Urban area newly-added employment and economic vitality measurement method and system and storage medium

Abstract

The invention discloses a method and a system for measuring newly increased employment and economic vitality of an urban area and a storage medium. The method comprises the following steps: acquiring original data of a target area; preprocessing the obtained original data to obtain preprocessed data; according to the preprocessed data, calculating density indexes and spatial distribution characteristics of strategic industry newly-added employment and talent demands of the target area; and according to the spatial distribution characteristics, identifying a gathering core boundary of strategic industry newly-added employment and talent demands, quantifying the gathering degree of the strategic industry newly-added employment and talent demands, and analyzing the economic vitality of the target area by using bivariate spatial autocorrelation. According to the method, the concept of newly added employment is introduced to the field of urban science, spatial distribution characteristics of talent demands are analyzed, the gathering degree of newly added employment and talent demands of strategic industries is identified and quantified, spatial analysis is assisted, planning or optimization suggestions are provided, and the existing research angle is supplemented.

Description

Technical Field

[0001] The present invention relates to a method, system and medium for measuring newly added employment and economic vitality in an urban area, and belongs to the technical field of urban area space measurement. Background Art

[0002] Job data mainly describes the situation of new employment in a city and is mostly used in research in the fields of human resources, labor market and talent demand. New employment usually refers to the number of new jobs in an economic system, usually measured by the number of new jobs in a specific period (such as a year or a quarter). New employment is an important indicator of urban vitality and can be used to assess the health of the economy and the activity of the labor market: in economic booms, new employment usually increases, and vice versa.

[0003] For a long time, scholars have analyzed urban employment patterns based on statistical yearbooks and questionnaire data. These methods have the disadvantages of being static, taking a long time to update, having low data accuracy, and having high acquisition costs. As urban research becomes more refined, data-driven and real-time sensing technologies become more advanced, and traditional data analysis methods are unable to meet current research needs.

[0004] The open source job data from the talent recruitment website provides a new angle for measuring the new employment center of the city. The data has the following innovations: 1) An intermediate variable that reflects the past and predicts the future: New employment is a kind of enterprise expansion behavior. More jobs mean that the region has higher enterprise vitality and better development trends. New jobs, as an intermediate variable in the process, are both a "barometer" that reflects the current economic vitality of the region and a "prophecy" that predicts future development trends; 2) The data is real-time and dynamic, and can reflect the new employment situation in the past and present, and has the conditions for time and space comparison, analysis and prediction. Summary of the invention

[0005] In view of this, the present invention provides a method, system, computer device and storage medium for measuring new employment and economic vitality in urban areas, which clearly define the fields of data with analytical value, propose density indicators and global distribution indicators for measuring new employment and talent demand in urban areas, measure the economic vitality of urban areas in combination with existing enterprise data, introduce the concept of new employment into the field of urban science, conduct spatial analysis and provide planning or optimization suggestions, supplementing the existing research perspectives.

[0006] The first object of the present invention is to provide a method for measuring new employment and economic vitality in an urban area.

[0007] The second object of the present invention is to provide a system for measuring new employment and economic vitality in urban areas.

[0008] A third object of the present invention is to provide a computer device.

[0009] A fourth object of the present invention is to provide a storage medium.

[0010] The first object of the present invention can be achieved by adopting the following technical solutions:

[0011] A method for measuring new employment and economic vitality in urban areas, the method comprising:

[0012] Acquire the original data of the target area, wherein the original data includes job data, company address data structured by job data, administrative division data, strategic industry statistical caliber data and enterprise POI data;

[0013] Preprocessing the acquired raw data to obtain preprocessed data;

[0014] Based on the pre-processed data, calculate the density index and spatial distribution characteristics of new employment and talent demand in the strategic industries of the target area;

[0015] Based on the spatial distribution characteristics, the core boundaries of the concentration of new employment and talent demand in strategic industries are identified, the degree of concentration of new employment and talent demand in strategic industries is quantified, and the economic vitality of the target region is analyzed using bivariate spatial autocorrelation.

[0016] Furthermore, the preprocessing of the acquired raw data to obtain preprocessed data specifically includes:

[0017] Merge and clean job data;

[0018] Convert the company address data into the longitude and latitude coordinates of the Amap coordinate system, and perform verification and correction;

[0019] Merge the administrative division data and divide it into N*N grids;

[0020] According to the statistical caliber data of strategic industries, the data on newly added employment in strategic industries are screened;

[0021] Classify enterprise POI data according to strategic industries, and convert the addresses of enterprise POI data into longitude and latitude coordinates of the Amap coordinate system.

