Job-housing balance evaluation method giving consideration to job-housing quantity balance and type matching

Abstract

The invention relates to the technical field of job-housing evaluation, in particular to a job-housing balance evaluation method considering job-housing quantity balance and type matching. The method comprises the steps of firstly obtaining a commuting data table, a housing price data set and an enterprise data set; fitting a distance attenuation function according to the commuting linear distance and the corresponding number of people; creating vector points at equal intervals, distributing the number of people in the commuting data table to the vector points according to a residence place and a working place, and determining the supply and demand of employment posts of the vector points; determining a job-housing quantity balance index of each space unit; determining a job-housing type matching index of each space unit in combination with the average housing price and the average post salary in the space unit; and classifying the space units according to the job-housing quantity balance index and the job-housing type matching index. According to the invention, the current job-housing balance situation of the space unit is comprehensively evaluated from the angles of job-housing supply and demand balance and salary matching, so that the job-housing balance evaluation is more comprehensive.

Description

Technical Field

[0001] The present invention relates to the technical field of job-housing assessment, and in particular to a job-housing balance evaluation method that takes into account both job-housing quantity balance and type matching. Background Art

[0002] Job-housing balance means that within a given geographical area, the number of workers and the number of jobs among residents are roughly equal, which helps to reduce commuting distances. However, to truly achieve short-distance commuting, matching workers with nearby jobs is also an indispensable condition. For more and more workers, even if there are jobs near their place of residence, they need to go to more distant areas for employment because their skills do not match the job requirements near their place of residence. Therefore, job-housing balance needs to be evaluated from both quantitative balance and type matching.

[0003] As for the specific measurement methods of job-housing balance, although there are a large number of algorithms, most of them divide the research scope into multiple spatial units, and independently calculate the job-housing balance index of each spatial unit based on the number of residents and jobs in the spatial unit. There are several problems with this method. First, it only considers whether the number of jobs and residences is balanced but not whether the types match; second, it does not consider the job-housing connection between spatial units, and the calculation results are greatly affected by the way the spatial units are divided. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for evaluating job-housing balance that takes into account both the job-housing quantity balance and type matching. The technical solution adopted is as follows: An embodiment of the present invention provides a method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching, the method comprising: Get the commuting data table, housing price data set and enterprise data set; the commuting data table contains the location of residence, workplace and number of people for each commuting flow line; Determine the commuting straight-line distance according to the straight-line distance between the residence and the workplace; fit a distance decay function according to the commuting straight-line distance and the corresponding number of people, wherein the distance decay function is a function that characterizes the decay of the influence of the workplace and residence with distance; Create equally spaced vector points, assign the number of people in the commuting data table to the vector points based on their place of residence and place of work, and determine the supply and demand of jobs at the vector points; spatially connect the vector points with the supply and demand of jobs to determine the job-housing balance index of each spatial unit; Combine the average housing price and average job salary in the space unit to determine the job-housing matching index of each space unit; The spatial units are classified according to the job-housing quantity balance index and the job-housing type matching index.

[0005] Preferably, the housing price data set includes data on community location, number of housing units and average housing price; and the enterprise data set includes data on enterprise location, average salary and number of employees.

[0006] Preferably, determining the commuting straight-line distance according to the straight-line distance between the residence and the workplace includes: Calculate the straight-line distance between the residence and the workplace as the commuting distance; divide the commuting distance into equal intervals, and count the number of people corresponding to each commuting distance interval; assign the midpoint value of each commuting distance interval to the interval as the commuting straight-line distance of the interval.

[0007] Preferably, fitting a distance decay function according to the commuting straight-line distance and the corresponding number of people includes: Take the commuting straight-line distance as the independent variable and the number of people as the dependent variable to fit the function. Select the exponential function, power function, and logarithmic function models for fitting respectively, and select the function model f(d) with the goodness of fit r² closest to 1; The function model f(d) is transformed into a form suitable for two-dimensional plane calculation, and the function obtained after two-dimensional plane transformation is as a distance decay function.

