Commercial district drawing method and device based on geographic space aggregation

Abstract

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

Description

Technical Field

[0001] This invention relates to the field of market planning, and more particularly to a method and apparatus for mapping business districts based on geospatial aggregation. Background Technology

[0002] The delineation of trade zones is one of the core bases for brands to formulate market strategies and optimize channel layout. Traditional methods of trade zone delineation mainly rely on manual experience and static data, usually using a "circular buffer zone" model or drawing maps based on manual experience to roughly cover the area around the outlets, combining on-site visits, map observation, and subjective judgment to determine the boundary range. However, traditional methods of drawing trade zones have the following drawbacks: 1. Highly subjective, with blurred boundaries Delineating circular or rectangular business districts based on manual experience without considering actual road network structure, administrative boundaries, and consumer route preferences leads to a disconnect between the business district outlines and actual consumer behavior. The lack of quantitative standards for business district boundaries makes them susceptible to disagreements arising from subjective perceptions among evaluators, affecting the consistency of cross-regional strategies.

[0003] 2. Data lag and low update efficiency. The long update cycle of basic data (such as outlet distribution) makes it difficult to reflect brand expansion or market changes in real time, causing business district planning to lag behind actual business needs. Non-sales outlets (such as after-sales service centers) are not effectively eliminated, interfering with the accurate identification of core consumer areas in the business district.

[0004] 3. The classification of levels is crude and lacks dynamic adaptability. The current business district rating system relies solely on outlet density or area size, failing to integrate multi-dimensional data such as consumer accessibility and the economic level of the administrative region, making it difficult to support differentiated marketing strategies. Traditional methods cannot dynamically track the evolution trends of business districts (such as the rise of emerging markets or the shrinkage of declining areas), thus hindering the efficient allocation of brand resources.

[0005] 4. Outdated technology and insufficient automation The lack of deep integration between GIS spatial analysis and database technologies, coupled with reliance on manual processing of multi-source heterogeneous data (such as river networks and administrative divisions), results in inefficiency and a high risk of errors. Furthermore, the failure to utilize advanced technologies such as aggregation radius algorithms and river network accessibility models makes it difficult for business district mapping results to adapt to complex urban structures. 5. The costs are enormous, and on-site site visits are prone to errors and omissions. Traditional business district mapping relies on manual on-site visits and map observation, requiring significant investment of manpower, time, and transportation costs. The difficulty increases exponentially, especially when covering a nationwide area. On-site investigations lack clear objectives and standardized procedures, making them prone to human error, such as overlooking locations or inaccurate labeling. Issues like failing to exclude non-sales outlets or shifting outlet coordinates directly impact the accuracy of business district boundary definition. Subjective on-site visits are difficult to quantify, and significant discrepancies can arise within the same area due to differences in the experience of the personnel involved, further exacerbating data error rates. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a method and apparatus for drawing business districts based on geospatial aggregation, which can effectively solve the problems of blurred boundaries, data lag and poor strategy adaptability in traditional technologies, achieve accurate definition of business district outlines, and thus improve the efficiency of business district drawing.

[0007] One embodiment of the present invention provides a method for drawing business districts based on geospatial aggregation, comprising the following steps: Spatial density clustering is performed on all dots in the drawing space to obtain several dot clusters; Based on the aforementioned clusters of dots, several convex hulls are generated accordingly. The boundary of the convex hull is optimized based on the collected river network data, and the optimized convex hull is determined as the business district within the space to be drawn.

[0008] Furthermore, the step of performing spatial density clustering on all dots in the drawing space to obtain several dot clusters specifically includes: Based on the number of nodes in the neighborhood of each node in EPS, several core points are determined; The core point and all points in its eps neighborhood are merged into the same cluster, resulting in several core clusters. The core clusters with overlapping dots are merged into the same cluster until there are no overlapping dots between the remaining clusters, thus obtaining the plurality of dot clusters.

