Urban physical examination method for multi-source data analysis production-city fusion and job-housing balance

Abstract

The invention discloses a multi-source data analysis city production and city integration and job-housing balance city physical examination method, which belongs to the technical field of city physical examination methods, integrates city scale, traffic accessibility and terrain multi-source data, selects a proper reference radius according to a city type needing to be evaluated, considers the influence of population density, congestion index, gradient and the like on actual commuting, and provides an evaluation result for the city production and city integration and job-housing balance. Correcting the reference radius through data standardization calculation and a three-dimensional correction formula to obtain a final buffer area radius, performing buffer area analysis on industrial land and residential land data by using a GIS tool to serve as the buffer area radius, calculating an overlapping area of the industrial land and residential land buffer areas, calculating a proximity index through a formula, and obtaining a final buffer area. And quantifying the job-housing balance fluctuation degree of the space unit by calculating the proximity index standard deviation, setting a stability threshold value, and judging whether the threshold value is met or not according to whether the threshold value is met or not, or triggering a three-stage iteration mechanism.

Description

Technical Field

[0001] This invention belongs to the technical field of urban physical examination methods, and in particular relates to an urban physical examination method that integrates industry and city and promotes work-life balance through multi-source data analysis. Background Technology

[0002] Early research on industry-city integration and job-housing balance focused primarily on the theoretical level. However, with accelerating urbanization, practical needs have prompted scholars to explore quantitative assessment methods. Traditionally, the employment-residence ratio is commonly used to measure job-housing balance. Cervero (1989) proposed a classic employment-residence ratio model, arguing that a ratio between 0.75 and 1.25 can be considered a basic balance. This method is simple and intuitive, providing a preliminary quantitative basis for assessing job-housing balance by comparing the number of jobs and the number of residents.

[0003] However, this method only considers the quantitative relationship and ignores the spatial distribution characteristics of industrial land and residential land. In reality, even if the employment-residence ratio is within a reasonable range, if the spatial distance between industrial land and residential land is far, residents still need to spend a lot of time and energy commuting, and cannot truly achieve a work-life balance.

[0004] With the development of Geographic Information System (GIS) technology, spatial analysis methods have been gradually introduced into urban planning research. Ye Yingcong et al. (2017), in their study on the coordinated layout of urban space and agricultural production space, used a GIS platform to evaluate the suitability of construction and development and the comprehensive quality of cultivated land. This demonstrates that GIS technology has significant advantages in processing spatial data and analyzing spatial relationships. In research on the integration of industry and city and the balance between work and residence, some scholars have also attempted to use GIS's buffer analysis and spatial overlay analysis functions to study the spatial relationship between industrial land and residential land. For example, by setting buffer zones for industrial land and residential land respectively, the overlap area of ​​the two buffer zones is calculated to measure spatial proximity. However, previous studies often used fixed values ​​for the buffer zone radius, lacking dynamic consideration of the actual urban situation. Different cities vary greatly in size, traffic conditions, and population density, making it difficult for a fixed buffer zone radius to accurately reflect the true spatial proximity relationships between different cities.

[0005] In recent years, with the rapid development of information technology, multi-source data has provided richer information sources for urban research. Besides traditional land use data and census data, this includes geographic information data provided by platforms such as OpenStreetMap and Gaode Maps, as well as traffic flow data and mobile phone signaling data. These multi-source data can reflect the operational status and spatial characteristics of cities from different perspectives. However, in current research on industry-city integration and job-housing balance, the integration and application of multi-source data still faces many challenges. On the one hand, different data sources have different data formats, accuracy, and coordinate systems, making data fusion difficult. On the other hand, how to extract effective information related to industry-city integration and job-housing balance from massive amounts of multi-source data and conduct reasonable analysis and application is also a current challenge.

[0006] In summary, although certain research results have been achieved in the field of industry-city integration and job-housing balance assessment, there are still many shortcomings. To address these issues, a multi-source data analysis method for urban health check of industry-city integration and job-housing balance is designed. Summary of the Invention

[0007] The main objective of this invention is to provide a method for urban health assessment that integrates industry and city and promotes work-life balance through multi-source data analysis.

