Urban vitality measurement method and system based on multi-scale data

Abstract

The invention discloses an urban vitality measurement method and system based on multi-scale data, and belongs to the technical field of urban planning and geographic information systems. The method comprises the steps of collecting multi-source data information in a research area, wherein the multi-source data information comprises remote sensing data, space-time positioning data and statistical data; constructing an analysis framework of three scales of villages and towns, communities and grids; establishing an urban vitality characterization index system based on three dimensions of population, economy and function, and respectively calculating a population index, an economic index and a function index; carrying out dimensionless processing and comprehensive evaluation on multiple indexes by adopting a spatial distance model, and measuring the urban vitality index; and identifying spatial aggregation characteristics and distribution modes of urban vitality through a spatial autocorrelation analysis method. The method can objectively quantify the spatial distribution characteristics of the urban vitality, provides a scientific basis for urban planning and updating decision making, and has the advantages of high precision, strong applicability and good operability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of urban planning and geographic information system, and specifically relates to a city vitality measurement method and system based on multi-scale data. BACKGROUND

[0002] With the continuous advancement of urbanization, city vitality has become an important indicator to measure the quality and sustainability of urban development. City vitality reflects the degree of human activity aggregation and the active degree of economic and social development in urban space, which has important guiding significance for urban planning, urban renewal and policy making.

[0003] The current city vitality evaluation method mainly has the following problems: first, the traditional evaluation method depends on statistical data, which is poor in timeliness and lacks spatial precision; second, most existing methods are based on single scale analysis, which is difficult to fully reflect the multi-scale characteristics of city vitality; third, the index system is not perfect, and subjective weight determination method is mostly used, which lacks objectivity; fourth, there is a lack of effective spatial analysis method, which cannot accurately identify the spatial distribution pattern and aggregation characteristics of city vitality.

[0004] Therefore, it is urgent to develop a multi-scale city vitality measurement method based on multi-source data fusion, which can objectively, accurately and timely evaluate the spatial distribution characteristics of city vitality, and provide scientific basis for urban planning and management decision-making. SUMMARY

[0005] In view of the deficiencies of the prior art, the purpose of the present application is to provide a city vitality measurement method and system based on multi-scale data, which solves the problems of single data source, single scale, strong subjectivity and insufficient spatial analysis in the existing city vitality evaluation method.

[0006] The purpose of the present application can be realized by the following technical solutions:

[0007] Firstly, the present application proposes a city vitality measurement method based on multi-scale data, which includes the key steps of multi-source data collection and preprocessing, multi-scale spatial analysis framework construction, city vitality index system construction, spatial distance model comprehensive evaluation and spatial autocorrelation analysis. The method integrates remote sensing data, spatio-temporal positioning data, statistical data and other multi-source information, constructs the analysis framework of township, community and grid three spatial scales, establishes a systematic index system based on population, economy and function three dimensions, uses objective spatial distance model for comprehensive evaluation, and uses spatial autocorrelation analysis to identify the spatial aggregation characteristics and distribution pattern of city vitality. Specifically, the following steps are included:

[0008] S1, collecting remote sensing data, spatio-temporal positioning data and statistical data in the study area; and performing coordinate unification, format conversion and quality inspection on the collected data;

[0009] S2. Based on the administrative boundaries and geographical characteristics of the study area, establish an analytical framework with three spatial scales: township, community, and grid; unify all data into the corresponding spatial scale units;

[0010] S3. Construct an urban vitality representation index system based on three dimensions: population, economy, and function, and calculate the population index, economic index, and functional index respectively.

[0011] S4. A spatial distance model is used to perform dimensionless processing on various indicators, establish an n-dimensional indicator space, and determine the minimum value point, that is, the point where all dimensions are at their minimum value; calculate the Euclidean distance from each research unit to the minimum value point as the comprehensive evaluation value; use the calculation method of the comprehensive evaluation value to calculate the population index PI, economic index EI, and functional index FI respectively, and calculate the comprehensive urban vitality index of each spatial unit after standardizing the three indices respectively.