[0009] Furthermore, the step of generating several convex hulls based on the aforementioned clusters of dots specifically includes: Based on all the said dot clusters, the ST_ConvexHull function is used to generate the corresponding convex hulls.

[0010] Preferably, after generating the convex hull, the method further includes: The buffer for the convex hull is generated using the St_Buffer function; When there are new dots in the buffer that are not included in the convex hull, the new dots are incorporated into the dot cluster corresponding to the convex hull, and the convex hull is updated by the ST_ConvexHull function according to the dot cluster after incorporating the new dots.

[0011] Furthermore, the step of generating the convex hull buffer using the St_Buffer function specifically includes: The St_Buffer function generates a polygonal region, which includes all points whose distance from the convex hull is less than or equal to a preset buffer distance, and the polygonal region is determined as the buffer of the convex hull.

[0012] Furthermore, the boundary optimization of the convex hull based on the collected river network data specifically includes: Based on the river network data, the ST_Dump function is used to decompose all rivers in the space to be drawn into atomic geometric models, resulting in several river geometric models. The difference between the convex hull and the river geometry model is calculated using the ST_Difference function, and the convex hull is then cut according to the difference to obtain several effective convex hulls. The minimum bounding circle corresponding to the effective convex hull is generated by the ST_MinimumBoundingCircle function, and the minimum bounding circle is determined as the business district.

[0013] Preferably, the river network data specifically refers to the main river data within the space to be drawn, where the area exceeds a preset area threshold.

[0014] Furthermore, after obtaining the aforementioned business district, it also includes: The level and label of the business district are determined based on the number of brands, outlets, and outlet types within the business district.

[0015] Furthermore, after obtaining the aforementioned business district, it also includes: Based on a pre-set big data model, the geographical coordinates, terrain features, and industry landmarks of the business district are analyzed using multi-dimensional semantic fusion to automatically generate standardized business district names.

[0016] Another embodiment of the present invention provides a business district mapping device based on geospatial aggregation, comprising: a clustering module, a generation module, and an optimization module. The clustering module is used to perform spatial density clustering on all dots in the drawing space to obtain several dot clusters; The generation module is used to generate several convex hulls based on the several clusters of dots respectively; The optimization module is used to optimize the boundary of the convex hull based on the collected river network data, and to determine the optimized convex hull as the business district within the space to be drawn.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: By using spatial density clustering algorithms, the boundary error of the drawn business districts can be reduced. At the same time, by optimizing the boundary based on river network data, discontinuous business district areas separated by rivers and road networks can be accurately identified, improving the boundary fit of the drawn business districts and thus improving the efficiency and accuracy of business district drawing. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for drawing business districts based on geospatial aggregation, as provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a convex hull model provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of a convex hull after model update, provided in an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of a convex hull based on river network data after segmentation, provided as an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of a business district mapping device based on geospatial aggregation, provided as another embodiment of the present invention. Detailed Implementation

[0023] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] Reference Figure 1 The following is a flowchart illustrating a method for drawing business districts based on geospatial aggregation, according to an embodiment of the present invention, comprising the following steps: S1: Perform spatial density clustering on all dots in the drawing space to obtain several dot clusters; S2: Generate several convex hulls based on the aforementioned clusters of dots; S3: Optimize the boundary of the convex hull based on the collected river network data, and determine the optimized convex hull as the business district within the space to be drawn.

[0026] For step S1, specifically, performing spatial density clustering on all the grid points in the to-be-drawn space to obtain several grid point clusters, which specifically includes: Determining several core points according to the number of grid points within the eps neighborhood corresponding to each grid point; Merging the core points and all the grid points within their eps neighborhoods into the same cluster, correspondingly obtaining several core clusters; Merging the core clusters with overlapping grid points into the same cluster until there are no overlapping grid points between the remaining clusters, obtaining the several grid point clusters.

