A kernel density calculation method for map segmentation and annotation configuration

Abstract

This invention relates to the field of electronic digital data processing technology, specifically disclosing a kernel density calculation method for map segmentation and annotation configuration. Combining sliding window scanning and annotation text length weighting mechanisms, it quantifies the spatial density distribution of geographic elements such as points, lines, and polygons in a digital map and outputs it as a kernel density point set. This accurately reflects the spatial element density distribution in various regions of the digital map, providing scientific data support for subsequent digital map segmentation, conflict identification, and annotation configuration. Furthermore, during the process of traversing various regions of the digital map using a sliding window method, the window size can be adjusted to adapt to different digital map scales and annotation density requirements. This invention also establishes a boundary expansion mechanism to address the sliding window edge problem, ensuring the continuity and integrity of kernel density calculation across the entire map.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing technology, and specifically discloses a kernel density calculation method for map segmentation and annotation configuration. Background Technology

[0002] With the widespread use of digital maps in various applications, map annotation configuration has become a crucial step in cartography. As the number of map features to be annotated continues to increase, their spatial distribution exhibits a highly uneven characteristic, leading to dense annotations in local areas, frequent overlaps and conflicts, and seriously affecting map readability.

[0003] The map annotation configuration problem is essentially an NP-hard problem. Its difficulty increases exponentially with the number of features, making traditional global optimization methods for the entire map area face huge challenges in terms of computational resources and time, and it is difficult to obtain high-quality results within a reasonable time.

[0004] Currently, some studies have attempted to reduce the computational complexity of annotation configuration problems through map segmentation, seeking optimal solutions within local regions to approximate a globally optimal solution. However, existing map annotation configuration techniques, especially in the map segmentation stage, typically employ grid-based uniform or regular segmentation methods. While these methods reduce computational complexity to some extent, they generally fail to consider the density differences in the spatial distribution of map features and lack targeted processing for high-density areas. This "average distribution" strategy results in annotations being overly concentrated in high-density areas, easily leading to occlusion and overlap; and annotations being sparse in low-density areas, thus affecting the overall readability of the map and the scientific validity of the annotation configuration, leading to unreasonable map segmentation methods.

[0005] This invention provides a kernel density calculation method for map segmentation and annotation configuration to solve the above-mentioned problems. Summary of the Invention

[0006] The purpose of this invention is to provide a kernel density calculation method for map segmentation and annotation configuration. This method calculates the kernel density of a map region using a sliding window approach, providing a quantitative basis for map region division and annotation configuration.

[0007] To achieve the above objectives, the basic solution of this invention provides a kernel density calculation method for map segmentation and annotation configuration, comprising the following steps:

[0008] Step A1: Obtain the digital map to be processed, obtain the vector geographic features in the digital map, extract the text string of the object to be annotated based on the attribute table fields of the vector geographic features, and record the total length of each line feature and the total area of ​​each polygon feature.

[0009] Step A2: Assign a basic kernel density weight of 1 to each type of vector geographic feature as the initial factor for subsequent local area kernel density calculation;

[0010] Step A3: Perform a continuous sliding scan of the digital map from left to right and from top to bottom, and use the center point of the sliding window as an identifier to record the corresponding kernel density value at the position of the center point;

[0011] Step A4: Calculate the kernel density value for each sliding window region and assign the result to the center point of the window as the kernel density value for that center point;

[0012] Step A5: Establish a kernel density point set to store the center point of each sliding window and the kernel density value corresponding to that center point, and use the kernel density point set as a global kernel density distribution map for map segmentation and annotation configuration.

[0013] Furthermore, in step A3, the sliding step size is half the size of the sliding window.

[0014] Furthermore, in step A3, the sliding scan process is as follows:

[0015] Step A31: Determine the sliding window size and sliding step size;

[0016] Step A32: Expand the boundaries of the digital map around its perimeter according to the sliding step size;

[0017] Step A33: Establish a scanning grid and determine the initial scanning node as the upper left corner of the expanded digital map;

[0018] Step A34: Begin the sliding scan, including scanning horizontally to the right boundary of the digital map and scanning vertically to the bottom boundary of the digital map;

[0019] Step A35: Determine the center point of the sliding window. Each window is identified by this center point, and the corresponding kernel density value is recorded at this position.