[0008] Preferably, the step of creating equally spaced vector points, allocating the number of people in the commuting data table to the vector points according to their place of residence and place of work, and determining the supply and demand of jobs at the vector points includes: Among them, the supply and demand of jobs include: the supply of jobs and the demand for jobs; Use the distance decay function to allocate the number of people in the commuting data table to vector points according to their workplaces and residences, and determine the supply and demand of jobs at the vector points; The calculation formula for the supply of jobs at a vector point is: ; Among them, S is the supply of jobs at the vector point; is the straight-line distance between the vector point and the workplace in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function; The calculation formula for the job demand at the vector point is: ; Among them, D is the job demand at the vector point; is the straight-line distance between the vector point and the residence in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function.

[0009] Preferably, the step of spatially connecting the vector points with the supply and demand of job positions to determine the job-housing quantity balance index of each spatial unit includes: Connect the vector points with the spatial units for which the job-housing quantity balance index and job-housing type matching index need to be calculated in the same coordinate system, and add the id field of the spatial unit where the vector points are located. The attribute table of the connected vector points needs to include four fields: vector point id, spatial unit id, job supply, and job demand. The job supply S and job demand D of each vector point in each spatial unit are calculated by summarizing the spatial unit ID field to obtain the job supply of the spatial unit. and job demand ; The function model of the job-housing balance index is: ; in, The job-housing balance index; The supply of jobs for the space unit; The number of jobs required for a space unit.

[0010] Preferably, the steps for obtaining the average housing price within a spatial unit are: Calculate the mean housing price of the housing opportunities accumulated by the vector point from the surrounding areas. The calculation formula is: ; in, is the mean of the housing prices of the housing opportunities accumulated from the surrounding areas by the i-th vector point in the spatial unit; is the straight-line distance between the i-th vector point and the centroid of the j-th residential area; The independent variable is The output value of the distance decay function; is the number of housing units in the jth residential area; is the housing price of the jth residential area; J is the number of residential areas in the spatial unit; right Perform range normalization to obtain the vector point standardized average housing price ; Calculate the corresponding vector points in each space unit The average of the values ​​is taken as the average housing price of each spatial unit.

[0011] Preferably, the steps for obtaining the average job salary in the space unit are: Calculate the average salary of the jobs that the vector point can obtain from the surrounding area. The calculation formula is: ; in, is the average job salary that the i-th vector point in the spatial unit can obtain from the surrounding areas; is the straight-line distance between the i-th vector point and the centroid of the location of the j-th enterprise; The independent variable is The output value of the distance decay function; is the number of employees in the k-th enterprise; is the average salary of the kth enterprise; K is the number of enterprises in the spatial unit; right Perform range normalization to obtain the average job salary after vector table normalization ; Calculate the corresponding vector points in each space unit The average of the values ​​is taken as the average job salary of each spatial unit.

[0012] Preferably, the combining of the average housing price and the average job salary in the spatial unit to determine the job-housing type matching index of each spatial unit includes: ; in, is the job-residence type matching index; is the average job salary of the space unit; is the average housing price per space unit.

[0013] Preferably, the classifying of the spatial units according to the job-housing quantity balance index and the job-housing type matching index includes: classifying the spatial units into 9 types, and formulating job-housing balance strategies according to the specific type corresponding to each spatial unit.

[0014] The embodiments of the present invention have at least the following beneficial effects: Based on the full sample data within the research scope, a distance attenuation function of the distribution of commuting number with commuting straight-line distance was fitted, and it was used as the distance attenuation function of the intensity of the impact of work-residence on surrounding areas. This function can be used to allocate various attributes of work-residence to the surrounding space, avoiding the errors caused by direct statistics by spatial units; the job-residence quantity balance is examined from the perspective of job-residence supply and demand balance, and the job-residence type matching is examined from the perspective of housing prices and job salary matching. The current status of job-residence balance in spatial units is comprehensively evaluated from these two perspectives. This is more comprehensive than the evaluation from the single perspective of quantity balance in previous studies, and the more detailed classification evaluation results are convenient for targeted measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 these drawings without paying creative work.

