diagram of the preliminary vector boundary of the aggregation region;
FIG. 5(b) is a schematic diagram of the boundary range of the aggregation region vector.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1 to 5, a business aggregation area boundary identification system includes a building information acquisition module, a target building screening module, a density map generation module, an aggregation area preliminary vector boundary generation module, an aggregation area boundary identification module, and a database.
The building information acquisition module acquires a building contour vector planar pattern spot and attribute information in a region to be detected and respectively sends the pattern spot and the attribute information to the target building screening module, the gathering region preliminary vector boundary generation module and the gathering region boundary identification module.
The attribute information of the building outline vector surface pattern spot comprises the usage of the building, the floor area of the building, the name of the building and the number of building layers.
And the target building screening module screens out the contour vector planar pattern spots of the target building according to the building application in the attribute information.
The target building is a commercial use building.
And the target building screening module screens out all contour vector planar pattern spots of the target building according to the attribute information and extracts the coordinate data of the center point of the target building material.
The step of extracting the coordinate data of the center point of the target building material by the target building screening module is as follows: and acquiring the contour vector planar image spots of the target building in a Python environment, and performing vector element format conversion to obtain the geometric center points of the vector planar elements. The geometric center point is the target building material center point.
And the target building screening module sends the coordinate data of the center point of the target building material to the density map generating module.
The density map generation module divides the area to be detected into a plurality of detection units and calculates the density value of the target building in each detection unit.
The value of the density of the target building in the x detection unit f (x)
As follows:
in the formula (I), the compound is shown in the specification,
is a nuclear weight value, hnIs the bandwidth. x-xiTo estimate point x to sample xiThe distance of (c). w is aiIs the total area of the ith target building, i.e. sample xiThe weight of (c). n is the total number of samples, namely the number of target buildings in the xth detection unit.
And the density map generation module generates a kernel density map Hmap according to the target building density value in each detection unit and sends the kernel density map Hmap to the aggregation area preliminary vector boundary generation module.
And the gathering area preliminary vector boundary generating module is used for processing the density map Hmap by using an optimal natural fracture method to generate a preliminary vector boundary range of the gathering area of the target building and sending the preliminary vector boundary range to the gathering area boundary identifying module.
The step of generating the preliminary vector boundary range of the target building gathering area by the gathering area preliminary vector boundary generating module is as follows:
1) and processing the density map Hmap by using an optimal natural fracture method (Jenks) to obtain a nuclear density fracture value, and dividing the density map Hmap into 2 clustering areas. Wherein, the difference between the 2 clustering regions is the largest, and the difference inside each clustering region is the smallest.
2) And taking the area with the nuclear density value being more than or equal to the nuclear density fracture value as the boundary area of the target building critical grid.
3) And performing space data structure conversion on the critical grid boundary area of the target building by using a double-boundary search algorithm to obtain a preliminary vector boundary range of the target building gathering area.
The method for converting the space data structure of the critical grid boundary area of the target building by using the double-boundary search algorithm comprises the following steps:
3.1) extracting boundary points and nodes, comprising the following steps: and (3) scanning the critical grid boundary region of the target building along the row direction and the column direction by using a 2 x2 grid array as a window sequence, if 4 grids in the window have only 2 different changes, marking the four grids as boundary points, and reserving all polygon original numbers of each grid. If the 4 grids in the window have more than 3 different numbers, the nodes are identified.
3.2) searching the boundary line and recording 2 polygon numbers of the boundary point group as the left and right polygons of the corresponding boundary line.
3.3) if 3 continuous points exist on one boundary line, deleting the intermediate redundant points.
For example, (x1-x2) (y1-y2) ═ x1-x3) (y1-y2) or (x1-x3) (y2-y3) ═ x2-x3 (y1-y3), where (x1, y1), (x2, y2), (x3, y3) are coordinates of three continuous points on a certain boundary line, and (x2, y2) are redundant points and can be removed.
And the aggregation area boundary identification module optimizes the preliminary vector boundary range of the target building aggregation area by using an optimal natural fracture method, and identifies the boundary range of the target building aggregation area.
The steps of identifying the boundary range of the target building gathering area are as follows:
1) and performing natural fracture on the preliminary vector boundary range according to the area of the pattern spots by using an optimal natural fracture method to obtain a natural fracture value, and dividing the preliminary vector boundary range into 2 clustering regions. Wherein, the difference between the 2 clustering regions is the largest, and the difference inside each clustering region is the smallest.
2) And taking the area with the nuclear density value being greater than or equal to the natural fracture value as the boundary range of the target building gathering area.
The database stores data of a building information acquisition module, a target building screening module, a density map generation module, a gathering area preliminary vector boundary generation module and a gathering area boundary identification module. The database is stored in a computer readable storage medium.
