Urban high-intensity development district identification and utilization efficiency detection method based on grid growth

Abstract

The invention discloses an urban high-intensity development district identification and utilization efficiency detection method based on grid growth, and the method comprises the steps: obtaining urban development data, and carrying out the grid processing of the urban development data; according to the gridded city development data, utilizing a city development district identification method to identify city development districts meeting conditions; based on a district development intensity stability index, a district geometric morphology rationality index and a district spatial relationship independence index, detecting a city development district identification result, and based on a single use efficiency index or a comprehensive use efficiency index, performing use efficiency detection; and outputting identification and use efficiency detection results of the city development districts. According to the method, the high-intensity development district and the use efficiency thereof are accurately identified, a scientific basis can be provided for urban density zoning control, updating unit delimitation and public resource reconfiguration, and refined urban design optimization and governance decision are supported.

Description

Technical Field

[0001] This invention relates to the field of urban planning technology, and in particular to a method for identifying high-intensity urban development areas and detecting their utilization efficiency based on grid growth. Background Technology

[0002] With rapid economic development and the continuous acceleration of urban-rural integration, some core functional areas of cities are making major contributions to urban GDP and innovation vitality due to their highly intensive land development and industrial agglomeration effects.

[0003] However, in practice, the population carrying capacity and industrial agglomeration capacity of many high-intensity development areas have not been effectively matched with their construction intensity, resulting in a number of structural underutilization phenomena of "high input and low output." This is due to internal reasons such as changes in urban economic structure, changes in industry demand, and increased new supply; on the other hand, inadequate basic supporting facilities such as transportation, medical care, education, shopping, and living are also external reasons. Therefore, accurately identifying high-intensity development areas and assessing their utilization efficiency is a prerequisite for improving the utilization efficiency of high-intensity development areas in cities.

[0004] Despite substantial research findings on the identification and analysis of high-intensity urban areas, the following shortcomings remain: (1) Existing recognition methods lack the accuracy and coherence of boundary recognition, fail to accurately identify features and grid neighborhood relationships, and are difficult to capture irregular shapes at the edges of areas. The algorithms lack interpretability and robustness.

[0005] (2) Research on the efficiency of land use in high-intensity development areas mainly focuses on the selection of different detection indicators, but there is no refined detection research that distinguishes different land use attributes.

[0006] Research on the identification and efficiency testing of high-intensity areas is relatively independent, lacking a systematic analytical framework that extends from identification to testing and then to optimization. Although existing studies have considered different land use attributes in efficiency testing, the methods used in the identification and testing stages are not closely integrated, making it difficult to form a complete decision support system.

[0007] Therefore, existing technologies still need improvement. Summary of the Invention

[0008] The technical problem to be solved by the present invention is that, in view of the defects of the prior art, the present invention provides a method for identifying urban high-intensity development areas and detecting their utilization efficiency based on grid growth, so as to solve the problems of insufficient accuracy and consistency in boundary identification and lack of fine detection for different land use attributes in the existing identification methods.

[0009] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention provides a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, comprising: Acquire urban development data and perform grid-based processing on the urban development data; Based on gridded urban development data, urban development zones that meet the criteria are identified using urban development zone identification methods. The identification results of urban development areas are tested based on the stability index of development intensity, the rationality index of geometric shape of the area, and the independence index of spatial relationship of the area. The use efficiency is tested based on a single use efficiency index or a comprehensive use efficiency index. Output the identification and utilization efficiency test results of urban development areas.

[0010] In one implementation, acquiring urban development data and performing grid-based processing on the urban development data includes: The urban development data is obtained by acquiring development intensity data, resident population density data, nighttime light brightness data, and point of interest density data. Based on the preset grid size, the urban research area is divided into multiple grid units using administrative boundaries as a mask; The urban data coordinate system is transformed, and the acquired urban development data is associated with multiple grid units to obtain development intensity data, resident population density data, nighttime light brightness data, and point of interest density data corresponding to each grid unit.

[0011] In one implementation, identifying urban development zones that meet certain conditions based on gridded urban development data using an urban development zone identification method includes: Based on the gridded urban development data, the urban development area identification method is used to identify the growth of development areas and obtain the initially identified urban development areas. The initially identified urban development zones are merged or divided to obtain the urban development zones that meet the conditions.

[0012] In one implementation, the step of performing development zone growth identification based on the gridded urban development data and the urban development zone identification method to obtain preliminarily identified urban development zones includes: Select a seed grid based on the gridded urban development data; Starting from all seed graticles, the graticle with the highest development intensity among the neighboring graticles of each development zone is taken as the growth graticle. The development zone is grown based on the growth graticle to obtain the initially identified urban development zone.

[0013] In one implementation, the step of merging or dividing the initially identified urban development zones to obtain the urban development zones that meet the conditions includes: Select the two areas with the largest overlap from all the initially identified urban development zones; Based on the merging and splitting conditions, the two selected areas are merged or split, and the areas are updated to obtain the urban development areas that meet the conditions.