[0016] Figure 1 A method flow chart of a method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching provided by one embodiment of the present invention; Figure 2 A flow chart of a method for fitting a distance decay function provided by an embodiment of the present invention; Figure 3 A schematic diagram of a one-dimensional relationship between a commuting straight-line distance and a number of people distribution, and a two-dimensional circular plane corresponding to a one-dimensional commuting straight-line distance interval, provided by an embodiment of the present invention; Figure 4 A schematic diagram of dividing a commuting straight-line distance interval on a two-dimensional plane into a large number of grids of equal area provided by an embodiment of the present invention; Figure 5 A flow chart of a method for determining the job-housing quantity balance index of each space unit provided by one embodiment of the present invention; Figure 6 A schematic diagram of a job-residence balance evaluation result based on a job-residence quantity balance index and a job-residence type matching index provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation method, structure, characteristics and effects of a job-residence balance evaluation method that takes into account the job-residence quantity balance and type matching proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0018] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Among them, in the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0020] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The residence data, workplace data, commuter data, housing price data, housing unit data, job salary data, and enterprise employee data involved in this application are all obtained with full consent and authorization, and the collection, use and processing of relevant information must comply with relevant laws, regulations and standards of relevant countries and regions.

[0022] The embodiments of the present invention are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0023] The embodiment of the present invention provides a specific implementation method of a method for evaluating job-residence balance that takes into account both the job-residence quantity balance and the type matching, and the method is applicable to job-residence balance evaluation scenarios. In order to solve the problem that only whether the job-residence quantity is balanced but not whether the type matches, and not considering the job-residence connection between spatial units, the calculation results are greatly affected by the way the spatial units are divided. Under this method, two sets of indicators, job-residence quantity balance and type matching, are calculated simultaneously, and the influence of the residential areas and jobs around the spatial unit is also taken into account in the calculation of the spatial unit through the distance attenuation function, which can avoid the above two problems and obtain a more accurate and comprehensive job-residence balance evaluation result.

[0024] The following is a detailed description of a specific scheme of a method for evaluating job-housing balance that takes into account both job-housing quantity balance and type matching provided by the present invention with reference to the accompanying drawings.

[0025] See also Figure 1 , which shows a flowchart of a method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching, provided by an embodiment of the present invention. The method includes the following steps: Step S100, obtaining a commuting data table, a housing price data set, and an enterprise data set; wherein the commuting data table includes the residence location, workplace location, and number of people of each commuting flow line.

[0026] Obtain a commuting data table within the city area obtained by processing the mobile phone signaling data. Please refer to Table 1, which is a sample commuting data table.

[0027] Table 1 The commuting data table is placed in the geographic coordinate system according to the longitude and latitude of the residence and the workplace, and then projected into the projected coordinate system to generate the xy coordinates of the residence and workplace. The commuting data table contains the location of the residence, the location of the workplace, and the number of people for each commuting flow line. It is placed and projected into the projected coordinate system, and plane coordinates are added to it, so that it includes five fields: x coordinate of residence, y coordinate of residence, x coordinate of workplace, y coordinate of workplace, and number of people.

[0028] Please refer to Table 2, which is a sample commuting data table with the plane coordinates of residence and workplace added.

[0029]

[0030] Table 2 Get the housing price dataset and enterprise dataset. The housing price dataset includes the location of the community, the number of housing units and the average housing price data, and the enterprise dataset includes the location of the enterprise, the average salary and the number of employees data. It should be noted that the average housing price data here refers to the average housing price of the community, because different house types or different sizes of apartments in a community may lead to differences in the housing prices of the community, so the average of the housing prices in the community is used as the average housing price of the community. The same processing is done for the obtained housing price dataset and enterprise dataset, and coordinates are added after the point is dropped in space.

[0031] Please refer to Table 3, which is a sample housing price data table with plane coordinates added.

[0032]

[0033] Table 3 Please refer to Table 4, which is a sample enterprise data table with plane coordinates added.

[0034]

[0035] Table 4 Step S200, determining the commuting straight-line distance according to the straight-line distance between the residence and the workplace; fitting a distance decay function according to the commuting straight-line distance and the corresponding number of people, wherein the distance decay function is a function that characterizes the attenuation of the influence of work and residence with distance.