[0012] S5. Identify the spatial clustering characteristics and distribution patterns of urban vitality through global and local spatial autocorrelation analysis.

[0013] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the specific steps of S1 are as follows:

[0014] S11, Population Data Collection: Collect WorldPop population density and population data, calculate population increase data by subtracting population data from different years; collect census data, and obtain detailed information such as population density by administrative division.

[0015] S12, Economic Data Collection: Based on official GDP statistics, combined with factors such as land use type, building density, nighttime light intensity, and residential distribution, GDP spatialization is achieved through multi-factor weight allocation and spatial interpolation methods to generate high-resolution GDP raster data.

[0016] S13, Functional Data Collection: Collect POI data from multiple platforms such as Gaode Map and Baidu Map, and classify and organize them according to categories such as commercial services, public services, leisure and entertainment, and transportation facilities; obtain road network vector data and calculate traffic convenience indicators such as road density and accessibility;

[0017] S14, Perform coordinate system transformation, projection transformation and format standardization on all data;

[0018] S15. Establish a data quality inspection system to remove outliers and missing values, ensuring data integrity and accuracy.

[0019] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the specific steps of S2 are as follows:

[0020] S21, Establishment of Township-Scale Framework: Based on administrative divisions, the study area is divided into township-level spatial units, and various indicator data within each township are statistically analyzed.

[0021] S22, Establishment of a community-scale framework: Based on community boundaries, the study area is subdivided into community-level spatial units, and various indicator data within each community are calculated;

[0022] S23, Establishing the raster scale framework: Establish a regular raster network, and set the raster size to 100m×100m, 500m×500m or 1000m×1000m according to the analysis accuracy requirements, and resample all data into the raster cells.

[0023] S24, Multi-scale data fusion: Establish correspondences between multi-scale data to ensure consistency and comparability of data at different scales.

[0024] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the specific steps of S3 are as follows:

[0025] S31, Population Index Calculation: Includes population size index and population attraction index; population size is calculated by population density within the unit, and population attraction is calculated by annual population increase within the unit.

[0026] S32, Economic Index Calculation: Includes basic economic indicators and nighttime economic indicators; basic economic indicators are calculated based on the total GDP within the unit, and nighttime economic indicators are calculated based on the ratio of nighttime light intensity to area within the unit.

[0027] S33, Functional Index Calculation: Includes leisure and entertainment index and transportation convenience index; leisure and entertainment index is calculated by the density of POI in the third space within the unit, and transportation convenience index is calculated by the density of the road network within the unit.

[0028] The third space POI includes service facilities such as shopping, dining, entertainment, and culture.

[0029] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the population index is calculated using the following formula:

[0030] Population size index = population within the unit / unit area; Population attraction index = (current year's population - base year's population) / unit area;

[0031] The population index PI is obtained by comprehensively calculating the population size index and the population attraction index using a spatial distance model.

[0032] The formula for calculating the economic index is as follows:

[0033] Economic Foundation Index = Total GDP within the unit / Unit area; Nighttime Economy Index = Total Nighttime Light Intensity within the unit / Unit area;

[0034] The Economic Index (EI) is obtained by comprehensively calculating the basic economic index and the nighttime economy index using a spatial distance model.

[0035] The formula for calculating the functional index is as follows:

[0036] Leisure and Recreation Index = Number of Points of Interest (POIs) in the Third Space within the Unit / Unit Area; Transportation Convenience Index = Total Road Length within the Unit / Unit Area;

[0037] The Function Index FI is obtained by comprehensively calculating the Leisure and Entertainment Index and the Transportation Convenience Index using a spatial distance model.