[0022] Furthermore, the density index and spatial distribution characteristics of new employment and talent demand in strategic industries in the target area are calculated based on the pre-processed data, specifically including:

[0023] The density of new employment and talent demand per unit area and the density of new employment and talent demand per unit population are used as density indicators;

[0024] The location entropy is used to characterize the concentration of certain recruitment positions in the grid as a distribution indicator;

[0025] Standard deviation ellipse analysis and spatial autocorrelation analysis are performed on the new employment and talent demand to obtain spatial distribution characteristics. The standard deviation ellipse includes the ellipse center, azimuth and standard deviation. The spatial autocorrelation analysis includes global Moran's index calculation for the new employment / talent demand.

[0026] Furthermore, the calculation formula of the density index is as follows:

[0027]

[0028]

[0029] Among them, D i is the density of new employment or talent demand per unit area, D Pi is the density of new employment or talent demand per unit population, N i is the number of recruitment positions in the sample, S is the sample area, N p is the number of permanent residents in the sample, N a is the county-level population in the census, s a is the area corresponding to the county.

[0030] Furthermore, the calculation formula of the distribution index is as follows:

[0031]

[0032] Among them, Q is the location entropy, S is the ratio of the number of jobs in a grid area to the total permanent population, and P is the ratio of the number of jobs in the target area to the total permanent population; if Q=1, the concentration level of this type of working population in the grid is the same as the level of the entire city; if Q<1, the concentration level of this type of working population in the grid is lower than the level of the entire city; if Q>1, the concentration level of this type of working population in the grid is higher than the level of the entire city.

[0033] Furthermore, the calculation formula of the ellipse center is as follows:

[0034]

[0035] The calculation formula of the azimuth angle is as follows:

[0036]

[0037] The standard deviation is calculated as follows:

[0038]

[0039] Among them, (X i , Y i ) represents the coordinates of point data i (i = 1, 2, 3 ... n), W i represents weight;

[0040] The calculation formula of the global Moran index is as follows:

[0041]

[0042] Where n is the number of spatial units, y i ,y j are the observation values ​​of the i-th and j-th spatial units, respectively. is the mean value of the observed values, w ij is the spatial weight value.

[0043] Furthermore, according to the spatial distribution characteristics, the core boundaries of the agglomeration of new employment and talent demand in strategic industries are identified, the degree of agglomeration of new employment and talent demand in strategic industries is quantified, and the economic vitality of the target area is analyzed using bivariate spatial autocorrelation, specifically including:

[0044] Based on the kernel density analysis method, combined with the standard deviation tail value test method of the statistical normal distribution hypothesis, the core boundaries of the agglomeration of new industrial employment and talent demand are identified;

[0045] Using the standard deviation surface as the statistical scope, calculate the concentration of new employment and talent demand in the industry;

[0046] Taking the strategic industry enterprises and new employment in the target area as the first and second variables, the bivariate global spatial autocorrelation is used to measure the overall spatial correlation of the two variables, and the bivariate local spatial autocorrelation is used to further analyze the spatial correlation and distribution pattern of the two variables.

[0047] By superimposing the data on strategic industry enterprises and new employment, we can obtain the hot spots and potential areas for new employment in strategic industries.

[0048] The second object of the present invention can be achieved by adopting the following technical solutions:

[0049] A system for measuring new employment and economic vitality in urban areas, the system comprising:

[0050] An acquisition module is used to acquire the original data of the target area, wherein the original data includes job data, company address data structured by job data, administrative division data, new employment data of strategic industries and enterprise POI data;

[0051] A preprocessing module is used to preprocess the acquired raw data to obtain preprocessed data;

[0052] A calculation module is used to calculate the density index and spatial distribution characteristics of new employment and talent demand in strategic industries in the target area based on the preprocessed data;

[0053] The measurement module is used to identify the core boundaries of the concentration of new employment and talent demand in strategic industries based on spatial distribution characteristics, quantify the degree of concentration of new employment and talent demand in strategic industries, and analyze the economic vitality of the target area using bivariate spatial autocorrelation.

[0054] The third object of the present invention can be achieved by adopting the following technical solutions:

[0055] A computer device includes a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned method for measuring new employment and economic vitality in urban areas is implemented.

[0056] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0057] A storage medium stores a program, which, when executed by a processor, implements the above-mentioned method for measuring new employment and economic vitality in urban areas.

[0058] The present invention has the following beneficial effects compared with the prior art:

[0059] The present invention uses newly added employment as an indicator to conduct a quantitative study on the increment of urban employment space, and analyzes the economic vitality of urban areas by comparing the spatial autocorrelation results of the increment and the stock. The job data and enterprise data required by this method are open source data and have good generalizability. Introducing the concept of newly added employment in the labor market into the field of urban science and conducting real-time spatial analysis has practical significance in urban space planning and related policy regulation: the government can formulate targeted and effective stimulus and control policies based on the current status of regional industrial development; the transportation department can estimate the additional commuting capacity brought by the newly added employment, and reasonably optimize public transportation and road planning; obtain real-time data by dynamically crawling website recruitment information, calculate and compare regional indicators, and build a smart city visualization platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0061] Figure 1 This is a flow chart of the method for measuring new employment and economic vitality in an urban area according to Example 1 of the present invention.