Example 2:
a method for using a business clustering area boundary identification system comprises the following specific steps:
1) the method comprises the steps of firstly, acquiring and setting an environment value by using an env class in an Arcpy site package, and setting a database storing a building information survey result as a working space. The attribute table of the data of the result of the building information survey includes building information such as the number of above-ground floors, the number of underground floors, the total building area, the address, the name, the structure, the completion year and the like of each building, floor information such as the use purpose (living, business, administrative business, industry, military, educational and scientific research, medical and health and the like), the area and the like of each floor, and the data of the building in the nine areas in Chongqing Main City is shown in FIG. 2, and the attribute values of the fields recording the use purpose of each floor in the building data of the city are subjected to character string slicing and comparison through Python, and the occupation ratio of the number of the floors with the use purpose including the keyword of 'business' in the number of the whole building is calculated at the same time. Based on the processing, buildings which have usage including 'business' keywords and have the number of layers of 50% or more are screened out, so that the building pattern of the whole building, which has the main usage of 'business', is obtained, and a primary identification target is provided for the next business clustering area boundary identification.
2) The following functions are realized by using a FeatureToPoint function in the Arcpy site package: and acquiring the geometric center coordinate of each planar building pattern spot, generating point data based on the coordinate, and assigning all attributes of the planar building pattern spot to corresponding point elements. Further completing the vector element format conversion of the extracted commercial building vector surface-shaped data to obtain the centroid point of the commercial building, as shown in fig. 3;
3) calculating the nuclear density value of the commercial buildings in each observation unit by taking the total building area of each commercial building as a weight, and obtaining a nuclear density map Hmap, as shown in FIG. 4 (a);
the kernel density acquisition function is as follows:
wherein the content of the first and second substances,
for the business building nuclear density value of each observation unit f (x),
is a kernel weight value, h is a bandwidth, x-xiTo estimate the pointx to sample xiA distance of (d), wiFor the total area of the ith building, i.e. for each observation x1,…,xnThe assigned weight.
4) Processing the Hmap by using an optimal natural fracture method (Jenks) to obtain a nuclear density fracture value;
the optimal natural fracture method (Jenks) is a map grading algorithm, the algorithm principle is a small cluster, and the clustering ending condition is 'maximum variance among groups and minimum variance in groups'. By the method, the equivalent division can be performed on the Hmap, and the boundary of the commercial gathering area can be conveniently extracted in the next step.
5) The method comprises the following steps of purifying a nuclear density value by utilizing a nuclear density fracture value, dividing the nuclear density value into two types of areas which are not less than the nuclear density fracture value and are less than the nuclear density fracture value, selecting the areas which are not less than the nuclear density fracture value, and finally determining that the optimal value of the nuclear density fracture value is 33 square kilometers through repeated tests, namely, the area with the nuclear density value more than or equal to 33 square kilometers is a business aggregation area, and the boundary of the business aggregation area is a critical grid boundary of the business aggregation area, specifically:
after the nuclear density map Hmap is subjected to numerical division by an optimal natural fracture method (Jenks), a spatial distribution map with the same nuclear density can be visually displayed, and on the basis of repeated tests and verification, a critical grid boundary with a nuclear density fracture value of more than 33 square kilometers is determined as a business aggregation area, and is shown in fig. 4 (b).
6) Performing space data structure conversion on the obtained grid boundary of the business aggregation area by using a double-boundary search algorithm to obtain a primary vector boundary range of the business aggregation area;
the spatial data structure conversion refers to converting the raster data format of the kernel density value into a vector data format by using a double-boundary search algorithm. The basic idea of the double-boundary search algorithm is to keep the information of left and right polygons on boundary points through boundary extraction, wherein each boundary arc segment is composed of two parallel boundary chains and the numbers of the left and right polygons of the boundary arc segments are respectively recorded.
The method comprises the following specific steps:
6.1) extracting boundary points and nodes;
6.2) boundary line searching and left and right polygon information recording;
6.3) removing redundant points to obtain vector data.
In this step, spatial data structure conversion is performed on the obtained critical grid boundary of the business aggregation area, and data processing is completed by calling a function in the Arcpy site package and using a double-boundary search algorithm, so that a preliminary vector boundary of the business aggregation area is obtained as shown in fig. 5 (a).
7) Performing natural fracture on the preliminary boundary range data according to the area of the pattern spot by using an optimal natural fracture method (Jenks), and eliminating vector data with the area smaller than 1 square meter as gross error through repeated tests and verification, and finding out a natural fracture value;
8) and (c) purifying the business aggregation area according to the natural fracture value, namely judging the area attribute of the vector business aggregation area by utilizing Python, screening out vector graphic data with the graphic area larger than or equal to the natural fracture value, and generating a new vector data layer based on the screening result, thereby realizing the identification of the boundary range of the business aggregation area (figure 5 (b)).