[0038] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the specific steps of S4 are as follows:

[0039] S41, Dimensionless Processing of Indicators: Dimensionless processing is performed on each indicator using the following formula:

[0040] ,

[0041] in, For the original data of cell i under index j, The data is dimensionless;

[0042] S42, establish an n-dimensional index space and determine the minimum value point, that is, the point where all dimensions are at their minimum values;

[0043] S43, Calculate the Euclidean distance from each research unit to the lowest value point as the comprehensive evaluation value:

[0044] ,

[0045] in, This is the comprehensive evaluation value for the i-th unit. Let be the dimensionless value of the j-th indicator in the i-th unit, and n be the number of indicators. Let j be the minimum value of the j indicators;

[0046] The comprehensive evaluation values ​​calculated from population data, economic data, and functional data are respectively represented as the population index PI, economic index EI, and functional index FI.

[0047] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the calculation steps of the comprehensive urban vitality index in step S4 are as follows:

[0048] Calculate the population index (PI), economic index (EI), and functional index (FI) separately.

[0049] The three indicators were standardized.

[0050] Calculate the comprehensive urban vitality index VI:

[0051] ,

[0052] in, Let i be the urban vitality index of the i-th unit. Let be the population index of the i-th unit. Let i be the economic index of the i-th unit. Let i be the functional index of the i-th unit. The minimum population index for the i-th unit. The minimum value of the economic index for the i-th unit. This is the minimum value of the functional index of the i-th unit.

[0053] Furthermore, in the urban vitality measurement method based on multi-scale data provided by this invention, the specific steps of the spatial autocorrelation analysis in step S5 are as follows:

[0054] S51, perform global spatial autocorrelation analysis;

[0055] The formula for calculating Moran's I exponent is:

[0056] ,

[0057] in, Moran's I index, The number of research units, and This represents the urban vitality index of adjacent units. This represents the average value of the city's vitality index. This is a spatial weight matrix based on adjacency relationships;

[0058] S52, perform local spatial autocorrelation analysis;

[0059] The LISA index is calculated using the following formula:

[0060] ,

[0061] in, For unit Local spatial autocorrelation index;

[0062] S53. Based on the LISA analysis results, the research units were divided into four clustering types: High-High, Low-Low, Low-High, and High-Low.

[0063] Furthermore, the urban vitality measurement method based on multi-scale data provided by this invention also includes a visualization step:

[0064] S61, Hierarchical Visualization: The urban vitality index is divided into 5 levels using the natural breakpoint method, equal interval method, or quantile method, and spatial visualization is performed through a GIS platform;

[0065] S62, Multi-scale comparative analysis: Generate urban vitality distribution maps, spatial clustering pattern maps and trend maps at different scales, and analyze the differences and correlations in vitality distribution at different scales;

[0066] S63, Thematic Map Production: Create thematic maps of population vitality, economic vitality, and functional vitality, and analyze the spatial distribution characteristics of vitality in each dimension;

[0067] S64, Statistical Analysis Report: Outputs a city vitality statistical report, including vitality distribution characteristics and spatial clustering at various scales, and generates a vitality distribution histogram, scatter plot and correlation matrix;

[0068] S65, Decision Recommendation Generation: Based on the results of vitality analysis, identify areas with potential for vitality enhancement and areas at risk of vitality decline, and propose differentiated planning recommendations and policy measures for different types of areas.

[0069] Secondly, this invention designs an integrated urban vitality measurement system, including...

[0070] The multi-source data acquisition and preprocessing unit is used to collect remote sensing data, spatiotemporal positioning data, and statistical data within the study area; and to perform coordinate unification, format conversion, and quality inspection on the collected data.

[0071] The multi-scale analysis framework is established based on the administrative boundaries and geographical characteristics of the study area, creating an analysis framework at three spatial scales: township, community, and raster. All data are then unified into the corresponding spatial scale units.

[0072] The Urban Vitality Index System Construction Unit is used to construct an urban vitality representation index system based on three dimensions: population, economy, and function, and to calculate the population index, economic index, and functional index respectively.

[0073] The spatial distance model is used to perform dimensionless processing on various indicators and calculate the comprehensive urban vitality index of each spatial unit.