[0062] Figure 2 This is a schematic diagram of a recruitment website page according to Embodiment 1 of the present invention.

[0063] Figure 3 This is a schematic diagram of the results of crawling recruitment website data according to Example 1 of the present invention.

[0064] Figure 4 This is a schematic diagram of the number of jobs with different educational backgrounds in the Pearl River Delta urban agglomeration according to Example 1 of the present invention.

[0065] Figure 5 This is a schematic diagram of the proportion of job positions with different educational backgrounds in Example 1 of the present invention.

[0066] Figure 6 This is an ellipse analysis diagram of the spatial location of job positions and the standard deviation of talent demand in Example 1 of the present invention.

[0067] Figure 7a to Figure 7f This is an identification map of newly added employment spaces for strategic industries in the nine cities in the Greater Bay Area according to Example 1 of the present invention.

[0068] Figure 8a to Figure 8f This is a bivariate spatial autocorrelation analysis diagram of strategic industry enterprises and newly added employment in strategic industries in Example 1 of the present invention.

[0069] Fig. 9 This is a structural block diagram of the system for measuring new employment and economic vitality in urban areas according to Example 2 of the present invention.

[0070] Fig.10 This is a structural block diagram of a computer device according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0072] Embodiment 1:

[0073] like Figure 1 As shown, this embodiment provides a method for measuring new employment and economic vitality in an urban area, and the method includes the following steps:

[0074] S101, obtaining original data of a target area.

[0075] In this embodiment, the original data includes job data, company address data structured by job data, administrative division data, strategic industry statistical caliber data and enterprise POI data. The acquisition of various data is as follows:

[0076] A. Obtaining job data: Obtain open source job data from recruitment websites. The description of each job on the recruitment website includes job title, salary, company name, company type, number of employees, industry, work location, work experience requirements, education requirements, job description and release date. In this embodiment, the evaluation website is the "51Job" website, such as Figure 2 As shown; the web crawler is used to crawl and store the above description information of each recruitment position. When a page of content is crawled, the page turner automatically turns to the next page and uses the same method to extract the recruitment position information on the page; when it turns to the last page, the web crawler stops working and outputs the crawled data into a csv table according to each recruitment position data; the header of each csv table is the name of the above recruitment position description, such as Figure 3 shown.

[0077] Furthermore, this embodiment obtains all the recruitment data of nine cities in the Guangdong-Hong Kong-Macao Greater Bay Area: Guangzhou, Shenzhen, Foshan, Dongguan, Zhuhai, Huizhou, Zhongshan, Jiangmen and Zhaoqing from February 1 to February 31, 2022. The job data of different regions crawled every day are unified and standardized according to text information and stored as crawled region-crawled location.csv files.

[0078] B. Get structured company address data of job data: There are two ways to get the specific address of the company where the job data is located. One is to directly crawl the company address in the company information on the recruitment website; the other is to use the "company name" to perform batch queries on the "Qichacha" website, and obtain a structured address based on the company address results of the batch query.

[0079] The advantages and disadvantages of the two methods are: the advantage of the direct crawling method is that it omits the time for subsequent integration of two data from different sources, and the disadvantage is that the crawler needs to enter the secondary page of the web page, which requires a high response speed of the website; the advantage of the batch query method is that it can obtain additional company data, such as company size, company national standard industry classification and other fields for more detailed analysis in the later stage, and the disadvantage is that it requires additional data acquisition steps and the time cost of integrating two data is relatively high; since the subsequent analysis of this embodiment requires indicators such as company size, the batch query method is used to obtain the structured address of the job data.

[0080] Furthermore, the specific address of the company where the job data is obtained in this embodiment is as follows:

[0081] 1) Take "Company Name" as the parameter and remove duplicate values ​​in the data. Because different positions may correspond to one company, removing duplicate values ​​can reduce the workload of batch queries. Extract all values ​​of the "Company Name" column in all data in job_clean.csv and output it as comname.csv. Use the drop.duplicates() function of the pandas library of Python to remove the same rows and output it as comname_clean.csv.

[0082] 2) Split comname_clean.csv into several csv files with 5000 data items, because the maximum number of batch queries on the "QiChaCha" website is 5000. The csv files are named company-X.csv according to the serial number, where X is the serial number.

[0083] 3) Perform batch query operations on the "Qichacha" website to obtain the structured address loc, industry category ind and other information corresponding to each company name, save them as csv files and name them cominfo-X.csv according to the serial number, where X is the serial number.

[0084] 4) Merge all cominfo-X.csv files to get cominfo.csv.

[0085] 5) Link cominfo.csv and job_clean.csv. The link field should be "Company Name". Use the merge() function of the pandas library in Python. The on parameter is the company name, and the how parameter is the name of the original job data table, that is, job_clean.csv. Output

[0086] C. Obtain administrative division data: query the national geographic information public service platform to obtain the administrative division data of the nine urban administrative districts required for this embodiment.