[0014] In one implementation, the detection of urban development zone identification results based on zone development intensity stability index, zone geometric morphology rationality index, and zone spatial relationship independence index includes: The dispersion coefficient of grid development intensity within the identified urban development area is used as the stability index of the development intensity of the area. The convexity of the identified urban development area is used as an indicator of the rationality of the area's geometric shape. The minimum buffer overlap rate among the identified urban development zones is selected as the spatial relationship independence index of the zones; The identification results of the urban development area are detected based on the stability index of the development intensity of the area, the rationality index of the geometric shape of the area, and the independence index of the spatial relationship of the area, so as to obtain the identification rate detection result of the urban development area.

[0015] In one implementation, the usage efficiency detection based on a single usage efficiency indicator or a comprehensive usage efficiency indicator includes: The factors selected were resident population density, nighttime light brightness, and point of interest density. The ratio of factor level to development intensity level is used as the utilization efficiency index of each factor. The utilization efficiency of urban development areas corresponding to each factor is calculated according to the formula of single utilization efficiency index, and the single utilization efficiency test results are obtained. Alternatively, the factor with the highest usage rate among the three factors can be selected as the grid, and the comprehensive utilization efficiency of the grid can be calculated using the comprehensive utilization efficiency index formula to obtain the comprehensive utilization efficiency test result.

[0016] Secondly, the present invention provides a system for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, comprising: The data acquisition module is used to acquire urban development data and perform grid-based processing on the urban development data; The urban development zone identification module is used to identify urban development zones that meet certain conditions based on gridded urban development data and urban development zone identification methods. The efficiency detection module is used to detect the identification results of urban development areas based on the stability index of development intensity, the rationality index of geometric shape of the area, and the independence index of spatial relationship of the area, and to detect the use efficiency based on a single use efficiency index or a comprehensive use efficiency index. The output module is used to output the identification and utilization efficiency detection results of urban development areas.

[0017] Thirdly, the present invention provides a terminal, comprising: a processor and a memory, wherein the memory stores a program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, and the program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth is executed by the processor to implement the operation of the method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth as described in the first aspect.

[0018] Fourthly, the present invention also provides a computer-readable storage medium storing a program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth. When executed by a processor, the program is used to implement the operation of the method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth as described in the first aspect.

[0019] The present invention, by employing the above technical solution, has the following effects: This invention, through grid-based processing of urban development data, identifies eligible urban development zones using an urban development zone identification method, achieving adaptive boundary expansion and redundancy removal. It also detects the identification results based on zone development intensity stability indicators, zone geometric morphology rationality indicators, and zone spatial relationship independence indicators, and performs usage efficiency detection based on single or comprehensive usage efficiency indicators. This can be integrated with an efficiency-matching detection system, and spatial regression analysis reveals the influencing mechanisms, ultimately constructing a closed-loop decision support process of identification-diagnosis-optimization. By accurately identifying high-intensity development zones and their usage efficiency, this invention provides a scientific basis for urban density zoning control, urban renewal unit delineation, and public resource reallocation, supporting refined urban design optimization and governance decisions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth in this invention.

[0022] Figure 2 This is the intensity grid map developed in this invention.

[0023] Figure 3 This is the resident population density raster map in this invention.

[0024] Figure 4 This is the digital brightness grid diagram of nighttime lights in this invention.

[0025] Figure 5 This is the interest point density raster map in this invention.

[0026] Figure 6 This is the seed raster diagram in this invention.

[0027] Figure 7 This is a preliminary high-intensity development area map in this invention.

[0028] Figure 8 This is a high-intensity development area map in this invention.

[0029] Figure 9 This is the convexity diagram of the high-intensity development area in this invention.

[0030] Figure 10 This is the minimum buffer overlap rate diagram of the high-intensity development area in this invention.

[0031] Figure 11 This is a diagram showing the utilization efficiency of high-intensity development areas in this invention.

[0032] Figure 12 This is a functional schematic diagram of the terminal in one implementation of the present invention.

[0033] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0035] Exemplary methods While there is a wealth of research on the identification and analysis of high-intensity urban areas, the following shortcomings remain: (1) Existing recognition methods lack the accuracy and coherence of boundary recognition, fail to accurately identify features and grid neighborhood relationships, and are difficult to capture irregular shapes at the edges of areas. The algorithms lack interpretability and robustness.

[0036] (2) Research on the efficiency of land use in high-intensity development areas mainly focuses on the selection of different detection indicators, but there is no refined detection research that distinguishes different land use attributes.

[0037] Research on the identification and efficiency testing of high-intensity areas is relatively independent, lacking a systematic analytical framework that extends from identification to testing and then to optimization. Although existing studies have considered different land use attributes in efficiency testing, the methods used in the identification and testing stages are not closely integrated, making it difficult to form a complete decision support system.

[0038] To address the above-mentioned technical problems, this invention provides a method for identifying and detecting the utilization efficiency of high-intensity urban development areas based on grid growth. The method includes: acquiring urban development data and performing grid-based processing on the urban development data; identifying urban development areas that meet certain conditions using an urban development area identification method based on the grid-based urban development data; detecting the identification results of urban development areas based on area development intensity stability indicators, area geometric morphology rationality indicators, and area spatial relationship independence indicators, and detecting utilization efficiency based on a single utilization efficiency indicator or a comprehensive utilization efficiency indicator; and outputting the identification and utilization efficiency detection results of the urban development areas. This invention, by accurately identifying high-intensity development areas and their utilization efficiency, can provide a scientific basis for urban density zoning control, urban renewal unit delineation, and public resource reallocation, supporting refined urban design optimization and governance decisions.