[0074] The spatial autocorrelation analysis unit is used to identify the spatial clustering characteristics and distribution patterns of urban vitality through global and local spatial autocorrelation analysis.

[0075] The system adopts a modular design, has good scalability and compatibility, can support the access and processing of multiple data sources, provides rich analysis functions and visualization, and provides users with convenient and professional analysis tools.

[0076] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, it implements the steps of the method described in the present invention.

[0077] The present invention employs the above-mentioned technical means and, compared with the prior art, has the following technical effects:

[0078] 1. This invention combines remote sensing data, spatiotemporal positioning data, and statistical data to improve the timeliness and spatial accuracy of the data; at the same time, it establishes an analytical framework at three scales: township, community, and grid, which can comprehensively reflect the scale effect of urban vitality.

[0079] 2. This invention adopts a spatial distance model, which avoids the problem of subjective weight determination and improves the objectivity of the evaluation results; through spatial autocorrelation analysis, it accurately identifies the spatial distribution patterns and clustering characteristics of urban vitality.

[0080] 3. This invention integrates data processing, analysis and calculation, visualization and decision support functions, which improves the practicality of the method; the method is highly operable and can be widely applied to the vitality evaluation and planning decision-making of different cities, and has system integration and wide applicability. Attached Figure Description

[0081] 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 are briefly introduced below. For those skilled in the art, the drawings can be obtained without creative effort, and other drawings can also be obtained based on these drawings.

[0082] Figure 1 This is a schematic diagram of the overall process of the method of the present invention.

[0083] Figure 2 This is the community-scale vitality measurement result of an embodiment of the present invention. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] Example 1: As Figure 1 As shown in Example 1, this method for measuring urban vitality based on multi-scale data includes the following steps:

[0086] S1, Multi-source data acquisition and preprocessing

[0087] S11: Collect population density and population data from WorldPop in 2020, and subtract the 2010 population data from the 2020 population data to obtain the population increase data. Simultaneously, collect data from the Seventh National Population Census to obtain population data statistically analyzed by administrative division.

[0088] S12, based on official GDP data of Nanjing, comprehensively considers factors such as land use type, nighttime light intensity, and residential areas, and achieves GDP spatialization through multi-factor weight allocation to obtain GDP raster data.

[0089] S13 collects POI data from Gaode Maps in 2020. Through operations such as correction, deduplication, and spatial matching, the POI data is overlaid with administrative boundary vector maps or rasters. The information entropy of each unit is calculated, and the ratio of the number of POIs in the third space to their area is statistically analyzed.

[0090] S14. Apply WGS84 coordinate system to all data and use an appropriate projection method to ensure data spatial consistency.

[0091] S2, Establishment of a Multi-Scale Analysis Framework

[0092] S21, Township Scale: Nanjing City is divided into 130 township-level spatial units, and population, economic, and functional indicators within each township are statistically analyzed.

[0093] S22, Community Scale: Nanjing City is subdivided into 1519 community-level spatial units, and statistical values ​​of indicators within each community are calculated.

[0094] S23, Grid Scale: Establish a 100m×100m regular grid and resample all data into grid cells to obtain refined spatial distribution information.

[0095] S3, Construction of Urban Vitality Indicator System

[0096] S31, Population Index Calculation:

[0097] Population size index = Number of people in the unit / Area of ​​the unit;

[0098] Population attraction index = (2020 population - 2010 population) / unit area;

[0099] The population index PI is calculated using a spatial distance model.

[0100] S32, Economic Index Calculation:

[0101] Economic Foundation Index = Total GDP within the unit / Area of ​​the unit;

[0102] Nighttime Economy Index = Total Nighttime Light Intensity within the Unit / Unit Area;

[0103] The economic index EI is calculated using a spatial distance model.

[0104] S33, Function Index Calculation:

[0105] Leisure and Entertainment Index = Number of Points of Interest (POIs) in the third space within the unit / Unit area;

[0106] Transportation convenience index = Total length of roads within the unit / Unit area;

[0107] The functional index FI is obtained by comprehensive calculation using a spatial distance model.