[0008] To achieve the above objectives, the present invention employs the following technical solution:

[0009] A multi-source data analysis method for urban health assessment of industry-city integration and job-housing balance includes the following steps:

[0010] Integrate multi-source data on city size, transportation accessibility, and topography;

[0011] Select an appropriate baseline radius R0 based on the type of city to be evaluated;

[0012] Considering the impact of population density, congestion index, and slope on actual commuting, the baseline radius is corrected through data standardization calculation and a three-dimensional correction formula to obtain the final buffer radius Rpred;

[0013] Using GIS tools, buffer analysis was performed on industrial and residential land data. Rpred was used as the buffer radius to calculate the overlapping area of ​​the industrial and residential land buffers, and the proximity index was calculated using a formula.

[0014] The degree of fluctuation in the job-housing balance of spatial units is quantified by calculating the standard deviation of the proximity index. A stability threshold is set, and a standard-setting or three-level iteration mechanism is triggered based on whether the threshold is met.

[0015] In one scheme, the multi-source data includes urban resident population, built-up area population density, morning peak congestion index, and average slope, wherein: the urban resident population data comes from the National Bureau of Statistics Yearbook, local government bulletins, government official websites, and websites related to publicly available data;

[0016] Population density data for built-up areas are derived from national land survey data, community statistics data, and public security bureau population data.

[0017] Morning rush hour congestion index data comes from Baidu Maps, Gaode Maps API, and traffic police data;

[0018] The average slope data comes from BIGMap and the National Geomatics Center of China (DEM).

[0019] In one scheme, the baseline radius R0 for different city types is as follows:

[0020] Type II small cities: urban resident population < 200,000, baseline radius R0 is 500 meters;

[0021] Type I small cities: urban resident population of 200,000-500,000, with a baseline radius R0 of 700 meters;

[0022] Medium-sized cities: urban resident population of 500,000 to 1,000,000, with a baseline radius R0 of 900 meters;

[0023] Type II large cities: urban resident population of 1-3 million, with a baseline radius R0 of 1200 meters;

[0024] Type I large cities: urban resident population of 3-5 million, with a baseline radius R0 of 1400 meters;

[0025] Mega-cities: Urban resident population of 5-10 million, with a baseline radius R0 of 1600 meters;

[0026] Mega-cities: Urban resident population ≥ 10 million, with a baseline radius R0 of 1800 meters.

[0027] In one approach, the data standardization calculation is performed using a formula. conduct;

[0028] Where X is the original data value; μ type The mean of the original data values ​​of similar cities; max type min type These represent the maximum and minimum extreme values ​​of indicators for similar cities, respectively. When the standardized result exceeds [-1, 1], the boundary value is taken; x norm X represents the standardized value, while X represents the original data value.

[0029] In one scheme, the three-dimensional correction formula is R. pred =R0+β P ·Δ P +β T ·Δ T +β L ·Δ L ;

[0030] Where, β P This is a population density correction factor, with a value of 2.0 m / (person / km²). 2 );

[0031] β T This is the congestion index correction factor, with a value of -200 meters per congestion unit;

[0032] β L This is the slope correction factor, with a value of -30 meters per degree;

[0033] Δ P This represents the difference between the standardized population density value and the average value of similar cities.

[0034] Δ T This is the difference between the standardized value of the congestion index and the average value of similar cities.

[0035] Δ L This represents the difference between the standardized slope value and the average value for similar cities.

[0036] In one approach, the proximity index is calculated using GIS tools, and the proximity index is calculated using the following formula:

[0037] Where A is the overlap area (km) of the two types of buffer zones. 2 B1 and B2 are the total buffer zones for industrial and residential land use (km²), respectively. 2 ).

[0038] In one scheme, the stability threshold of the dynamic verification and feedback mechanism is set to a standard deviation σ < 5%, corresponding to a planning decision error rate ≤ 2%.

[0039] One approach also includes a data preprocessing step, specifically unifying the coordinate system of the data;

[0040] The study area was divided into a 1km×1km grid, and invalid grids were removed.

[0041] Beneficial technical effects of the present invention:

[0042] 1. Breaking through the limitations of assessments based solely on quantity, we will construct an assessment index that comprehensively considers spatial factors. By quantifying the spatial proximity between industrial and residential land, we can more accurately reflect the likelihood of residents finding employment nearby, thereby accurately measuring the actual level of industry-city integration and work-life balance.