[0039] like Figure 1 As shown, this embodiment of the invention provides a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, including the following steps: Step S100: Obtain urban development data and perform grid-based processing on the urban development data.

[0040] In this embodiment, the method is mainly used to identify high-intensity urban development areas and to detect the utilization efficiency of the identified high-intensity urban development areas. High-intensity urban development refers to a development model that concentrates capital, technology, and labor on limited land, achieving efficient land use by increasing building density, plot ratio, and population capacity. Its core characteristics include: high-density building clusters, high population concentration, and high industrial concentration. (1) Spatial intensification: characterized by high-rise buildings, underground space development, and three-dimensional transportation.

[0041] (2) Functional integration: a “complex” model that integrates residential, commercial, office and entertainment functions.

[0042] (3) Capital and technology intensive: Relying on high-investment infrastructure and construction technologies (prefabricated buildings, deep foundation pit support).

[0043] In this embodiment, the characteristics of high-intensity urban development, such as high building density, high population concentration, and high infrastructure load, are utilized. Development intensity is selected as the main factor for identifying high-intensity development areas, and population density and point of interest density are selected as the main factors for evaluating the utilization efficiency of high-intensity development areas. This is to realize a scheme for identifying high-intensity urban development areas and detecting their utilization efficiency.

[0044] Specifically, in one implementation of this embodiment, step S100 includes the following steps: Step S101: Obtain development intensity data, resident population density data, nighttime light digital brightness data, and point of interest density data to obtain the acquired urban development data; Step S102: Based on the preset grid size, the urban research area is divided into multiple grid units using administrative boundaries as a mask; Step S103: Convert the urban data coordinate system, associate the acquired urban development data with multiple grid units, and obtain the development intensity data, resident population density data, nighttime light brightness data, and point of interest density data corresponding to each grid unit.

[0045] In this embodiment, the selected factors for identifying high-intensity development areas in cities have the following characteristics: 1) Development intensity is a core planning indicator for measuring the proportion of regional construction space, mainly used in territorial spatial planning and intensive land use management. It is calculated as the percentage ratio of urban construction land area to the total area of ​​the administrative region, and a control system is constructed through indicators such as building density and plot ratio.

[0046] 2) Population density can be divided into two parts: static population density and dynamic population density. Static population density will be calculated using the resident population density. However, since there is no direct basis for calculating dynamic population density, and considering that the digital brightness of urban nighttime lights should have a high correlation with dynamic population, the digital brightness of nighttime lights will be used as the basis for calculating dynamic population density.

[0047] 3) Points of interest (POIs) are a commonly used term in geographic information systems and map services. They typically refer to a specific location on a map that represents a certain type of point that can attract people. POIs include various types such as buildings, shopping, dining, transportation, and healthcare. Since this embodiment will use POI density to measure infrastructure load, it will primarily select non-building entity POIs, specifically including: businesses, shopping, dining, entertainment, education, accommodation, services, and healthcare—a total of eight categories.

[0048] Regarding the aforementioned factors for identifying high-intensity urban development areas, the data obtained in this embodiment is as follows: (1) Development intensity: The development intensity data required in this embodiment comes from the Zenodo database, which contains building data in my country including outline vectors and heights. The data is calculated based on the assumption that the building floor height is 3 meters. The data time is 2020, the data size is 41GB, and the data coordinate system is WGS1984.

[0049] The building outline data comes from vector data of 280 million buildings in five East Asian countries published by Qian Shi et al. (building blocks extracted from Google Earth imagery at level 18 with a spatial resolution of 0.5 m from 2020 to 2022). Building height information was estimated using machine learning methods by integrating multi-source remote sensing features and building morphological features, extracting model input features from the multi-source dataset using the GEE platform, and then using machine learning methods. Therefore, the data accuracy is guaranteed.

[0050] (2) Resident population density: The resident population density data required for this embodiment comes from the 100-meter resolution population grid data of China shared on the figshare platform. The data time is 2020, and the data coordinate system is Albers_Conic_Equal_Area. Compared with the LandScan global population dataset, the data from my country's population census is more accurate and reliable in terms of the domestic population distribution, thus ensuring data accuracy.

[0051] (3) Digital brightness of nighttime lights: Global nighttime light data is derived from long-term nighttime illumination data studies similar to the NPP-VIIRS. The raw data provided on the official website includes two data formats: global nighttime light data from 2000 to 2018 and masked global nighttime light data from 2013 to 2023 (masking refers to removing light data from global water systems). This example uses data from 2020.

[0052] (4) Density of points of interest: The seven types of point-of-interest (POI) density data required in this embodiment are obtained from the API interface call of the POI search module of Gaode Map Open Platform. The data includes eight categories: enterprises, shopping, catering, entertainment, education, accommodation, services, and medical care. The data time is 2020, and the data coordinate system is gcj02.

[0053] The development intensity data, resident population density data, nighttime light brightness data, and point of interest density data obtained in this embodiment need to be further rasterized. The specific method is as follows: The grid size is determined based on research needs. Using administrative boundaries as a mask, and employing ArcGIS geographic information system software, the study area was rasterized according to a uniform raster size (specifically including boundary clipping, raster encoding, etc.) to divide the area into... grid unit ( Based on the conversion of the data coordinate system from I to WGS1984, the acquired data is correlated with the raster cells to obtain the development intensity, resident population density, nighttime light intensity, and POI density of each raster cell, which are expressed as follows: , , and .