[0108] S4, Comprehensive Evaluation of Spatial Distance Model

[0109] S41, dimensionless processing of all indicators:

[0110] ,

[0111] S42, Establish a three-dimensional index space and determine the minimum value point ( , , );

[0112] S43, Calculate the comprehensive urban vitality index for each spatial unit:

[0113] .

[0114] like Figure 2 As shown, taking Nanjing's community scale as an example, the city vitality index exhibits a power-law distribution and a significant clustering effect.

[0115] S5, Spatial Autocorrelation Analysis

[0116] S51, Calculate the global Moran's I exponent:

[0117] ,

[0118] Taking the township scale in Nanjing as an example, Moran's I value is 0.812, which passed the significance test of 0.05, indicating that the urban vitality index of the township scale unit has spatial autocorrelation characteristics.

[0119] S52, Calculate the local LISA index:

[0120] ,

[0121] Based on the LISA analysis results, the townships were divided into four clustering types: High-High (27), Low-Low (37), Low-High (0), and High-Low (0).

[0122] S6, Results Visualization and Analysis

[0123] The urban vitality index was divided into 5 levels using the natural breakpoint method. Spatial visualization was performed using the ArcGIS platform to generate urban vitality distribution maps of Nanjing at different scales. The results show:

[0124] 1. Township scale: Urban vitality exhibits a clear center-periphery gradient distribution, with the core area accounting for more than 65% of the total vitality concentration;

[0125] 2. Community scale: A superstar community effect emerged, with the most active community exceeding the maximum value of a township by 1.86 times;

[0126] 3. Grid scale: Reveals the micro-spatial pattern of vitality distribution, providing detailed spatial guidance for precise planning.

[0127] Example 2: This example proposes a city vitality measurement system based on multi-scale data, including:

[0128] The multi-source data acquisition and preprocessing unit is used to collect remote sensing data, spatiotemporal positioning data, and statistical data within the study area; and to perform coordinate unification, format conversion, and quality inspection on the collected data.

[0129] The multi-scale analysis framework is established based on the administrative boundaries and geographical characteristics of the study area, creating an analysis framework at three spatial scales: township, community, and raster. All data are then unified into the corresponding spatial scale units.

[0130] The Urban Vitality Index System Construction Unit is used to construct an urban vitality representation index system based on three dimensions: population, economy, and function, and to calculate the population index, economic index, and functional index respectively.

[0131] The spatial distance model is used to perform dimensionless processing on various indicators and calculate the comprehensive urban vitality index of each spatial unit.

[0132] The spatial autocorrelation analysis unit is used to identify the spatial clustering characteristics and distribution patterns of urban vitality through global and local spatial autocorrelation analysis.

[0133] Spatial autocorrelation analysis reveals a significant scale-dependent characteristic of urban vitality. At the township level, urban vitality follows a clear center-periphery gradient distribution, exhibiting a power-law distribution (α=2.34, R²=0.892), with the core area accounting for over 65% of the total vitality concentration. At the community level, spatial heterogeneity of vitality distribution increases significantly (CV increases from 4.65 to 8.79), revealing a superstar community effect, with the highest-vitality community exceeding the township maximum by 1.86 times. The grid scale further reveals the micro-spatial pattern of vitality distribution, providing detailed spatial guidance for precise urban renewal. Urban vitality at different scales exhibits a spatial distribution pattern of "large-scale dispersion and small-scale clustering," demonstrating significant regional differentiation. Areas with higher urban vitality are concentrated in the city's core area and scattered in surrounding areas. The analysis results provide an actionable solution for evidence-based urban renewal strategies, enabling planners to identify the optimal intervention areas where population challenges and vitality deficiencies intertwine, thereby maximizing resource allocation efficiency.

[0134] Example 3: This example proposes an electronic system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0135] It should be noted that the processing flow of embodiments 2-3 corresponds to the specific steps of the method provided in embodiment 1 of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment can be found in the method provided in embodiment 1 of the present invention, and will not be repeated here. The program code used to implement the method of this application can be written in any combination of one or more programming languages. This program code can be provided to the processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or server.