[0020] Further, in step A3, the coordinates of the center point are represented as (x_center, y_center) = (x + W / 2, y + H / 2), where x_center is the horizontal coordinate of the center point of the sliding window on the digital map, y_center is the vertical coordinate of the center point of the sliding window on the digital map, x represents the horizontal coordinate of the scanned node on the digital map, y represents the vertical coordinate of the scanned node on the digital map, W represents the width of the sliding window, W / 2 represents the horizontal sliding step, H represents the height of the sliding window, and H / 2 represents the vertical sliding step.

[0021] Furthermore, in step A4, the nuclear density value Den is calculated as follows:

[0022] ;

[0023] In the formula, P represents the set of point features within the sliding window. This represents the annotation length of the i-th point feature in the point feature set P;

[0024] In the formula, Q represents the set of line features intersecting the sliding window. This represents the length of the j-th line feature in the online feature set Q within the sliding window. This represents the total length of the j-th line element in the online element set Q. This represents the annotation length of the j-th line element in the online element set Q;

[0025] In the formula, U represents the set of facet features intersecting the sliding window. This represents the area of ​​the k-th polygon in the polygon feature set U within the sliding window. This represents the total area of ​​the k-th facet in the facet set U. This represents the annotation length of the k-th polygon feature in the polygon feature set U;

[0026] In the formula, the annotation length is uniformly equal to the length of the text string of each annotation object.

[0027] Furthermore, in step A5, the kernel density point set Represented as:

[0028] ;

[0029] In the formula, This represents the coordinates of the center point of the first sliding window. This represents the kernel density value corresponding to the center point of the first sliding window.

[0030] The principle and effect of this solution are as follows:

[0031] 1. Compared with existing technologies, this invention generates a global kernel density distribution map by estimating the spatial distribution of map elements. This map serves as the basis for dividing digital map regions and identifies high-density areas with a high risk of annotation conflicts. Based on the global kernel density distribution map, high-density areas can be prioritized for local subdivision and annotation configuration optimization. This reduces the dimensionality of the problem-solving process while improving overall computational efficiency and system performance, helping to more efficiently approximate the globally optimal annotation configuration results in complex scenarios.

[0032] 2. Compared with existing technologies, this invention calculates kernel density for digital map regions using a sliding window approach, providing a quantitative basis for regional division and annotation configuration of digital maps. Combining sliding window scanning with a text length weighting mechanism for annotations, it quantifies the spatial density distribution of geographic elements such as points, lines, and polygons in the digital map and outputs it as a kernel density point set. This invention can accurately reflect the spatial element density distribution of each region of the digital map, providing scientific data support for subsequent digital map segmentation, conflict identification, and annotation configuration. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart of a kernel density calculation method for map segmentation and annotation configuration proposed in an embodiment of this application is shown. Detailed Implementation

[0035] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0036] A kernel density calculation method for map segmentation and annotation configuration, implementing, for example... Figure 1 As shown, it includes the following steps:

[0037] Step A1: Obtain the digital map to be processed, obtain vector geographic features such as points, lines, and polygons from the digital map, extract the text string of the object to be annotated based on the attribute table fields of the vector geographic features, and record the total length of each line feature and the total area of ​​each polygon feature.

[0038] In this embodiment, a parameter `Feature` of type `GeoData` is defined to store text attribute information including vector geographic features such as points, lines, and polygons, as well as the objects to be annotated. A parameter `Lens` of type `list` is also defined to store the length of the text string for each object to be annotated.

[0039] Step A2: Assign a basic kernel density weight of 1 to each type of vector geographic feature as the initial factor for subsequent local area kernel density calculations.

[0040] Step A3: Perform a continuous sliding scan of the digital map from left to right and from top to bottom. In this embodiment, the sliding step size is half the size of the sliding window. During the sliding scan, the center point of the sliding window is used as an identifier, and the corresponding kernel density value is recorded at the position of the center point to achieve continuous kernel density coverage. In other embodiments, the length of the sliding step size cannot be greater than the side length of the sliding window.