[0087] D. Obtaining statistical data on strategic industries: The strategic industries in this embodiment are six strategic industries in the "strategic pillar industries" and "strategic emerging industries" of the Guangdong-Hong Kong-Macao Greater Bay Area, namely, green petrochemical industry, automobile industry, software and information service industry, biomedicine and health industry, new generation electronic information industry and advanced materials industry; the statistical data on the six strategic industries of green petrochemical industry, automobile industry, software and information service industry, biomedicine and health industry, new generation electronic information industry and advanced materials industry are obtained from the official website of the Guangdong Provincial Bureau of Statistics (stats.gd.gov.cn / ) and the Guangdong Provincial Department of Industry and Information Technology (https: / / gdii.gd.gov.cn / ), as shown in Table 1 below.

[0088] Table 1 Statistical data on strategic industries in nine cities in the Greater Bay Area

[0089]

[0090]

[0091] E. Obtain enterprise POI data: Obtain enterprise POI data from the Qichacha website platform.

[0092] S102: preprocess the acquired original data to obtain preprocessed data.

[0093] Furthermore, step S102 specifically includes:

[0094] S1021. Merge and clean job data.

[0095] 1) Data merging: Use the pandas library in Python to read each CSV file and merge all job information into the job.csv table.

[0096] 2) Data cleaning: First, remove the garbled information collected due to errors during the crawling process. Since companies have the phenomenon of repeatedly posting recruitment positions, data cleaning is required. Only one identical position posted by the same company on the same date is retained. For job information posted on the same day but crawled multiple times, the job title, company name and release time are used as the unique code of the job information, and the unique code is used to remove duplicate values. Use the drop.duplicates() function of the pandas library of python to remove rows with the same unique code, where the subset parameter selects the job title, company name and release time, and finally outputs job_clean.csv.

[0097] S1022. Convert the company address data into longitude and latitude coordinates of the Amap coordinate system, and perform verification and correction.

[0098] 1) Write the request code for the geocoding function in the Amap API. Use Python to read Enter the loc parameter of each job data into the address parameter, enter the user's identification code into the key parameter, and optionally enter the name of the city corresponding to the data into the city parameter.

[0099] 2) The location field in the return parameter contains the required information, including the longitude and latitude coordinates. Use the "," symbol as the separator to split it into two columns, "longitude" and "latitude". Write it together with the "formatted_address" in the return parameter, which is the loc parameter entered in the previous step. After obtaining the "longitude" and "latitude" data of all job data, output com_loc.csv.

[0100] 3) Copy com_loc.csv and Link, the link field should be loc, use the merge() function of the pandas library in Python, the on parameter is loc, and the how parameter is the position data name, that is, job-_com.csv.

[0101] S1023. Merge the administrative division data and divide it into N*N grids.

[0102] In this embodiment, the administrative division data of the nine cities' administrative districts are merged into a .shp file of the nine cities in the Greater Bay Area, and the .shp file of the nine cities in the Greater Bay Area is imported into the ArcGIS Pro platform and divided into 3km*3km grids according to administrative regions.

[0103] S1024. Screen the data on new employment in strategic industries based on the statistical caliber of strategic industries.

[0104] In this embodiment, the newly added employment data of strategic industries are screened, and the "Select by Attribute" tool is used to filter the ind in the job.shp field according to the industry subcategories of the six strategic industries to obtain the newly added employment data of the green petrochemical industry.shp, the newly added employment data of the automobile industry.shp, the newly added employment data of the software and information service industry.shp, the newly added employment data of the biomedicine and health industry.shp, the newly added employment data of the new generation electronic information industry.shp, and the newly added employment data of the advanced materials industry.Shp, which are then visualized in ArcGIS Pro.

[0105] S1025. Classify the enterprise POI data according to strategic industries, and convert the addresses of the enterprise POI data into longitude and latitude coordinates of the Amap coordinate system.

[0106] In this embodiment, according to the classification of six strategic industries, the data information such as the name, industry, enterprise scale, address, etc. of the enterprises in normal status and with more than 0 insured persons are screened out, and "green petrochemical industry enterprises.csv", "automobile industry new employment data.csv", "software and information service industry new employment data.csv", "biomedicine and health industry new employment data.csv", "new generation electronic information industry new employment data.csv", and "advanced materials industry new employment data.csv" are obtained; the addresses are converted into longitude and latitude coordinates of the Gaode coordinate system and the spatial points are located in ArcGIS Pro to obtain the .shp files of the six strategic industries.

[0107] S103. Based on the preprocessed data, calculate the density index and spatial distribution characteristics of new employment and talent demand in the strategic industries of the target area.

[0108] Furthermore, step S103 specifically includes:

[0109] S1031. The density of new employment and talent demand per unit area and the density of new employment and talent demand per unit population are used as density indicators.