[0027] In a preferred embodiment, for the to-be-drawn space, first, it is necessary to implement spatial density clustering of the grid points in the space through ST_ClusterDBSCAN (Density-Based Spatial Clustering of Applications with Noise). This function analyzes the density and distance relationships of spatial objects, divides objects with similar density and adjacent in space into the same category, automatically identifies and excludes noise points, and divides grid points with dense geographical distributions into independent clusters based on the principle of density reachability, without presetting the number of clusters, and only requires configuring the distance threshold (eps) and the minimum density parameter (minpoints). Specifically, the generation process of the grid point clusters is as follows: Step 1: Identify core points Calculate the number of points within the eps neighborhood for each grid point. If the number of points within the neighborhood of point P ≥ minpoints, then determine point P as a core point, marking the starting position of a cluster.

[0028] Step 2: Density reachability expansion Starting from the core point, merge all points that are density reachable (i.e., points connected by continuous eps neighborhoods) into the same cluster. At the same time, if point Q is within the neighborhood of core point P and Q itself is a core point, then the neighborhood of Q will also be merged into the cluster, and repeat this process until no further expansion is possible.

[0029] Step 3: Noise filtering Finally, mark points that are not included in any cluster (i.e., the number of points within the neighborhood < minpoints and not density-connected to any core point) as noise points and exclude them from the final clustering result.

[0030] The advantage of this clustering method is that it does not require presetting the number of clusters, can dynamically adapt to the grid point distribution characteristics of different regions, and through the noise filtering mechanism, avoids the interference of abnormal points on the clustering result. Compared with the traditional "circular buffer" mode, this preferred embodiment introduces the linkage mechanism of the density clustering algorithm and spatial constraint conditions, solving the problem of fuzzy boundaries manually defined.

[0031] For step S2, specifically, respectively generating several convex hulls based on the several grid point clusters, which specifically includes: Based on all the said dot clusters, the ST_ConvexHull function is used to generate the corresponding convex hulls.

[0032] Preferably, after generating the convex hull, the method further includes: The buffer for the convex hull is generated using the St_Buffer function; When there are new dots in the buffer that are not included in the convex hull, the new dots are incorporated into the dot cluster corresponding to the convex hull, and the convex hull is updated by the ST_ConvexHull function according to the dot cluster after incorporating the new dots.

[0033] Furthermore, the step of generating the convex hull buffer using the St_Buffer function specifically includes: The St_Buffer function generates a polygonal region, which includes all points whose distance from the convex hull is less than or equal to a preset buffer distance, and the polygonal region is determined as the buffer of the convex hull.

[0034] In a preferred embodiment, refer to Figure 2 This is a schematic diagram of a convex hull model provided in an embodiment of the present invention. Figure 2 It can be seen that for the clusters of points obtained after clustering, calling the ST_ConvexHull function to generate the minimum convex hull can accurately define the boundary of the business district, replacing the traditional manually defined circular buffer mode, and significantly improving the scientific nature and automation level of boundary definition. Here, the convex hull represents the minimum convex geometry that contains all input geometry.

[0035] Reference Figure 3 This is a schematic diagram of a convex hull after model update according to an embodiment of the present invention. Figure 3 As can be seen, after generating the convex hull, to avoid missing any points, the ST_Buffer function is needed to generate a dynamic buffer corresponding to each convex hull based on the business district industry standard. Specifically, the ST_Buffer function generates the buffer by calculating a POLYGON or MULTIPOLYGON, representing all points whose distance to the geometric / geographic object is less than or equal to a given distance.

[0036] Then, spatial intersection detection identifies newly added points within the buffer. If any uncovered points exist, an expansion process is triggered. Finally, ST_ConvexHull is executed on all expanded convex hulls to regenerate the minimum convex hull boundary and update the convex hull. This update process eliminates the fragmentation problem caused by traditional polygon segmentation.

[0037] Compared to existing technologies, this preferred embodiment triggers data updates and business district recalculation through a preset rule engine, which is significantly better than the inefficient mode of existing technologies that rely on static data and manual updates.