[0054] While the phenomenon of high-intensity urban development is supported by theories such as central place theory and compact city theory at the macro level, and guided by planning theories such as building density control at the micro level, existing theories lack specific theoretical explanations for the formation mechanism and boundary determination of continuous high-intensity development areas at the meso-scale level. Therefore, this embodiment will draw on relevant urban spatial theories to analyze the formation mechanism and spatial characteristics of high-intensity development areas, and select appropriate identification methods accordingly.

[0055] The formation of high-intensity urban development zones follows the agglomeration and diffusion mechanism of growth pole theory, the economic logic of spatial agglomeration theory, the spatial organization law of urban morphology theory, and the structural evolution law of urban center system theory. Therefore, this embodiment will draw on these theoretical achievements and adopt the grid growth method to start from the high-intensity core. Through adjacency relationship judgment and spatial independence detection, relevant grids will be gradually incorporated to realize the growth of the zone, and finally the zone boundary with internal agglomeration and external independence characteristics will be determined.

[0056] like Figure 1 As shown, this embodiment of the invention provides a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, including the following steps: Step S200: Based on the gridded urban development data, identify urban development areas that meet the conditions using the urban development area identification method.

[0057] In this embodiment, based on gridded urban development data, urban development areas that meet the conditions are identified using an urban development area identification method; this embodiment selects the region growing algorithm in image recognition to identify high-intensity urban development areas.

[0058] The implementation process of the region growing algorithm is as follows: First, seed pixels are set, and then region growing is performed starting from these seed pixels. During the growing process, certain similarity criteria are used to determine whether the current pixel belongs to the current region. These criteria include grayscale similarity, color similarity, and texture similarity. If the current pixel belongs to the current region, it is merged into the current region; otherwise, the algorithm continues to explore surrounding pixels. The growing process ends when all pixels adjacent to the seed pixels have been merged into the current region. Simultaneously, this region becomes an independent set of pixels.

[0059] Considering that the regions obtained from the growth of multiple seeds may overlap, the region identification algorithm proposed in this embodiment includes two parts: high-intensity development region growth and high-intensity development region merging and segmentation.

[0060] Specifically, in one implementation of this embodiment, step S200 includes the following steps: Step S201: Based on the gridded urban development data, the urban development area identification method is used to perform development area growth identification to obtain the initially identified urban development areas.

[0061] In one implementation of this embodiment, step S201 includes the following steps: Step S2011: Select a seed grid based on the gridded urban development data; Step S2012: Starting from all seed grids, the grid with the highest development intensity among the neighboring grids of each development zone is taken as the growth grid. The development zone is grown based on the growth grid to obtain the initially identified urban development zone.

[0062] In this embodiment, the high-intensity development zone grows as follows: (1) Seed grid selection: The seed grid is the starting point for region growth, and its selection directly affects the growth effect. Generally, the closer the seed grid is to the center of the actual region, the better the effect. In hotspot analysis, Getis-Ord... *(A spatial statistical method for identifying spatial clustering phenomena) The statistical measure can precisely analyze the location where raster features cluster in space. Therefore, this embodiment selects the Getis-Ord of the raster. *Statistical measures are used as an indicator for seed grid selection. The specific calculation formula is as follows: ; ; in, Represents a grid Getis-Ord *Statistics; Represents a grid With grid Spatial weights between them; Represents a grid With grid The straight-line distance between them; Indicates the grid size is The average development intensity of all grids at that time; Indicates the grid size is The standard deviation of the development intensity of all grids at that time.

[0063] Considering that high-intensity development zones are usually few in number, this embodiment sets the seed raster selection criterion to all rasters. * The upper 5th percentile of the statistic is obtained. Seed grid ( ).

[0064] (2) Grid growth steps: From all seed grids respectively ( Starting from this point, a preliminary high-intensity development area was obtained. ( The specific growth steps are as follows: Step 1: Designate a specific area From seed raster Departure, that is ; Step 2: A specific area During the growth phase, the growth grid should be the grid with the highest development intensity among its neighboring grids at that phase.

[0065] Step 3: If adding this grid makes the area... If the average development intensity is greater than the initial intensity, then add it. Then proceed to the first step; otherwise, terminate the operation for that area. The growth of.

[0066] Specifically, in one implementation of this embodiment, step S200 further includes the following steps: Step S202: Merge or divide the initially identified urban development areas to obtain the urban development areas that meet the conditions.

[0067] In one implementation of this embodiment, step S202 includes the following steps: Step S2021: Select the two areas with the largest overlap from all the initially identified urban development areas; Step S2022: Merge or divide the two selected areas according to the merging and dividing conditions, and update the areas to obtain the urban development areas that meet the conditions.

[0068] In this embodiment, the high-intensity development zones are merged and divided in the following way: Because it is obtained through seed grid growth There may be overlaps between the initial high-intensity development zones, so it is necessary to merge or divide the overlapping zones.

[0069] (1) Merging conditions: Merging refers to combining two overlapping regions into a new region while deleting the original two regions. Considering that the initial regions are obtained from seed raster growth and the importance of overlapping areas to the regions, two regions will be merged if one of the following two conditions is met: (a) The seed grids of both regions are within the overlapping area; (b) The overlapping area is important for both areas (i.e., removing the overlapping area will reduce the average development intensity of the area).