[0041] Specifically, the sliding scan process is as follows:

[0042] Step A31: Define the sliding window parameters. Determine the sliding window size as a parameter Size of type int×int. Calculate the local kernel density using the parameter Size. Let the sliding window size Size be: Size=(W,H) (default such as 10×10 meters), and the sliding step size be W / 2 (horizontal) and H / 2 (vertical), where W represents the width of the sliding window and H represents the height of the sliding window.

[0043] Step A32: Extend the boundaries of the digital map on all four sides by half the size of the sliding window, that is, extend the boundary area by W / 2 or H / 2 to the left, right, top, and bottom. This extension method ensures that even when the center point of the sliding window is located at the edge of the original map, its coverage area still falls completely within the extended digital map area, thus guaranteeing the integrity and continuity of the kernel density calculation of the edge area and avoiding missing or biased edge values.

[0044] Step A33: Establish a scanning grid and determine the initial scanning node (x_start, y_start) as the upper left corner of the expanded digital map, where x_start is the horizontal coordinate of the initial scanning node on the digital map and y_start is the vertical coordinate of the initial scanning node on the digital map.

[0045] Step A34: Begin sliding scan, including horizontal scan:

[0046] Starting from x=x_start, the sliding step size is W / 2, until the right boundary of the digital map, where x represents the x-coordinate of the scanned node on the digital map;

[0047] Vertical scan:

[0048] Starting from y=y_start, the sliding step size is H / 2, until the lower boundary of the digital map, where y represents the vertical coordinate of the node being scanned on the digital map.

[0049] Step A35: Determine the center point of the sliding window. Each window is identified by this center point, and the corresponding kernel density value Den is recorded at this position. In this embodiment, for each sliding window, the coordinates of its center point are (x_center, y_center) = (x + W / 2, y + H / 2), where x_center is the horizontal coordinate of the center point of the sliding window on the digital map, and y_center is the vertical coordinate of the center point of the sliding window on the digital map.

[0050] Step A4: Calculate the kernel density value for each sliding window region and assign the result to the center point of that window as the kernel density value Den for that center point. The formula for calculating the kernel density value Den is as follows:

[0051] ;

[0052] In the formula, P represents the set of point features within the sliding window. This represents the annotation length of the i-th point feature in the point feature set P;

[0053] In the formula, Q represents the set of line features intersecting the sliding window. This represents the length of the j-th line feature in the online feature set Q within the sliding window. This represents the total length of the j-th line element in the online element set Q. This represents the annotation length of the j-th line element in the online element set Q;

[0054] In the formula, U represents the set of facet features intersecting the sliding window. This represents the area of ​​the k-th polygon in the polygon feature set U within the sliding window. This represents the total area of ​​the k-th facet in the facet set U. This represents the annotation length of the k-th polygon feature in the polygon feature set U;

[0055] In the formula, the annotation length is obtained by the parameter Lens, which is uniformly set to the length of the text string of each annotation object, serving as an important factor of kernel density.

[0056] Step A5: Define the parameter DensityPoints as a set of kernel density points to serve as a global kernel density distribution map for map segmentation and annotation configuration. The kernel density point set stores the center point of each sliding window and the kernel density value corresponding to that center point.

[0057] In this embodiment, the kernel density point set Represented as:

[0058] ;

[0059] In the formula, This represents the coordinates of the center point of the first sliding window. This represents the kernel density value corresponding to the center point of the first sliding window.

[0060] In step A5, the spatial location of each window center point and its corresponding kernel density value are recorded as a data record, that is, each kernel density value Den and its corresponding window center coordinates. Together they form a record item And add it to the kernel density point set DensityPoints.

[0061] After obtaining the kernel density point set, by comparing the kernel density value distribution of each region in the kernel density point set, high-density and low-density regions can be differentiated, thereby achieving map segmentation and annotation configuration.