[0038] For step S3, specifically, the boundary optimization of the convex hull based on the collected river network data includes: Based on the river network data, the ST_Dump function is used to decompose all rivers in the space to be drawn into atomic geometric models, resulting in several river geometric models. The difference between the convex hull and the river geometry model is calculated using the ST_Difference function, and the convex hull is then cut according to the difference to obtain several effective convex hulls. The minimum bounding circle corresponding to the effective convex hull is generated by the ST_MinimumBoundingCircle function, and the minimum bounding circle is determined as the business district.

[0039] Preferably, the river network data specifically refers to the main river data within the space to be drawn, where the area exceeds a preset area threshold.

[0040] In a preferred embodiment, refer to Figure 4 This is a schematic diagram of the convex hull after river network data segmentation, provided in an embodiment of the present invention. Regarding the impact of rivers on the accessibility of commercial areas, this preferred embodiment designs the following process: (1) Data filtering: Extract river network data with an area greater than 1,000 square meters within the space to be drawn to ensure computational efficiency and business relevance; (2) Geometric decomposition: Based on the collected river network data, the complex river polygons are decomposed into atomic geometric types (such as line segments or sub-faces) by ST_Dump, which facilitates fine processing; (3) Spatial cutting: Use ST_Difference to calculate the difference between the convex hull of the business district and the geometric model of the river, remove the invalid areas in the convex hull that are blocked by the river, and correct the actual coverage of the convex hull; (4) Boundary optimization: The ST_MinimumBoundingCircle function is called on the corrected convex hull to generate the minimum bounding circle, further simplifying the boundary shape and adapting to administrative division constraints, improving the visualization effect and application compatibility, and finally obtaining the business district.

[0041] Compared to existing technologies, this preferred embodiment innovatively incorporates the river blocking effect into the business district boundary correction logic, improving the consistency between the planning results and actual traffic accessibility.

[0042] Furthermore, after obtaining the aforementioned business district, it also includes: The level and label of the business district are determined based on the number of brands, outlets, and outlet types within the business district.

[0043] In a preferred embodiment, after obtaining the business district, its level can be determined based on the number of brands and outlets within it. In this preferred embodiment, the specific level classification rules are as follows: The prerequisites for the formation of a commercial district are: number of brands >= 3 and number of outlets >= 5; When the number of outlets in a business district is greater than or equal to 15, the business district is determined to be a mainstream business district. When the number of outlets in the business district is greater than or equal to 8 and less than 15, the business district is determined to be a general business district. If the number of outlets in a business district is greater than or equal to 5 and less than 8, the business district is determined to be a micro-business district.

[0044] Additionally, the label for a business district can be determined based on the type of outlets within it. For example, taking a vehicle sales business district as an example, the specific label determination rules are as follows: A business district is designated as a new energy business district if it contains five or more new energy outlets. A business district is designated as a new energy business district if it contains five or more fuel outlets. A business district is defined as a comprehensive business district if it contains five or more new energy vehicle outlets and five or more fuel vehicle outlets.

[0045] Furthermore, after obtaining the aforementioned business district, it also includes: Based on a pre-set big data model, the geographical coordinates, terrain features, and industry landmarks of the business district are analyzed using multi-dimensional semantic fusion to automatically generate standardized business district names.

[0046] In a preferred embodiment, after determining the level and label of the business district, a multi-dimensional semantic fusion analysis can be performed on the geographical coordinates, terrain features and industry identifiers of the business district based on a big data model to automatically generate standardized business district names that conform to the characteristics of consumption scenarios, thereby improving the efficiency of business district naming.