[0043] 2. In previous studies, when using GIS technology to analyze the spatial relationship between industrial and residential land, fixed buffer radii were often used. For example, although Ye Yingcong et al. (2017) used GIS for spatial layout research, the fixed radius could not dynamically adapt to the actual differences in city size, traffic conditions, population density, etc., and it was difficult to accurately reflect the real spatial proximity relationship of different areas.

[0044] 3. This patent aims to develop a dynamic and adaptive buffer analysis method. This method can flexibly adjust the buffer radius by combining specific urban characteristics, such as traffic accessibility and population distribution, to more accurately measure the spatial proximity between industrial and residential land, providing more realistic spatial analysis results for urban planning.

[0045] 4. The problem this patent aims to solve is to establish an efficient data integration and analysis system. Through data preprocessing, feature extraction, and fusion algorithms, it effectively integrates multi-source data from the National Natural Resources Survey, OpenStreetMap, and Gaode Map Open Platform, extracting valuable information and improving the accuracy and comprehensiveness of the assessment. Attached Figure Description

[0046] Figure 1 This is a land use extraction map of a preferred embodiment of a city health check method for multi-source data analysis of industry-city integration and job-housing balance according to the present invention;

[0047] Figure 2 This is a buffer generation diagram of a preferred embodiment of an urban health check method for multi-source data analysis of industry-city integration and work-housing balance according to the present invention;

[0048] Figure 3 This is an overlapping area generation map of a preferred embodiment of an urban health check method for multi-source data analysis of industry-city integration and work-life balance according to the present invention;

[0049] Figure 4 This is a detailed comparative analysis diagram of street blocks according to a preferred embodiment of a city health check method for multi-source data analysis of industry-city integration and work-life balance according to the present invention;

[0050] Figure 5 This is a rendering of the urban renewal and transformation effect of G01 community as an example, based on a preferred embodiment of a multi-source data analysis method for urban health check of industry-city integration and work-life balance according to the present invention.

[0051] Figure 6 This is a G01 community proximity analysis diagram, representing a preferred embodiment of a multi-source data analysis method for urban health checkup on industry-city integration and job-housing balance according to the present invention.

[0052] Figure 7 This is a flowchart of a preferred embodiment of an urban health check method for multi-source data analysis of industry-city integration and work-life balance according to the present invention. Detailed Implementation

[0053] To enable those skilled in the art to understand the technical solution of the present invention more clearly, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0054] Multi-source data acquisition: Integrating multi-source data such as city size (built-up area, population density), transportation accessibility (road grade, public transport stop coverage, congestion index, average vehicle speed), topography (slope reflected by DEM elevation model), and terrain, as detailed below:

[0055]

[0056]

[0057] The baseline radius R0 for different city types: Based on the State Council's "Notice on Adjusting the Standards for Classifying City Sizes," and referencing relevant standards such as the "Standards for Planning and Design of Urban Residential Areas" (GB50180-2018) and the "Guidelines for Mountain City Planning" (DB50 / T547-2021), and verified through multi-city measured data (including topographic correction) and academic commuting research, the baseline radii corresponding to different city types are proposed. Details are as follows:

[0058]

[0059] Determine the initial buffer radius: Select an appropriate baseline radius R0 based on the type of city to be evaluated.

[0060] Step 2: Adjust the buffer radius;

[0061] Considering the impact of population density, congestion index, and slope on actual commuting, and referencing the Ministry of Housing and Urban-Rural Development's 2024 Statistical Yearbook and local planning data, the "2024 Urban Construction Statistical Bulletin," Baidu Maps' "2024 China Urban Traffic Report," the "Third National Land Survey Slope Data," and typical sponge city cases, this paper compiles basic data from representative cities including Beijing, Hangzhou, Dongguan, Xi'an, Xiamen, Nanning, Wuhan, Baoding, Chongqing, Luzhou, Shaoguan, Baoding, and Linxia, ​​and proposes the following standardized urban parameter table:

[0062]

[0063] Data standardization calculation:

[0064]

[0065] The standardized value of xnorm (dimensionless, used for model calculation);

[0066] X Original data values ​​(such as population density, congestion index, slope);

[0067] μtype: The average value of this indicator for similar cities;

[0068] maxtype is the maximum value of this indicator for similar cities;

[0069] Three-dimensional revision announcement:

[0070] R pred =R0+β P ·Δ P +β T ·Δ T +β L ·Δ L ;

[0071] Population density: β P = 2.0 meters / (person / km) 2 (For each additional person, the radius increases by 2 meters);

[0072] Congestion Index: β T = -200 meters / congestion unit (for every increase of 0.1, the radius decreases by 20 meters);

[0073] Slope: β L = -30 meters / degree (for every 1 degree increase, the radius decreases by 30 meters);

[0074] Example 1

[0075] City A Case Calculation

[0076] Population density standardization: Pnorm = (2500-1200) / (420-1800) = -1380 / 1300 ≈ -1.06 (truncated to -1);

[0077] Meaning: City A has a significantly lower population density than the average of medium-sized cities (1800 people / km²). 2 After standardization, it becomes -1 (representing the lower limit of the extreme value).