[0110] The objects of statistics in this embodiment include two categories: new employment and talent demand. Referring to the definition of existing research, new employment is represented by the number of all job data, and the number of jobs requiring college degree or above is regarded as talent demand, such as Figure 4 and Figure 5 As shown, the statistical methods include density indicators and distribution indicators.

[0111] With reference to the existing demographic density indicators, the density of new employment and talent demand per unit area and the density of new employment and talent demand per unit population are used as density indicators. The calculation formula is as follows:

[0112]

[0113] Among them, D i To increase the density of employment and talent demand per unit area, is the density of new employment and talent demand per unit population, N i is the number of recruitment positions in the sample, S is the sample area, N p is the number of permanent residents in the sample, N a is the county-level population in the census, s a is the area corresponding to the county.

[0114] S1032. Use location entropy to characterize the concentration of certain recruitment positions in the grid as a distribution indicator.

[0115] Referring to the existing demographic distribution indicators, the location entropy is used to represent the concentration of certain recruitment positions in the grid in the region as the distribution indicator. The calculation formula is as follows:

[0116]

[0117] Among them, Q is the location entropy, S is the ratio of the number of jobs in a grid area to the total permanent population, and P is the ratio of the number of jobs in the target area to the total permanent population; if Q=1, the concentration level of this type of working population in the grid is the same as the level of the entire city; if Q<1, the concentration level of this type of working population in the grid is lower than the level of the entire city; if Q>1, the concentration level of this type of working population in the grid is higher than the level of the entire city.

[0118] In the above steps S1031 and S1032, the job data is processed spatially, and the research units are divided into a certain range according to the research needs, and the density index and distribution index are calculated for each unit (grid).

[0119] S1033. Conduct standard deviation ellipse analysis and spatial autocorrelation analysis on new employment / talent demand to obtain spatial distribution characteristics.

[0120] In this embodiment, the standard deviation ellipse includes the ellipse center, azimuth and standard deviation, which are used to measure the degree of dispersion and distribution direction trend of new employment / talent demand. The ellipse center is the center of gravity of new employment / talent demand, which is the average point of all element distributions. The azimuth indicates the distribution direction of new employment / talent demand. The standard deviation indicates the degree of dispersion of data in the x-axis and y-axis directions. The calculation formula is as follows:

[0121] Ellipse Center:

[0122]

[0123] Azimuth:

[0124]

[0125] Standard Deviation:

[0126]

[0127]

[0128] Among them, (X i , Y i ) represents the coordinates of point data i (i = 1, 2, 3 ... n), W i Represents weight.

[0129] Furthermore, the standard deviation ellipse calculation is performed on ArcGIS Pro. In this embodiment, the talent demand is calculated as the statistical object. The "Directional Distribution (Standard Deviation Ellipse)" tool in the toolbox is selected, and the job.shp that has been filtered in the previous step is input into the "Input Feature Class", and "distri.shp" is filled in the "Output Ellipse Feature Class". Other parameters remain default. The output feature distri.shp, in its attribute table, the "CenterX" parameter is the X-coordinate of the mean center, the "CenterY" parameter is the Y-coordinate of the mean center, the "CenterX" parameter is the direction of the ellipse, indicating the rotation angle of the major axis measured clockwise from the vertex, the "XstdDist" parameter is the standard distance of the ellipse on the X-axis, and the "YstdDist" parameter is the standard distance of the ellipse on the Y-axis. The standard deviation ellipse analysis of the spatial location of the job position and the talent demand is as follows: Figure 6 shown.

[0130] In this embodiment, the spatial autocorrelation analysis includes calculating the global Moran's index for the new employment / talent demand to determine whether the data is spatially clustered.

[0131] The global Moran's Index (Moran's I) is used to determine the correlation of the spatial distribution of the number of new talent positions. Its value range is between -1 and +1. When the value is greater than 0, it shows a positive spatial correlation. The closer it is to 1, the more clustered the distribution of the same data is. When it is less than 0, it shows a negative spatial correlation. The closer it is to -1, the more clustered the distribution of different data is. When it is equal to 0, the space is random.

[0132]

[0133] Where n is the number of spatial units, y i ,y j are the observation values ​​of the i-th and j-th spatial units, respectively. is the mean value of the observed values, w ij is the spatial weight value.

[0134] The significance of Moran's I was determined by the Z test, and the formula is as follows:

[0135]

[0136] in,

[0137] Furthermore, the global Moran's index is calculated on ArcGIS Pro, and the example calculation takes talent demand as the statistical object. Select the "Spatial Autocorrelation (Global Moran's I)" tool in the toolbox, input area_talent.shp into the "Input Feature Class", input "dens_sq" and "dens_pop" into the "Input Field" twice as statistical values, check the option to generate a report, and keep other settings as default. Obtain the global Moran's index of talent demand per unit area and per unit population. If the global Moran's index is close to 1, the p value is less than 0.01, and the z score is greater than positive 2.5, it means that there is a 99% probability that the indicator has a significant spatial clustering of the same value, and the next step of analysis can be carried out.