[0136] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0137] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. The present invention is not limited to the above embodiments; the embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for measuring urban vitality based on multi-scale data, characterized in that, Includes the following steps: S1. Collect remote sensing data, spatiotemporal positioning data and statistical data within the study area, and preprocess the collected data, including: coordinate unification, format conversion and quality inspection; S2. Based on the administrative boundaries and geographical characteristics of the study area, establish an analytical framework with three spatial scales: township, community, and grid; unify the data processed in step S1 into the corresponding spatial scale units; S3. Construct an urban vitality representation index system based on three dimensions: population, economy, and function, and calculate the population index, economic index, and functional index respectively. S4. A spatial distance model is used to perform dimensionless processing on each indicator, establishing an n-dimensional indicator space and determining the minimum value point; the Euclidean distance from each research unit to the minimum value point is calculated as the comprehensive evaluation value. ， in, This is the comprehensive evaluation value for the i-th unit. Let be the dimensionless value of the j-th indicator in the i-th unit, and n be the number of indicators. Let j be the minimum value of the j indicators; The population index (PI), economic index (EI), and functional index (FI) are calculated using a comprehensive evaluation value method. After standardizing the three indices, the comprehensive urban vitality index of each spatial unit is calculated. S5. Identify the spatial clustering characteristics and distribution patterns of urban vitality through global and local spatial autocorrelation analysis.

2. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The specific steps of S1 are as follows: S11. Population Data Collection: Collect WorldPop population density data and population size data, and calculate population increment data by subtracting population size data from different years. Collect census data and obtain detailed information such as population density by administrative division; S12. Economic Data Collection: Based on official GDP statistics, combined with various factors including land use type, building density, nighttime light intensity, and residential distribution, GDP spatialization is achieved through multi-factor weighting and spatial interpolation methods to generate high-resolution GDP raster data. S13. Functional Data Acquisition: Collect POI data from multiple platforms and classify and organize it to obtain road network vector data and calculate traffic convenience indicators including road density and accessibility. S14. Perform coordinate system transformation, projection transformation and format standardization on all data; S15. Perform data quality checks and remove outliers and missing values.

3. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The specific steps of S2 are as follows: S21. Establishment of Township-Scale Framework: Based on administrative divisions, the study area is divided into township-level spatial units, and various indicator data within each township are statistically analyzed. S22. Establishment of a community-scale framework: Based on community boundaries, the study area is subdivided into community-level spatial units, and various indicator data within each community are calculated. S23. Establishing a grid scale framework: Establish a regular grid, and set the grid size to 100m×100m, 500m×500m or 1000m×1000m according to the analysis accuracy requirements. Resample all data into the grid cells. S24. Multi-scale data fusion: Establish correspondences between multi-scale data to ensure consistency and comparability of data at different scales.

4. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The specific steps of S3 are as follows: S31. Population Index Calculation: This includes population size index and population attraction index; the population size index is calculated by the population density within the unit, and the population attraction index is calculated by the annual population increase within the unit. S32. Calculation of economic indexes: including basic economic indicators and nighttime economic indicators; basic economic indicators are calculated based on the total GDP within the unit, and nighttime economic indicators are calculated based on the ratio of nighttime light intensity to area within the unit. S33. Functional Index Calculation: This includes leisure and entertainment indicators and transportation convenience indicators; leisure and entertainment indicators are calculated based on the density of POIs in the third space within the unit, and transportation convenience indicators are calculated based on the density of the road network within the unit. The third space POI includes various service facilities such as shopping, dining, entertainment, and culture.