[0078] Standardized congestion index: Tnorm = (3.0 - 2.5) / (5 - 2.5) = 0.5 - 1 = -2 (truncated to -1);

[0079] Meaning: City A's congestion index is lower than the average of medium-sized cities (2.5), and after standardization, it is -1 (representing the lower limit of the extreme value).

[0080] Slope standardization: Lnorm = (8.7 - 2.5) / (8.0 - 2.0) = 6.0 / 6.2 ≈ 1.03 (truncated to 1)

[0081] Meaning: The slope of city A is significantly higher than the average of medium-sized cities (2.5°), and is standardized to 1 (representing the upper limit of the extreme value).

[0082] Outlier handling rules;

[0083] Truncation logic: If the calculated value exceeds [-1,1], take the boundary value (e.g., the standardized population density value of city A is -1.06 → -1).

[0084] Reason: To avoid the excessive influence of extreme values ​​on the model (e.g., City A is a river valley city, and its population density is much lower than the average of medium-sized cities).

[0085] Through standardization, the population density, congestion index, and slope of city A can be uniformly incorporated into the model calculation, reflecting its relative position among similar cities, and providing a scientific basis for dynamically adjusting the buffer radius (e.g., due to the low population density and high slope of city A, the initial radius Rfinal=900+(-1)*2+(-1)*(-200)+1*(-30) is corrected from 900 meters to 854 meters).

[0086] Calculate the proximity index using GIS tools;

[0087] Using GIS tools such as ArcGIS or QGIS, buffer analysis is performed on industrial and residential land data. Using Rfinal as the buffer radius, the overlapping area of ​​the industrial and residential land buffers is calculated. The formula is:

[0088]

[0089] Where A is the overlap area (km) of the two types of buffer zones. 2 B1 and B2 are the total buffer zones for industrial and residential land use (km²), respectively. 2 ).

[0090] The evaluation criteria for the proximity index between industrial land and residential land can be divided into the following levels.

[0091] High proximity (≥0.7): This indicates that there is a large overlap between industrial land and residential land, and residents have excellent conditions for finding employment nearby. In this case, residents have short commuting distances and low transportation costs, which is conducive to improving the quality of life of residents and promoting the integrated development of industry and city.

[0092] For example, if the proximity index of a region reaches 0.8, it means that most residents in the region can find employment within a short distance, reducing commuting time and traffic pressure, while also enhancing the region's economic vitality and social stability.

[0093] Medium proximity (0.4-0.7): The overlap area is moderate, and residents have certain conditions for finding employment nearby, but there is still room for improvement. In this case, some residents may have a slightly longer commute distance. It is necessary to further improve the balance between work and residence through reasonable urban planning and traffic optimization measures. For example, bus routes can be optimized in this area, the number of shared bicycle deployment points can be increased, and the accessibility and convenience of public transportation can be improved to facilitate residents' commutes.

[0094] Low proximity (<0.4): This indicates a small overlap between industrial and residential land, resulting in generally longer commuting distances for residents, causing inconvenience and increasing the burden on urban transportation. In this case, significant adjustments to land use layout are necessary. For example, increasing mixed-use land (such as mixed commercial and residential land) in low proximity areas could promote the integration of industrial and residential functions; or, based on the needs of industrial development, the layout of industrial land could be rationally adjusted to be closer to residential areas to improve the current imbalance between work and residence.

[0095] Dynamic verification and feedback mechanism

[0096] The degree of fluctuation in the job-housing balance of spatial units is quantified by calculating the standard deviation (σ) of the proximity index. The formula is as follows: Where Pi is the proximity index of the i-th spatial unit, P is the overall mean, and n represents the total number of spatial units. Referring to the "Urban Physical Examination and Evaluation Regulations for Territorial Spatial Planning" and data from over 30 pilot cities, a stability threshold σ < 5% (corresponding to a planning decision error rate ≤ 2%) is set.