[0138] S104. Based on the spatial distribution characteristics, identify the core boundaries of the concentration of new employment and talent demand in strategic industries, quantify the degree of concentration of new employment and talent demand in strategic industries, and use bivariate spatial autocorrelation to analyze the economic vitality of the target area.

[0139] Furthermore, step S104 specifically includes:

[0140] S1041. Based on the kernel density analysis method, combined with the standard deviation tail value test method of the statistical normal distribution hypothesis, identify the core boundaries of the concentration of new employment and talent demand in the industry.

[0141] Kernel density analysis can identify the agglomeration area of ​​new employment point data, but it cannot quantify the degree of agglomeration. This method refers to the standard deviation tail value test method based on the statistical normal distribution hypothesis. In the data distribution, the range of mean ± σ contains about 68% of the total data volume, the range of mean ± 2σ contains about 95% of the total data volume, and the range of mean ± 3σ contains about 99% of the total data volume. Therefore, this embodiment is based on the kernel density analysis method, combined with the standard deviation tail value test method based on the statistical normal distribution hypothesis, to identify the agglomeration core boundaries of new employment and talent demand in the industry.

[0142] Furthermore, in ArcGIS Pro, a "kernel density analysis" was performed on the data of newly added employment points in the six categories of strategic industries, in which the parameter output pixel size was set to 50m to obtain a smoother boundary, and the kernel density analysis files of the newly added employment in the six categories of strategic industries, namely "KernelD_Green Petrochemical Industry" and other files, were obtained; the "Reclassification" tool was selected to classify the density analysis files of the six categories of strategic industries based on standard deviation, with an interval size of one standard deviation, to obtain the reclassified files. In the symbol system of the reclassification file, the color with the lowest value is set to none, and the other three values ​​are visualized in the same color system from small to large and from light to dark, and the "Standard Deviation Surface" field is added to the attribute table, and the values ​​are assigned in ascending order of "1sd standard deviation surface", "2sd standard deviation surface", and "3sd standard deviation surface", such as Figure 7a to Figure 7f shown.

[0143] S1042. Using the standard deviation surface as the statistical scope, calculate the concentration level of new employment and talent demand in the industry.

[0144] This embodiment uses the obtained standard deviation surface as the statistical range to calculate the degree of data concentration within each boundary, and specifically takes the 1sd standard deviation surface as an example to quantify the degree of new employment concentration in six strategic industries.

[0145] Furthermore, the “Raster to Polygon” tool was selected in ArcGIS Pro to convert the reclassified files of new employment in the six strategic industries into the surface data of new employment in the six strategic industries, namely, “new employment data of green petrochemical industry.shp”, “new employment data of automobile industry.shp”, “new employment data of software and information service industry.shp”, “new employment data of biomedicine and health industry.shp”, “new employment data of new generation electronic information industry.shp”, and “new employment data of advanced materials industry.shp”; the “Spatial Join” tool was used to count the amount of new employment data of the corresponding strategic industries on the 1sd standard deviation surface based on a one-to-one join operation, as shown in Table 2.

[0146] Table 2 Distribution characteristics of new employment in six strategic industries based on standard deviation

[0147]

[0148]

[0149] S1043. Taking the strategic industrial enterprises and newly created employment in the target area as the first and second variables, the bivariate global spatial autocorrelation is used to measure the overall spatial correlation of the two variables, and the bivariate local spatial autocorrelation is used to further analyze the spatial correlation and distribution pattern of the two variables.

[0150] Bivariate spatial autocorrelation can explore the spatial distribution characteristics of a certain attribute and the degree of clustering between variables. It is divided into two categories: bivariate global spatial autocorrelation (Global Moran's I, GMI) and bivariate local spatial autocorrelation (Local Indicators of Spatial Association, LISA).

[0151] Furthermore, the global spatial autocorrelation of the two variables was analyzed through the global Moran's index to analyze the overall correlation of the two spatial data sets in the entire target area. The existing enterprises and new jobs in the strategic industries of the nine cities in the Greater Bay Area were taken as the first and second variables, and the global Moran's index in the Geoda software was used to measure the overall spatial correlation of the two variables. The bivariate local spatial autocorrelation was used to further analyze the spatial correlation and distribution pattern of the two variables. The results included high-high aggregation, low-low aggregation, high-low aggregation, low-high aggregation, and insignificant.

[0152] S1045. By overlaying the data on strategic industry enterprises and newly created jobs, we can obtain the hot spots and potential areas for newly created jobs in strategic industries.

[0153] This embodiment, by superimposing strategic industry enterprises and new employment data, observes the spatial connection between high-high and low-high agglomeration areas, and studies the hot spots and potential areas of new employment in related science and technology innovation industries. Hot spots are areas where the number of enterprises and the number of new employment are both high, and new employment agglomeration is generated due to the agglomeration of enterprises. Potential areas are areas with a small number of enterprises but a large number of new employment, and more employment opportunities are generated due to the development of enterprises.