5. The urban vitality measurement method based on multi-scale data according to claim 4, characterized in that, (1) The formula for calculating the population index is: Population size index = population within the unit / unit area; Population attraction index = (current year's population - base year's population) / unit area; The population index PI is obtained by comprehensively calculating the population size index and the population attraction index using a spatial distance model. (2) The formula for calculating the economic index is: Economic Foundation Index = Total GDP within the unit / Unit area; Nighttime Economy Index = Total Nighttime Light Intensity within the unit / Unit area; The Economic Index (EI) is obtained by comprehensively calculating the basic economic index and the nighttime economy index using a spatial distance model. (3) The formula for calculating the functional index is: Leisure and Recreation Index = Number of Points of Interest (POIs) in the Third Space within the Unit / Unit Area; Transportation Convenience Index = Total Length of Roads within the Unit / Unit Area; The Function Index FI is obtained by comprehensively calculating the Leisure and Entertainment Index and the Transportation Convenience Index using a spatial distance model.

6. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The calculation steps for the comprehensive urban vitality index mentioned in step S4 are as follows: S41. Calculate the population index PI, economic index EI, and functional index FI respectively; S42. Standardize the three indicators; S43, Calculate the comprehensive urban vitality index VI: ， in, Let i be the urban vitality index of the i-th unit. Let be the population index of the i-th unit. Let i be the economic index of the i-th unit. Let i be the functional index of the i-th unit. The minimum population index for the i-th unit. The minimum value of the economic index for the i-th unit. This is the minimum value of the functional index of the i-th unit.

7. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The specific steps of the spatial autocorrelation analysis described in step S5 are as follows: S51. Perform global spatial autocorrelation analysis; The formula for calculating Moran's I exponent is: ， in, Moran's I index, The number of research units, and This represents the urban vitality index of adjacent units. This represents the average value of the city's vitality index. This is a spatial weight matrix based on adjacency relationships; S52. Perform local spatial autocorrelation analysis; The LISA index is calculated using the following formula: ， in, For unit Local spatial autocorrelation index; S53. Based on the LISA analysis results, the research units are divided into four clustering types: High-High, Low-Low, Low-High, and High-Low.

8. The urban vitality measurement method based on multi-scale data according to claim 1, characterized in that, The method also includes a visualization step: S61. Hierarchical Visualization: The urban vitality index is divided into 5 levels using the natural breakpoint method, equal interval method, or quantile method, and spatial visualization is performed through a GIS platform. S62. Multi-scale comparative analysis: Generate urban vitality distribution maps, spatial clustering pattern maps, and trend maps at different scales, and analyze the differences and correlations in vitality distribution at different scales; S63. Thematic Map Production: Create thematic maps of population vitality, economic vitality, and functional vitality, and analyze the spatial distribution characteristics of vitality in each dimension. S64, Statistical Analysis Report: Outputs a city vitality statistical report, including vitality distribution characteristics and spatial clustering at various scales, and generates a vitality distribution histogram, scatter plot and correlation matrix; S65. Decision Recommendation Generation: Based on the results of vitality analysis, identify areas with potential for vitality enhancement and areas at risk of vitality decline, and propose differentiated planning recommendations and policy measures for different types of areas.

9. A city vitality measurement system based on multi-scale data, characterized in that, include: The multi-source data acquisition and preprocessing unit is used to collect remote sensing data, spatiotemporal positioning data, and statistical data within the study area; The collected data underwent coordinate standardization, format conversion, and quality inspection. The multi-scale analysis framework is established based on the administrative boundaries and geographical characteristics of the study area, creating an analysis framework at three spatial scales: township, community, and raster. All data are then unified into the corresponding spatial scale units. The Urban Vitality Index System Construction Unit is used to construct an urban vitality representation index system based on three dimensions: population, economy, and function, and to calculate the population index, economic index, and functional index respectively. The spatial distance model is used to perform dimensionless processing on various indicators and calculate the comprehensive urban vitality index of each spatial unit. The spatial autocorrelation analysis unit is used to identify the spatial clustering characteristics and distribution patterns of urban vitality through global and local spatial autocorrelation analysis.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed, it implements the steps of the method as described in any one of claims 1 to 8.

Images