[0097] Criterion assessment: If σ meets the threshold, it indicates that the buffer radius adjustment is reasonable, the reliability of the assessment results is over 98%, and it can be directly applied to the planning and implementation.

[0098] Dynamic feedback: If σ exceeds the threshold, a three-level iteration mechanism is triggered.

[0099] Data layer verification: Check the accuracy of basic data such as spatial unit boundaries and land use classification;

[0100] Model layer optimization: Adjust the parameters of the buffer radius formula (such as population density and slope correction coefficient);

[0101] Result layer validation: Recalculate the proximity index until σ regresses to a reasonable range, forming a closed loop of "evaluation-adjustment-revalidation".

[0102] This mechanism ensures the scientific nature of the dynamic adjustment of the buffer zone radius by quantifying the degree of fluctuation and controlling the threshold, keeping the planning error within an acceptable range for engineering, and providing reliable support for the planning of industry-city integration.

[0103] Data collection and organization;

[0104] When implementing this technical solution in City A, multi-dimensional data was first collected. City size data, such as built-up area and population density, were obtained from City A's statistical yearbook, with the built-up area being 18.2 km². 2 The population density within the urban area is 420 people / km². 2 (The population density in the built-up area is 4708 people / km²) 2 The road network density (4.1 km / km) was obtained through open-source data platforms such as Gaode Maps. 2 Traffic accessibility data such as peak congestion index (1.5) and terrain data such as slope (average slope 8.7°) were obtained from the DEM elevation model obtained from the National Geomatics Center of China. At the same time, relevant data reflecting land use intensity were collected.

[0105] Data preprocessing;

[0106] The collected data underwent preprocessing. The coordinate system for the unified data was CGCS2000, consistent with the spatial planning of City A. The study area was divided into 1km × 1km grids, resulting in 18 grids covering the built-up area. Invalid grids with a slope >25° were removed. All data were standardized; for example, population density and road network density data were standardized using formulas. Normalization is performed to eliminate the influence of dimensions, ensuring that all eigenvalues ​​fall within the interval [0,1]. The specific calculation is as follows:

[0107]

[0108] Outlier handling:

[0109] Both population density and slope exceed the standard range, so they are standardized according to extreme values ​​(the boundary value is taken if the value exceeds the range of [-1,1]);

[0110] Buffer radius calculation

[0111] Step 1: Refer to Table II for the baseline radius of a small city: R0 = 500 meters

[0112] Step 2: Three-dimensional correction;

[0113] formula:

[0114] R pred =R0+β P ·Δ P +β T·Δ T +β L ·Δ L ;

[0115] Population density: β P = 2.0 meters / (person / km) 2 (The radius increases by 2 meters for each additional person.)

[0116] Congestion Index: β T = -200 meters / congestion unit (for every increase of 0.1, the radius decreases by 20 meters)

[0117] Slope: β L = -30 meters / degree (for every 1 degree increase, the radius decreases by 30 meters)

[0118] Substituting boundary values, the calculation is: Rpred = 500 + 1 × 2 + 0 × (-200) + 1 × (-30) = 500 + 2 - 30 = 472 meters

[0119] (III) Using GIS tools to analyze proximity index

[0120] Spatial Analysis Practice (ArcGIS Pro);

[0121] (1) Data source:

[0122] Industrial land extraction: Data from the Third National Land Survey of the Ministry of Natural Resources (SHP format, including "industrial land", "logistics and warehousing land", etc., the industrial area of ​​city A is concentrated in the industrial park east of Beichuan River).

[0123] Residential land extraction: Same as above (SHP format, including "urban residential land" and "rural homestead". The residential land in City A is distributed on both sides of the river and in the old city in the south).

[0124] Coordinate system: uniformly converted to CGCS20003 degree zone (EPSG:4548) to ensure consistency with DEM topographic data.

[0125] Data cleaning and extraction:

[0126] Areas smaller than 0.1 km² are excluded. 2 Scattered land use;

[0127] Adjacent industrial / residential patches were merged (Datong County has a compact built-up area, and a total of 5 industrial patches and 12 residential patches were extracted).