[0154] Furthermore, the enterprise POI and new employment point data of the six types of strategic industries were imported into Geoda, and the spatial weight matrix was constructed. The OBJECTID variable was selected as the adjacent spatial weight. Then, the bivariate Moran's I in the spatial analysis tool was used for analysis. The first variable was set as the strategic industry enterprise, and the second variable was set as the new employment in the strategic industry. The reliability and validity of the calculation results were verified by the P value and Z value, as shown in the following figure. Figure 8a to Figure 8f shown.

[0155] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0156] Embodiment 2:

[0157] like Fig. 9As shown, this embodiment provides a system for measuring new employment and economic vitality in urban areas, which includes an acquisition module 901, a preprocessing module 902, a calculation module 903 and a measurement module 904. The specific functions of each module are as follows:

[0158] An acquisition module 901 is used to acquire original data of a target area, wherein the original data includes job data, company address data structured by job data, administrative division data, new employment data of strategic industries, and enterprise POI data;

[0159] The preprocessing module 902 is used to preprocess the acquired raw data to obtain preprocessed data;

[0160] The calculation module 903 is used to calculate the density index and spatial distribution characteristics of new employment and talent demand of strategic industries in the target area based on the preprocessed data;

[0161] The measurement module 904 is used to identify the core boundaries of the agglomeration of new employment and talent demand in strategic industries based on spatial distribution characteristics, quantify the degree of agglomeration of new employment and talent demand in strategic industries, and analyze the economic vitality of the target region using bivariate spatial autocorrelation.

[0162] It should be noted that the system provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0163] Embodiment 3:

[0164] This embodiment provides a computer device, such as Fig.10 As shown, it includes a processor 1002, a memory, an input device 1003, a display device 1004 and a network interface 1005 connected through a system bus 1001. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 1006 and an internal memory 1007. The non-volatile storage medium 1006 stores an operating system, a computer program and a database. The internal memory 1007 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 1002 executes the computer program stored in the memory, the method for measuring new employment and economic vitality in the urban area of ​​the above-mentioned embodiment 1 is implemented as follows:

[0165] The original data of the target area is obtained, and the original data includes job data, company address data structured by job data, administrative division data, strategic industry statistical caliber data and enterprise POI data; the obtained original data is preprocessed to obtain preprocessed data; based on the preprocessed data, the density index and spatial distribution characteristics of the new employment and talent demand of the strategic industries in the target area are calculated; based on the spatial distribution characteristics, the core boundaries of the agglomeration of the new employment and talent demand of the strategic industries are identified, the agglomeration degree of the new employment and talent demand of the strategic industries is quantified, and the economic vitality of the target area is analyzed using bivariate spatial autocorrelation.

[0166] Embodiment 4:

[0167] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, the method for measuring new employment and economic vitality in an urban area in the above embodiment 1 is implemented as follows:

[0168] The original data of the target area is obtained, and the original data includes job data, company address data structured by job data, administrative division data, strategic industry statistical caliber data and enterprise POI data; the obtained original data is preprocessed to obtain preprocessed data; based on the preprocessed data, the density index and spatial distribution characteristics of the new employment and talent demand of the strategic industries in the target area are calculated; based on the spatial distribution characteristics, the core boundaries of the agglomeration of the new employment and talent demand of the strategic industries are identified, the agglomeration degree of the new employment and talent demand of the strategic industries is quantified, and the economic vitality of the target area is analyzed using bivariate spatial autocorrelation.

[0169] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0170] In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device, or device. In this embodiment, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium, which may send, propagate, or transmit a program used by or in combination with an instruction execution system, device, or device. The computer program contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0171] The computer readable storage medium can be written in one or more programming languages ​​or a combination thereof to execute the computer program of the present embodiment, and the programming language includes an object-oriented programming language, such as Java, Python, C++, and also includes a conventional procedural programming language, such as C language or a similar programming language. The program can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0172] In summary, the present invention uses new employment as an indicator to conduct a quantitative study on the increment of urban employment space, and analyzes the economic vitality of urban areas by comparing the spatial autocorrelation results of the increment and the stock. The job data and enterprise data required by this method are open source data and have good generalizability.

[0173] The above description is only a preferred embodiment of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention.

Claims

1. A method for measuring new employment and economic vitality in urban areas, characterized in that: The method comprises: Acquire the original data of the target area, wherein the original data includes job data, company address data structured by job data, administrative division data, strategic industry statistical caliber data and enterprise POI data; Preprocessing the acquired raw data to obtain preprocessed data; Based on the pre-processed data, calculate the density index and spatial distribution characteristics of new employment and talent demand in the strategic industries of the target area; Based on the spatial distribution characteristics, the core boundaries of the concentration of new employment and talent demand in strategic industries are identified, the degree of concentration of new employment and talent demand in strategic industries is quantified, and the economic vitality of the target region is analyzed using bivariate spatial autocorrelation.