[0070] (2) Segmentation conditions: Segmentation refers to removing the overlapping portion of two overlapping regions from one or both regions, while simultaneously deleting one or both original regions. Considering the importance of the initial overlapping area to a region, if the overlapping area is not important to a region (i.e., deleting the overlapping area would reduce the average development intensity of the region), the overlapping area is deleted from that region to obtain a new region. If the new region does not contain a seed raster, it is also deleted.

[0071] (3) Merging and splitting steps: The initial high-intensity development areas were respectively ( By merging and dividing, high-intensity development zones can be obtained. ( The specific steps for merging and splitting are as follows: Step 1: Select the two regions with the largest overlap from all current regions; Step 2: Merge or split the two regions based on the merge and split conditions, and update the regions. If there are still overlapping areas between the regions, proceed to step 1; otherwise, terminate.

[0072] Urban planning typically imposes certain requirements on the area of ​​a development zone. Considering that the high-intensity development zone in this embodiment is obtained through seed grid growth, two screening conditions are proposed: the zone area should contain at least one seed grid. (By deleting...) ( The areas that do not meet these two screening criteria are then selected as the final high-intensity development areas. ( ).

[0073] like Figure 1 As shown, this embodiment of the invention provides a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, including the following steps: Step S300: The urban development area identification results are detected based on the area development intensity stability index, area geometric shape rationality index, and area spatial relationship independence index, and the use efficiency is detected based on a single use efficiency index or a comprehensive use efficiency index.

[0074] In this embodiment, in order to comprehensively evaluate the accuracy and rationality of the high-intensity development areas identified based on the rasterization growth method, this study will introduce relevant indicators to detect the development intensity distribution, geometric shape and spatial relationship of the areas from multiple perspectives.

[0075] Specifically, in one implementation of this embodiment, step S300 includes the following steps: Step S301: The dispersion coefficient of the grid development intensity within the identified urban development area is used as the stability index of the development intensity of the area. Step S302: Use the convexity of the identified urban development area as an indicator of the rationality of the area's geometric shape; Step S303: Select the minimum buffer overlap rate among the identified urban development zones as the spatial relationship independence index of the zones; Step S304: Based on the stability index of the development intensity of the area, the rationality index of the geometric shape of the area, and the independence index of the spatial relationship of the area, the identification result of the urban development area is detected to obtain the identification rate detection result corresponding to the urban development area.

[0076] In this embodiment, the method for detecting the stability of the development intensity of the area is as follows: The stability and consistency of development intensity distribution within a region is a crucial basis for accurate region identification. Therefore, this embodiment introduces the dispersion coefficient of grid development intensity within a region as a detection index for the stability of region development intensity. The specific calculation formula is as follows: ; in, Indicates area The coefficient of variation of the development intensity of all grids within the area; ( Indicates area The variance of the development intensity of all grid cells within the grid; Indicates area The average development intensity of all grid cells within the cell. According to common standards, a value less than 0.3 indicates good stability.

[0077] The method for detecting the rationality of the geometric shape of the area is as follows: There are generally no specific shape requirements for the geometry of the region. Typically, as long as the boundaries are not excessively curved, elongated, or deeply recessed, it is acceptable. Therefore, this embodiment introduces the convexity of the region as an indicator of the reasonableness of its geometry. The convexity of the region is the ratio of its area to the area of ​​its minimum convex hull region. The minimum convex hull region can be efficiently obtained using the Graham scan algorithm, and the specific calculation formula is as follows: ; in, Indicates area convexity; area The minimum convex hull region. According to common standards, a value greater than 0.8 is considered reasonable.

[0078] The method for detecting the independence of spatial relationships within a region is as follows: The spatial independence and clustering pattern of areas are important indicators for accurate area identification. Common indicators for detecting independence include the centroid distance between areas and the minimum buffer zone overlap rate. Since the centroid distance is suitable for areas with regular shapes and small area differences, but high-intensity development areas do not meet the above conditions, this embodiment selects the minimum buffer zone overlap rate between areas as the indicator for detecting the spatial independence of areas. The specific calculation formula is as follows: ; ; in This indicates that when the buffer is defined as Each grid time zone Minimum buffer overlap ratio; This indicates that when the buffer is defined as Each grid time zone With the area Minimum buffer overlap ratio; Indicates the area of ​​the region; This indicates that when the buffer is defined as When expanding outwards from a grid area, each grid cell is used. The buffer area is obtained by dividing the grid into several grids. According to common standards, a value less than 0.2 indicates good independence.

[0079] In this embodiment, high-intensity development implies high costs, and its utilization efficiency directly relates to the economic development level and sustainable resource utilization of the area. Therefore, this embodiment will select three factors—resident population density, nighttime digital light brightness, and point-of-interest density—to detect the utilization efficiency of high-intensity development areas. First, the individual utilization efficiency indicators of the three factors at the grid and area levels will be described, and then a comprehensive utilization efficiency indicator for the grid and area levels will be given.