[0128] (2) Buffer generation and proximity calculation

[0129] Buffer generation (ArcGIS operation steps):

[0130] Tools: AnalysisTools>Proximity>Buffer

[0131] parameter:

[0132] Input elements: Industrial land and residential land (processed separately);

[0133] Buffer radius: 472 meters (initial buffer radius Rpred = 472 meters);

[0134] Merge option: ALL (Merge overlapping buffers).

[0135] Output result:

[0136] The industrial buffer zone area B1 = 1.28 km2 (including 5 industrial land buffer zones);

[0137] The residential buffer zone area B2 = 0.35 km2 (including 12 residential area buffer zones).

[0138] Overlap area calculation:

[0139] Tools: AnalysisTools > Overlay > Intersect

[0140] Result: The overlapping area of ​​the industrial and residential buffer zone is A = 0.12 km2 (mainly concentrated at the junction of the old city and the industrial zone).

[0141] Proximity index calculation:

[0142] (P) = Overlapping area of ​​industrial land and residential land (A) / Buffer area of ​​industrial land and residential land

[0143] The job-housing proximity in the built-up area of ​​City A is 0.079, indicating a low overlap between industry and residential areas and a clear separation between jobs and housing (threshold reference: P<0.4 requires key optimization).

[0144] Detailed comparative analysis of neighborhoods

[0145] The study focused on communities or streets within the built-up area of ​​City A, including 12 communities in one town. The specific calculation results are shown in the table below:

[0146]

[0147]

[0148] Note: The scope of a block is determined according to the administrative boundaries of each community.

[0149] Data characteristics description:

[0150] Spatial Differentiation:

[0151] Low proximity areas (P<0.45): concentrated in the lower reaches of the Huangshui River (G01-G03), due to the separation of industrial and residential spaces and topographical barriers.

[0152] High proximity areas (P≥0.7): concentrated in the central plain of the Huangshui River (G08-G12), benefiting from bus stations, commercial facilities and mixed-use land.

[0153] Dynamic verification and feedback mechanism;

[0154] Key validation metrics calculation

[0155] Supported by standard deviation calculation;

[0156] Full grid standard deviation (σ): The overall standard deviation calculated using the latest data is 4.8%, indicating that the dispersion of the proximity index of the built-up area of ​​city A is within the range of "±5% fluctuation tolerance", and the overall stability meets the standard.

[0157] Extreme values ​​of fluctuations in a single grid:

[0158] The lowest deviation was in community G01 (-53.6%), and the highest deviation was in community G09 (+13.0%). G01 exceeded the "±15% volatility tolerance" and triggered the "major adjustment" mechanism; the deviations of the remaining communities were all within the allowable range.

[0159] Proximity index distribution characteristics;

[0160] Mean (μ): The overall job-housing proximity mean is 0.58, reflecting a moderate level of job-housing balance in the built-up area of ​​City A.

[0161] The proportion of low proximity communities is only 8.3% (G01 community), which is lower than the planning requirement of 10%. The remaining communities all meet the medium to high proximity standards.

[0162] Dynamic verification process

[0163] Data layer verification

[0164] Outlier identification: Due to the lack of supporting housing and terrain barriers, the proximity of G01 community is significantly lower than the average. After verification, the data is confirmed to be correct, and it is confirmed to be a true work-residence separation area.

[0165] Model stability: After removing the extreme values ​​of G01, the standard deviation decreased to 2.1%, proving that the model is stable for non-extreme data.

[0166] Planning layer verification

[0167] Simulation of the effects of the measures:

[0168] After the renovation of G01 community: the proximity increased by 103% from the current value, reaching 0.65 (the upper limit of medium proximity);

[0169] Work-life self-sufficiency rate: increased by 46% from the low level before the renovation, and commuting time was reduced by 46% (from 28 minutes of detour to 15 minutes of direct access).

[0170] Feedback mechanism design (tiered response strategy);

[0171] Analysis of the differences between this invention and traditional models and explanation of its advantages; comparison with the traditional employment-residence ratio model;

[0172]

[0173] Comparison with other simple buffer analysis models based on GIS;

[0174]

[0175] Strengths summary and creative argumentation;

[0176] (1) Improved assessment accuracy

[0177] Traditional models only reflect the relationship between the number of jobs and residences, and cannot quantify spatial commuting costs (e.g., in City A, the traditional ratio of G09 community is 1:1.2, but the actual commuting time is 28 minutes; this invention's proximity of 0.32 accurately reveals the separation problem).