[0047] Reference Figure 5 The diagram below illustrates the structure of a geospatial aggregation-based business district mapping device according to another embodiment of the present invention, comprising: a clustering module 101, a generation module 102, and an optimization module 103. The clustering module 101 is used to perform spatial density clustering on all dots in the drawing space to obtain several dot clusters; The generation module 102 is used to generate a plurality of convex hulls based on the plurality of dot clusters respectively; The optimization module 103 is used to optimize the boundary of the convex hull based on the collected river network data, and to determine the optimized convex hull as the business district within the space to be drawn.

[0048] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for drawing business districts based on geospatial aggregation, characterized in that, Includes the following steps: Spatial density clustering is performed on all dots in the drawing space to obtain several dot clusters; Based on the aforementioned clusters of dots, several convex hulls are generated accordingly. The boundary of the convex hull is optimized based on the collected river network data, and the optimized convex hull is determined as the business district within the space to be drawn.

2. The business district mapping method based on geospatial aggregation as described in claim 1, characterized in that, The process of performing spatial density clustering on all dots within the drawing space to obtain several dot clusters specifically includes: Based on the number of nodes in the neighborhood of each node in EPS, several core points are determined; The core point and all points in its eps neighborhood are merged into the same cluster, resulting in several core clusters. The core clusters with overlapping dots are merged into the same cluster until there are no overlapping dots between the remaining clusters, thus obtaining the plurality of dot clusters.

3. The business district mapping method based on geospatial aggregation as described in claim 1, characterized in that, The step of generating several convex hulls based on the aforementioned clusters of dots specifically includes: Based on all the said dot clusters, the ST_ConvexHull function is used to generate the corresponding convex hulls.

4. The business district mapping method based on geospatial aggregation as described in claim 3, characterized in that, After generating the convex hull, the process also includes: The buffer for the convex hull is generated using the St_Buffer function; When there are new dots in the buffer that are not included in the convex hull, the new dots are incorporated into the dot cluster corresponding to the convex hull, and the convex hull is updated by the ST_ConvexHull function according to the dot cluster after incorporating the new dots.

5. The business district mapping method based on geospatial aggregation as described in claim 4, characterized in that, The process of generating the convex hull buffer using the St_Buffer function specifically includes: The St_Buffer function generates a polygonal region, which includes all points whose distance from the convex hull is less than or equal to a preset buffer distance, and the polygonal region is determined as the buffer of the convex hull.

6. The business district mapping method based on geospatial aggregation as described in claim 1, characterized in that, The boundary optimization of the convex hull based on the collected river network data specifically includes: Based on the river network data, the ST_Dump function is used to decompose all rivers in the space to be drawn into atomic geometric models, resulting in several river geometric models. The difference between the convex hull and the river geometry model is calculated using the ST_Difference function, and the convex hull is then cut according to the difference to obtain several effective convex hulls. The minimum bounding circle corresponding to the effective convex hull is generated by the ST_MinimumBoundingCircle function, and the minimum bounding circle is determined as the business district.

7. The business district mapping method based on geospatial aggregation as described in claim 6, characterized in that, The river network data specifically refers to the main river data within the space to be drawn, where the area exceeds a preset area threshold.

8. The business district mapping method based on geospatial aggregation as described in claim 1, characterized in that, After obtaining the aforementioned business district, the following is also included: The level and label of the business district are determined based on the number of brands, outlets, and outlet types within the business district.

9. The business district mapping method based on geospatial aggregation as described in claim 1, characterized in that, After obtaining the aforementioned business district, the following is also included: Based on a pre-set big data model, the geographical coordinates, terrain features, and industry landmarks of the business district are analyzed using multi-dimensional semantic fusion to automatically generate standardized business district names.

10. A business district mapping device based on geospatial aggregation, characterized in that, include: Clustering module, generation module, and optimization module: The clustering module is used to perform spatial density clustering on all dots in the drawing space to obtain several dot clusters; The generation module is used to generate several convex hulls based on the several clusters of dots respectively; The optimization module is used to optimize the boundary of the convex hull based on the collected river network data, and to determine the optimized convex hull as the business district within the space to be drawn.

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