[0080] Specifically, in one implementation of this embodiment, step S300 further includes the following steps: Step S305: Select resident population density, nighttime light digital brightness, and point of interest density as factors; Step S306: The ratio of factor level to development intensity level is used as the utilization efficiency index of each factor, and the utilization efficiency of the urban development area corresponding to each factor is calculated according to the formula of single utilization efficiency index, so as to obtain the single utilization efficiency test result. Step S307: Select the factor with the highest usage rate among the three factors as the grid, calculate the comprehensive utilization efficiency of the grid using the comprehensive utilization efficiency index formula, and obtain the comprehensive utilization efficiency test result.

[0081] In this embodiment, the detection method for a single efficiency index is as follows: The utilization efficiency of a high-intensity development area is an indicator relative to the development intensity. Therefore, this embodiment proposes to use the ratio of the factor level to the development intensity level as the utilization efficiency index of that factor. The factor level reflects the relationship between the described area and the average situation. The specific calculation formulas for the individual utilization efficiency indices of the three factors are as follows: ; ; ; ; ; ; in , and Representing grids The resident population density level, nighttime digital light brightness level, and point of interest density utilization efficiency; , , and Representing the regions The development intensity level, the resident population density level, the digital brightness level of nighttime lights, and the density and utilization efficiency of points of interest; , , and These represent the overall average development intensity, resident population density, nighttime light intensity, and point of interest density, respectively.

[0082] The method for detecting comprehensive utilization efficiency indicators is as follows: Since the efficiency of different factors reflects different dimensions of population and industry agglomeration, this embodiment selects the three factors with the highest usage rates as the overall grid utilization efficiency index. The specific calculation formula is as follows: ; ; in, Represents a grid Overall utilization efficiency; Indicates area The overall utilization efficiency.

[0083] Referring to general confidence level standards and slightly relaxing the requirements, this embodiment considers that when the utilization efficiency is in the range of [0, 0.8), [0.8, 1.2], and (1.2, ..., ... () is inefficient, matched, and efficient.

[0084] like Figure 1 As shown, this embodiment of the invention provides a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, including the following steps: Step S400: Output the identification and utilization efficiency detection results of the urban development area.

[0085] In this embodiment, City A is selected as the case study object. On the one hand, relevant data of City A will be used to verify and analyze the research results of this embodiment. On the other hand, it is hoped that the research of this embodiment can provide a reference for the future urban planning of City A, especially the optimization of high-intensity development areas.

[0086] The total area of ​​City A is 1997.47 square kilometers. Considering both refined analysis and computational efficiency, this embodiment sets the grid size to 200 meters. Using ArcGIS software, City A was divided into 34,940 grid cells.

[0087] Using data acquisition sources and related methods, data on development intensity, resident population, nighttime light intensity, and points of interest in City A were collected and rasterized.

[0088] 1) Development intensity: The development intensity comprises 34,710 non-empty grid cells, with an average of 1.15 and a maximum of 17.89. The majority (approximately 85%) are concentrated between 0.5 and 3.0. Figure 2 As shown.

[0089] 2) Resident population density: The resident population density comprises 35,081 non-empty raster cells, with an average of 107.73 and a maximum of 359. The majority of these cells are concentrated between 30 and 160, accounting for approximately 50% of the total. (Specific details are as follows...) Figure 3 As shown.

[0090] 3) Digital brightness of nighttime lights: The nighttime light digital brightness consists of 89,547 non-empty grid cells, with an average value of 23.44 and a maximum value of 173.18. The majority of these cells are concentrated between 5 and 50, accounting for approximately 72% of the total. (Specific details are as follows...) Figure 4 As shown.

[0091] 4) Point of interest density: The interest point density consists of 46,738 non-empty raster cells, with an average of 103.13 and a maximum of 2,338. The majority of these cells are concentrated between 10 and 200, accounting for approximately 65% ​​of the total. (Specific details are as follows...) Figure 5 As shown.

[0092] High-intensity development zone identification 1) Regional growth: In this embodiment, a total of 1747 seed grids were screened. The development intensity of the seed grids was mainly concentrated between 3.5 and 6.0, with an average of 4.67, which is much higher than the average of 1.15. The extremely high values ​​greater than 8.0 accounted for less than 5%, as shown in the figure below. Figure 6 As shown.

[0093] 1747 initial zones were generated from 1747 seed grids following growth rules. The results showed a spatial distribution highly consistent with the urban core: zoned or clustered along rail transit corridors, main roads, and existing business districts; simultaneously, the growth radius was larger in high-density hotspots. The average plot ratio of the candidate zones generally fell between 3.0 and 6.5, with an average of approximately 4.23, slightly lower than the average level of the seed grids. This indicates that while maintaining a high-density background, this stage introduced more secondary zones with slightly lower edge carrying capacity but still high intensity; extremely high values ​​above 8.0 correspond to several ultra-high-density clusters primarily for business and research functions. Specifically... Figure 7 As shown.

[0094] 2) Merging and dividing areas: From 1,747 initial zones, through merging and subdivision, 212 high-intensity development zones were ultimately formed, accounting for 12.5% ​​of the total area. The largest of these zones is located in the Futian Central District, covering approximately 9.88 km². 2 .