[0062] This invention combines sliding window scanning with a text length weighting mechanism to quantify the spatial density distribution of vector geographic elements such as points, lines, and polygons in digital maps, and outputs it as a kernel density point set. This invention can accurately reflect the density distribution of spatial elements in different areas of a digital map, providing scientific data support for subsequent digital map segmentation, conflict identification, and annotation configuration.

[0063] The kernel density point set output by this invention can provide density references for various digital map region division methods, helping to achieve more reasonable region segmentation and annotation layout. Furthermore, this invention uses a sliding window approach to traverse each region of the digital map, and the window size can be adjusted to adapt to different digital map scales and annotation density requirements. This invention also designs a boundary expansion mechanism to address the sliding window edge problem, ensuring the continuity and integrity of kernel density calculation across the entire map.

[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any indirect modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A kernel density calculation method for map segmentation and annotation configuration, characterized in that, Includes the following steps: Step A1: Obtain the digital map to be processed, obtain the vector geographic features in the digital map, extract the text string of the object to be annotated based on the attribute table fields of the vector geographic features, and record the total length of each line feature and the total area of ​​each polygon feature. Step A2: Assign a basic kernel density weight of 1 to each type of vector geographic feature as the initial factor for subsequent local area kernel density calculation; Step A3: Perform a continuous sliding scan of the digital map from left to right and from top to bottom, and use the center point of the sliding window as an identifier to record the corresponding kernel density value at the position of the center point; Step A4: Calculate the kernel density value for each sliding window region and assign the result to the center point of the window as the kernel density value for that center point; Step A5: Establish a kernel density point set to store the center point of each sliding window and the kernel density value corresponding to that center point, and use the kernel density point set as a global kernel density distribution map for map segmentation and annotation configuration; In step A3, the coordinates of the center point are represented as (x_center, y_center) = (x + W / 2, y + H / 2), where x_center is the horizontal coordinate of the center point of the sliding window on the digital map, y_center is the vertical coordinate of the center point of the sliding window on the digital map, x represents the horizontal coordinate of the scanned node on the digital map, y represents the vertical coordinate of the scanned node on the digital map, W represents the width of the sliding window, W / 2 represents the horizontal sliding step, H represents the height of the sliding window, and H / 2 represents the vertical sliding step. In step A4, the nuclear density value Den is calculated as follows: ； In the formula, P represents the set of point features within the sliding window. This represents the annotation length of the i-th point feature in the point feature set P; In the formula, Q represents the set of line features intersecting the sliding window. This represents the length of the j-th line feature in the online feature set Q within the sliding window. This represents the total length of the j-th line element in the online element set Q. This represents the annotation length of the j-th line element in the online element set Q; In the formula, U represents the set of facet features intersecting the sliding window. This represents the area of ​​the k-th polygon in the polygon feature set U within the sliding window. This represents the total area of ​​the k-th facet in the facet set U. This represents the annotation length of the k-th polygon feature in the polygon feature set U; In the formula, the annotation length is uniformly equal to the length of the text string of each annotation object.

2. The kernel density calculation method for map segmentation and annotation configuration according to claim 1, characterized in that, In step A3, the sliding step size is half the size of the sliding window.

3. The kernel density calculation method for map segmentation and annotation configuration according to claim 1, characterized in that, In step A3, the sliding scan process is as follows: Step A31: Determine the sliding window size and sliding step size; Step A32: Expand the boundaries of the digital map around its perimeter according to the sliding step size; Step A33: Establish a scanning grid and determine the initial scanning node as the upper left corner of the expanded digital map; Step A34: Begin the sliding scan, including scanning horizontally to the right boundary of the digital map and scanning vertically to the bottom boundary of the digital map; Step A35: Determine the center point of the sliding window. Each window is identified by this center point, and the corresponding kernel density value is recorded at this position.

4. The kernel density calculation method for map segmentation and annotation configuration according to claim 1, characterized in that, In step A5, the kernel density point set Represented as: ； In the formula, This represents the coordinates of the center point of the first sliding window. This represents the kernel density value corresponding to the center point of the first sliding window.

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