2. The method for measuring new employment and economic vitality in urban areas according to claim 1 is characterized in that: The preprocessing of the acquired raw data to obtain preprocessed data specifically includes: Merge and clean the job data; Convert the company address data into the longitude and latitude coordinates of the Amap coordinate system, and perform verification and correction; Merge the administrative division data and divide it into N*N grids; According to the statistical caliber data of strategic industries, the data on newly added employment in strategic industries are screened; Classify enterprise POI data according to strategic industries, and convert the addresses of enterprise POI data into longitude and latitude coordinates of the Amap coordinate system.

3. The method for measuring new employment and economic vitality in urban areas according to claim 1 is characterized in that: The density index and spatial distribution characteristics of new employment and talent demand in strategic industries in the target area are calculated based on the pre-processed data, including: The density of new employment and talent demand per unit area and the density of new employment and talent demand per unit population are used as density indicators; The location entropy is used to characterize the concentration of certain recruitment positions in the grid as a distribution indicator; Standard deviation ellipse analysis and spatial autocorrelation analysis are performed on the new employment / talent demand to obtain spatial distribution characteristics. The standard deviation ellipse includes the ellipse center, azimuth and standard deviation. The spatial autocorrelation analysis includes global Moran's index calculation for the new employment and talent demand.

4. The method for measuring new employment and economic vitality in urban areas according to claim 3 is characterized in that: The calculation formula of the density index is as follows: Among them, D i The density of new employment or talent demand per unit area. is the density of new employment or talent demand per unit population, N i is the number of recruitment positions in the sample, S is the sample area, N p is the number of permanent residents in the sample, N a is the county-level population in the census, s a is the area corresponding to the county.

5. The method for measuring new employment and economic vitality in urban areas according to claim 3 is characterized in that: The calculation formula of the distribution index is as follows: Among them, Q is the location entropy, S is the ratio of the number of jobs in a grid area to the total permanent population, and P is the ratio of the number of jobs in the target area to the total permanent population; if Q=1, the concentration level of this type of working population in the grid is the same as the level of the entire city; if Q<1, the concentration level of this type of working population in the grid is lower than the level of the entire city; if Q>1, the concentration level of this type of working population in the grid is higher than the level of the entire city.

6. The method for measuring new employment and economic vitality in urban areas according to claim 3 is characterized in that: The calculation formula of the ellipse center is as follows: The calculation formula of the azimuth angle is as follows: The standard deviation is calculated as follows: Among them, (X i , Y i ) represents the coordinates of point data i (i = 1, 2, 3 ... n), W i represents weight; The calculation formula of the global Moran index is as follows: Where n is the number of spatial units, y i ,y j are the observation values ​​of the i-th and j-th spatial units, respectively. is the mean value of the observed values, w ij is the spatial weight value.

7. The method for measuring new employment and economic vitality in urban areas according to any one of claims 3 to 6, characterized in that: According to the spatial distribution characteristics, the core boundaries of the agglomeration of new employment and talent demand in strategic industries are identified, the degree of agglomeration of new employment and talent demand in strategic industries is quantified, and the economic vitality of the target area is analyzed using bivariate spatial autocorrelation, including: Based on the kernel density analysis method, combined with the standard deviation tail value test method of the statistical normal distribution hypothesis, the core boundaries of the agglomeration of new industrial employment and talent demand are identified; Using the standard deviation surface as the statistical scope, calculate the concentration of new employment and talent demand in the industry; Taking the strategic industry enterprises and new employment in the target area as the first and second variables, the bivariate global spatial autocorrelation is used to measure the overall spatial correlation of the two variables, and the bivariate local spatial autocorrelation is used to further analyze the spatial correlation and distribution pattern of the two variables. By superimposing the data on strategic industry enterprises and new employment, we can obtain the hot spots and potential areas for new employment in strategic industries.

8. A system for measuring new employment and economic vitality in urban areas, characterized in that: The system comprises: An acquisition module is used to acquire the original data of the target area, wherein the original data includes job data, company address data structured by job data, administrative division data, new employment data of strategic industries and enterprise POI data; A preprocessing module is used to preprocess the acquired raw data to obtain preprocessed data; A calculation module is used to calculate the density index and spatial distribution characteristics of new employment and talent demand in strategic industries in the target area based on the preprocessed data; The measurement module is used to identify the core boundaries of the concentration of new employment and talent demand in strategic industries based on spatial distribution characteristics, quantify the degree of concentration of new employment and talent demand in strategic industries, and analyze the economic vitality of the target area using bivariate spatial autocorrelation.

9. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, it implements the method for measuring new employment and economic vitality in urban areas as described in any one of claims 1-7.

10. A storage medium storing a program, characterized in that: When the program is executed by a processor, the method for measuring new employment and economic vitality in an urban area as described in any one of claims 1 to 7 is implemented.

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