[0095] Development Intensity Analysis: The average development intensity density peaks at around 2.8, meaning that the development intensity in most areas is evenly distributed near the upper limit of the planned floor area ratio; the highest reaches 8.3, corresponding to several large business or research areas, which are classified as ultra-high-density development. Areas with low intensity (below 2.5) account for approximately 15%, representing reserved areas or ecological buffer zones for the new district. (Specific details are as follows...) Figure 8 As shown.

[0096] Area analysis: Over 80% of the areas are less than 1 km² 2 This indicates that high-intensity development units exhibit a clustered pattern of numerous small units, which is conducive to maintaining functional compactness within the area and convenient access to public services. Areas exceeding 1.5 km² 2 Medium-to-large scale areas account for less than 10%, concentrated only in a few core areas. These few super-large areas bear the important responsibility of providing regional services and transportation hubs in urban renewal or special planning, and need to strengthen internal roads, green spaces and functional zoning during planning to avoid unbalanced supporting facilities.

[0097] 3) Recognition result detection: Geometric morphology rationality test: The rationality of the geometric morphology of the area was tested based on convexity. The results showed that the geometric morphology of the area was very reasonable, with 96% of the areas having a convexity greater than 0.9. Only two areas had a convexity less than 0.8, but due to the large area and the absence of excessive deformation, the results were within acceptable limits. (Specific details are as follows...) Figure 9 As shown.

[0098] Spatial Relationship Independence Detection: The spatial relationship independence of the regions was detected based on the minimum buffer overlap rate. The results showed that the spatial relationship independence of the regions was very strong, with only 19 pairs of polygons having a minimum buffer overlap rate greater than 0.2, which is less than 0.1%. (Details omitted for brevity.) Figure 10 As shown.

[0099] 4) Utilization Efficiency Test: The utilization efficiency of each area was calculated. The results showed that the proportions of inefficient, balanced, and efficient utilization were 42.2%, 55.7%, and 2.1%, respectively. The utilization efficiency distribution was concentrated between 0.5 and 0.8, exhibiting a slightly right-skewed normal distribution. Overall, the utilization efficiency was low. (Specific details are as follows...) Figure 11As shown, there are numerous vacant and underutilized areas on the periphery of the central area and in non-central areas, failing to attract population activity commensurate with the level of development. The data for the inefficient group are scattered, with some areas exhibiting extremely low utilization efficiency and serious problems in land use and population attraction, while the values ​​for the efficient group are generally high, indicating an uneven distribution of land use.

[0100] This method ensures boundary consistency and possesses data fusion capabilities, effectively enabling precise positioning and resource allocation optimization in high-intensity development areas. Taking City A as an example, the effectiveness and practicality of the method were verified through the identification of high-intensity development areas and the matching of population and industrial agglomeration.

[0101] This embodiment achieves the following technical effects through the above technical solution: This embodiment, through grid-based processing of urban development data, can identify eligible urban development zones using urban development zone identification methods, achieving adaptive boundary expansion and redundancy removal. Furthermore, it can detect the identification results of urban development zones based on zone development intensity stability indicators, zone geometric morphology rationality indicators, and zone spatial relationship independence indicators. It can also detect usage efficiency based on single or comprehensive usage efficiency indicators, connecting to an efficiency-matching detection system. Spatial regression analysis reveals the influencing mechanisms, ultimately constructing a closed-loop decision support process of identification-diagnosis-optimization. By accurately identifying high-intensity development zones and their usage efficiency, this embodiment can provide a scientific basis for urban density zoning control, urban renewal unit delineation, and public resource reallocation, supporting refined urban design optimization and governance decisions.

[0102] Exemplary device Based on the above embodiments, the present invention also provides a system for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, comprising: The data acquisition module is used to acquire urban development data and perform grid-based processing on the urban development data; The urban development zone identification module is used to identify urban development zones that meet certain conditions based on gridded urban development data and urban development zone identification methods. The efficiency detection module is used to detect the identification results of urban development areas based on the stability index of development intensity, the rationality index of geometric shape of the area, and the independence index of spatial relationship of the area, and to detect the use efficiency based on a single use efficiency index or a comprehensive use efficiency index. The output module is used to output the identification and utilization efficiency detection results of urban development areas.

[0103] This embodiment achieves the following technical effects through the above technical solution: This embodiment, through grid-based processing of urban development data, can identify eligible urban development zones using urban development zone identification methods, achieving adaptive boundary expansion and redundancy removal. Furthermore, it can detect the identification results of urban development zones based on zone development intensity stability indicators, zone geometric morphology rationality indicators, and zone spatial relationship independence indicators. It can also detect usage efficiency based on single or comprehensive usage efficiency indicators, connecting to an efficiency-matching detection system. Spatial regression analysis reveals the influencing mechanisms, ultimately constructing a closed-loop decision support process of identification-diagnosis-optimization. By accurately identifying high-intensity development zones and their usage efficiency, this embodiment can provide a scientific basis for urban density zoning control, urban renewal unit delineation, and public resource reallocation, supporting refined urban design optimization and governance decisions.

[0104] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 12 As shown.

[0105] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected via a system bus; wherein, the processor of the terminal provides computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and internal memory; the computer-readable storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display relevant information; and the communication module is used to communicate with a cloud server or other devices.

[0106] When executed by a processor, this computer program is used to implement a method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth.

[0107] It will be understood by those skilled in the art that Figure 12 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In one embodiment, a terminal is provided, comprising: a processor and a memory, the memory storing a program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, the program being executed by the processor to implement the operation of the above-described method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth.