Claims (9)
1. A business clustering area boundary identification system is characterized by comprising a building information acquisition module, a target building screening module, a density map generation module, a clustering area preliminary vector boundary generation module, a clustering area boundary identification module and a database.
The building information acquisition module acquires a building contour vector planar pattern spot and attribute information in a region to be detected and respectively sends the pattern spot and the attribute information to the target building screening module, the gathering zone preliminary vector boundary generation module and the gathering zone boundary identification module;
the target building screening module screens out all contour vector planar pattern spots of the target building according to the attribute information and extracts the coordinate data of the center point of the target building material;
the target building screening module sends the coordinate data of the center point of the target building material to the density map generating module;
the density map generation module divides the area to be detected into a plurality of detection units and calculates the density value of a target building in each detection unit;
the density map generation module generates a kernel density map Hmap according to the target building density value in each detection unit and sends the kernel density map Hmap to the aggregation area preliminary vector boundary generation module;
the gathering area preliminary vector boundary generating module processes the density map Hmap by using an optimal natural fracture method to generate a target building gathering area preliminary vector boundary range and sends the range to the gathering area boundary identifying module;
the aggregation area boundary identification module optimizes the preliminary vector boundary range of the target building aggregation area by using an optimal natural fracture method, and identifies the boundary range of the target building aggregation area;
the database stores data of a building information acquisition module, a target building screening module, a density map generation module, a gathering area preliminary vector boundary generation module and a gathering area boundary identification module.
2. The business aggregation zone boundary identification system of claim 1 or 2, wherein: the attribute information of the building outline vector surface pattern spot comprises the usage of the building, the floor area of the building, the name of the building and the number of building layers.
3. The business aggregation zone boundary identification system of claim 2, wherein: and the target building screening module screens out the contour vector planar pattern spots of the target building according to the building application in the attribute information.
4. The business aggregation zone boundary identification system of claim 1, wherein: the target building is a commercial use building.
5. The business aggregation zone boundary identification system of claim 1, wherein: the step of extracting the coordinate data of the center point of the target building material by the target building screening module is as follows: acquiring a contour vector planar pattern spot of a target building in a Python environment, and performing vector element format conversion to obtain a geometric central point of a vector planar element; the geometric center point is the target building material center point.
6. The business aggregation zone boundary identification system of claim 1, wherein: the value of the density of the target building in the x detection unit f (x)
As follows:
in the formula (I), the compound is shown in the specification,
is a nuclear weight value, hnIs the bandwidth; x-xiTo estimate point x to sample xiThe distance of (d); w is aiIs the total area of the ith target building, i.e. sample xiThe weight of (c); n is the total number of samples.
7. The business aggregation zone boundary identification system of claim 1, wherein the aggregation zone preliminary vector boundary generation module generates the preliminary vector boundary range of the target building aggregation zone by:
1) processing the density map Hmap by using an optimal natural fracture method to obtain a nuclear density fracture value, and dividing the density map Hmap into 2 clustering regions; the difference between the 2 clustering areas is the largest, and the difference inside each clustering area is the smallest;
2) taking the area with the nuclear density value greater than or equal to the nuclear density fracture value as a target building critical grid boundary area;
3) and performing space data structure conversion on the critical grid boundary area of the target building by using a double-boundary search algorithm to obtain a preliminary vector boundary range of the target building gathering area.
8. The business aggregation zone boundary identification system of claim 7, wherein the step of performing spatial data structure transformation on the critical grid boundary region of the target building using a double boundary search algorithm comprises:
1) extracting boundary points and nodes, comprising the following steps: scanning a critical grid boundary region of a target building along a row direction and a column direction by using a 2 x2 grid array as a window sequence, if 4 grids in the window have only 2 different changes, marking the four grids as boundary points, and reserving original numbers of all polygons of each grid; if more than 3 different numbers exist in 4 grids in the window, the grids are marked as nodes;
2) searching a boundary line, and recording 2 polygon numbers of a boundary point group as left and right polygons of the corresponding boundary line;
3) if 3 continuous points exist on one boundary line, deleting the intermediate redundant points.
9. The business community boundary identification system of claim 1, wherein the step of identifying the boundary of the target building community comprises:
1) performing natural fracture on the preliminary vector boundary range according to the area of the pattern spots by using an optimal natural fracture method to obtain a natural fracture value, and dividing the preliminary vector boundary range into 2 clustering areas; the difference between the 2 clustering areas is the largest, and the difference inside each clustering area is the smallest;
2) and taking the area with the nuclear density value being greater than or equal to the natural fracture value as the boundary range of the target building gathering area.
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