[0178] This invention achieves a leap from "quantity balance" to "spatial efficiency balance" through a three-dimensional index of "proximity index + commuting time + job-housing self-sufficiency rate". Validated in 30+ cities, the assessment accuracy is 40% higher than traditional methods.

[0179] (2) Reflects the actual situation in the city;

[0180] For the first time, it integrates terrain slope (such as the 8.7° steep slope in City A) with traffic impedance (such as the river-crossing bridge in Community G04), overcomes the limitations of the traditional "plain thinking" model, and establishes a coupled "city scale-topography-transportation" model, filling the gap in river valley / mountain city planning.

[0181] (3) Dynamic programming support;

[0182] Unlike the "one-time evaluation" of simple buffer analysis, this invention constructs a closed loop of "data acquisition - model calculation - dynamic verification - planning feedback" (e.g. Figure 1For example, City A determined that the buffer radius was reasonable by using σ = 4.8%, thus avoiding the problem of a disconnect between planning and implementation; a simulation analysis was conducted to compare the effects of urban renewal and transformation before and after the transformation of Community G01.

[0183]

[0184]

[0185] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0186] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for urban health assessment based on multi-source data analysis of industry-city integration and work-life balance, characterized in that: Includes the following steps: Integrate multi-source data on city size, transportation accessibility, and topography; Select an appropriate baseline radius R0 based on the type of city to be evaluated; Considering the impact of population density, congestion index, and slope on actual commuting, the baseline radius is corrected using data standardization calculations and a three-dimensional correction formula to obtain the final buffer radius R. pred ; Buffer analysis of industrial and residential land use data was performed using GIS tools, with R... pred As the buffer radius, the overlapping area of ​​the industrial land and residential land buffer zones is calculated, and the proximity index is calculated using a formula. Dynamic verification and feedback are achieved by quantifying the degree of fluctuation in the job-housing balance of spatial units through the calculation of the standard deviation of the proximity index, setting a stability threshold, and determining compliance or triggering a three-level iteration mechanism based on whether the threshold is met.

2. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The multi-source data includes the urban resident population, the population density of the built-up area, the morning rush hour congestion index, and the average slope.

3. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The reference radius R0 is as follows: Type II small cities: urban resident population < 200,000, baseline radius R0 is 500 meters; Type I small cities: urban resident population of 200,000-500,000, with a baseline radius R0 of 700 meters; Medium-sized cities: urban resident population of 500,000 to 1,000,000, with a baseline radius R0 of 900 meters; Type II large cities: urban resident population of 1-3 million, with a baseline radius R0 of 1200 meters; Type I large cities: urban resident population of 3-5 million, with a baseline radius R0 of 1400 meters; Mega-cities: Urban resident population of 5-10 million, with a baseline radius R0 of 1600 meters; Mega-cities: Urban resident population ≥ 10 million, with a baseline radius R0 of 1800 meters.

4. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The data standardization calculation is performed using the formula. conduct; Where X is the original data value; μ type The mean of the original data values ​​of similar cities; max type min type These represent the maximum and minimum extreme values ​​of indicators for the same type of city, respectively. When the standardized result exceeds [-1, 1], the boundary value is taken. X represents the standardized value, while X represents the original data value.

5. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The three-dimensional correction formula is R. pred =R0+β P ·Δ P +β T ·Δ T +β L ·Δ L ; Where, β P This is a population density correction factor, with a value of 2.0 m / (person / km²). 2 ); β T This is a congestion index correction factor, with a value of -200 meters per congestion unit; β L This is the slope correction factor, with a value of -30 meters per degree; Δ P Δ represents the difference between the standardized population density value and the average value of similar cities. T The congestion index is the difference between its standardized value and the average value for similar cities; Δ L This represents the difference between the standardized slope value and the average value for similar cities.

6. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The proximity index is expressed by the formula: Where A is the overlap area (km) of the two types of buffer zones. 2 B1 and B2 are the total buffer zones for industrial and residential land use (km²), respectively. 2 ).

7. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The stability threshold is set to a standard deviation σ < 5%, corresponding to a planning decision error rate ≤ 2%.

8. The urban health check method for multi-source data analysis of industry-city integration and work-life balance according to claim 1, characterized in that: The dynamic verification and feedback also includes a data preprocessing step, specifically unifying the coordinate system of the data; dividing the study area into a 1km×1km grid and removing invalid grids.

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