[0109] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a grid-grown urban high-intensity development area identification and utilization efficiency detection program, which, when executed by a processor, is used to implement the above-described grid-grown urban high-intensity development area identification and utilization efficiency detection method.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include both non-volatile and volatile memory.

[0111] In summary, this invention provides a method for identifying high-intensity urban development areas and detecting their utilization efficiency based on grid growth. The method includes: acquiring urban development data and performing grid-based processing on the data; identifying eligible urban development areas using an urban development area identification method based on the grid-based data; detecting the identification results based on area development intensity stability indicators, area geometric morphology rationality indicators, and area spatial relationship independence indicators, and detecting utilization efficiency based on a single or comprehensive utilization efficiency indicator; and outputting the identification and utilization efficiency detection results of the urban development areas. This invention, by accurately identifying high-intensity development areas and their utilization efficiency, can provide a scientific basis for urban density zoning control, urban renewal unit delineation, and public resource reallocation, supporting refined urban design optimization and governance decisions.

[0112] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, characterized in that, include: Acquire urban development data and perform grid-based processing on the urban development data; Based on gridded urban development data, urban development zones that meet the criteria are identified using urban development zone identification methods. The identification results of urban development areas are tested based on the stability index of development intensity, the rationality index of geometric shape of the area, and the independence index of spatial relationship of the area. The use efficiency is tested based on a single use efficiency index or a comprehensive use efficiency index. Output the identification and utilization efficiency test results of urban development areas.

2. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 1, characterized in that, The process of acquiring urban development data and performing grid-based processing on the urban development data includes: The urban development data is obtained by acquiring development intensity data, resident population density data, nighttime light brightness data, and point of interest density data. Based on the preset grid size, the urban research area is divided into multiple grid units using administrative boundaries as a mask; The urban data coordinate system is transformed, and the acquired urban development data is associated with multiple grid units to obtain development intensity data, resident population density data, nighttime light brightness data, and point of interest density data corresponding to each grid unit.

3. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 1, characterized in that, The step of identifying eligible urban development areas based on gridded urban development data using an urban development area identification method includes: Based on the gridded urban development data, the urban development area identification method is used to identify the growth of development areas and obtain the initially identified urban development areas. The initially identified urban development zones are merged or divided to obtain the urban development zones that meet the conditions.

4. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 3, characterized in that, The process involves using the gridded urban development data and the urban development area identification method to perform development area growth identification, resulting in preliminarily identified urban development areas, including: Select a seed grid based on the gridded urban development data; Starting from all seed graticles, the graticle with the highest development intensity among the neighboring graticles of each development zone is taken as the growth graticle. The development zone is grown based on the growth graticle to obtain the initially identified urban development zone.

5. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 3, characterized in that, The process of merging or dividing the initially identified urban development zones to obtain the urban development zones that meet the conditions includes: Select the two areas with the largest overlap from all the initially identified urban development zones; Based on the merging and splitting conditions, the two selected areas are merged or split, and the areas are updated to obtain the urban development areas that meet the conditions.

6. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 1, characterized in that, The method for detecting urban development area identification results based on area development intensity stability index, area geometric morphology rationality index, and area spatial relationship independence index includes: The dispersion coefficient of grid development intensity within the identified urban development area is used as the stability index of the development intensity of the area. The convexity of the identified urban development area is used as an indicator of the rationality of the area's geometric shape. The minimum buffer overlap rate among the identified urban development zones is selected as the spatial relationship independence index of the zones; The identification results of the urban development area are detected based on the stability index of the development intensity of the area, the rationality index of the geometric shape of the area, and the independence index of the spatial relationship of the area, so as to obtain the identification rate detection result of the urban development area.

7. The method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth according to claim 1, characterized in that, The usage efficiency detection based on a single usage efficiency indicator or a comprehensive usage efficiency indicator includes: The factors selected were resident population density, nighttime light brightness, and point of interest density. The ratio of factor level to development intensity level is used as the utilization efficiency index of each factor. The utilization efficiency of urban development areas corresponding to each factor is calculated according to the formula of single utilization efficiency index, and the single utilization efficiency test results are obtained. Alternatively, the factor with the highest usage rate among the three factors can be selected as the grid, and the comprehensive utilization efficiency of the grid can be calculated using the comprehensive utilization efficiency index formula to obtain the comprehensive utilization efficiency test result.

8. A system for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, characterized in that, include: The data acquisition module is used to acquire urban development data and perform grid-based processing on the urban development data; The urban development zone identification module is used to identify urban development zones that meet certain conditions based on gridded urban development data and urban development zone identification methods. The efficiency detection module is used to detect the identification results of urban development areas based on the stability index of development intensity, the rationality index of geometric shape of the area, and the independence index of spatial relationship of the area, and to detect the use efficiency based on a single use efficiency index or a comprehensive use efficiency index. The output module is used to output the identification and utilization efficiency detection results of urban development areas.

9. A terminal, characterized in that, include: The processor and memory, wherein the memory stores a program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth, and the program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth is executed by the processor to implement the operation of the method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth. When executed by a processor, the program is used to implement the operation of the method for identifying and detecting the utilization efficiency of urban high-intensity development areas based on grid growth as described in any one of claims 1-7.

Images