[0036] Calculate the straight-line distance between residence and workplace in the commuting data table as the commuting straight-line distance, count the number of people by segment according to the commuting straight-line distance, fit the function of the attenuation of the influence of residence and work with distance, and transform it into a form suitable for two-dimensional plane calculation.

[0037] In some embodiments, the method for fitting the distance decay function is specific, that is, the above step S200 can be performed by Figure 2 The steps shown achieve: Step S210, determining the commuting straight-line distance according to the straight-line distance between the residence and the workplace.

[0038] The straight-line distance between the residence and the workplace is calculated as the commuting distance. Specifically: according to the plane coordinates of the residence and workplace added in the commuting data table in step S100, the commuting distance between the residence and the workplace is calculated and added to the commuting data table as a "commuting distance" field.

[0039] A field of "commuting distance" calculated based on the coordinates of the residence and workplace in Table 2 is added to the commuting data table, as shown in Table 5. Table 5 is a sample commuting data table with the commuting distance field added.

[0040]

[0041] Table 5 The commuting distance is divided into multiple commuting distance intervals at equal intervals, and the number of people corresponding to each commuting distance interval is counted. Specifically: the commuting distance in the commuting data table is used as the summary field, and each section is divided at a certain distance, and the total number of people in each section is counted to obtain a statistical table of commuting distance intervals and number of people, which includes two fields: commuting distance interval and number of people.

[0042] In some embodiments of the present invention, the commuting straight-line distance is segmented into segments at intervals of a meters (m), and each commuting distance interval and the number of people corresponding to each commuting distance interval are counted; please refer to Table 6, which is a sample table of commuting distance interval and number of people.

[0043]

[0044] Table 6 The midpoint value of each commuting distance interval is assigned to the interval as the commuting straight-line distance of the interval. In the obtained statistical table of commuting distance intervals and number of people, the midpoint value of each commuting distance interval is assigned to the commuting distance interval as the "commuting straight-line distance" field.

[0045] A new "Commuting Straight Line Distance" field is created in Table 6. The value of the field is the midpoint value of the commuting distance interval. After the values ​​are assigned, they are shown in Table 7.

[0046]

[0047] Table 7 Step S220, fitting a distance decay function according to the commuting straight-line distance and the corresponding number of people.

[0048] The commuting straight-line distance is used as the independent variable and the number of people is used as the dependent variable to fit the function. The function is fitted with the help of a computer program. The exponential function, power function, and logarithmic function models are selected for fitting respectively, and the function model f(d) with the goodness of fit r² closest to 1 is selected; among them, in order to prevent overfitting, the first x rows of commuting straight-line distance from small to large are selected for fitting, and the number of people participating in the fitting accounts for more than 95% of the total number of people.

[0049] The function model f(d) is transformed into a form suitable for two-dimensional plane calculation, and the function obtained after two-dimensional plane transformation is as a distance decay function.

[0050] The function f(d) obtained in the previous step reflects the overall distribution of commuting straight-line distance and number of people within the research scope, and can be used as a distance attenuation function for the intensity of the impact of the workplace and residence on the surrounding area. However, f(d) reflects the relationship between the midpoint value of the commuting straight-line distance interval on the straight line and the distribution of the number of people in the interval. Please refer to Figure 3 , Figure 3 The figure is a schematic diagram of the relationship between the one-dimensional commuting straight-line distance and the number of people, as well as the two-dimensional circular plane corresponding to the one-dimensional commuting straight-line distance interval. If it is applied to the two-dimensional plane analysis of the city, it needs to be converted into a two-dimensional form. The following will explain the method of converting a one-dimensional function into a two-dimensional form.

[0051] See also Figure 3 , Figure 3 Point O is the location of a job, a is the width of the annular area, the distance between the center line of the ring and point O is d; the distance between the inner ring and point O is ; The distance between the outer ring and point O is Assume that there is a job at point O, surrounded by a ring area with a width of a, and the distance between the center line of the ring and point O is d. Assume that the total impact intensity of a unit job on the surrounding area is 1, then The total impact intensity of jobs in this annular area is about ,Right now .

[0052] See also Figure 4 , Figure 4 This is a schematic diagram of dividing the commuting straight-line distance interval on a two-dimensional plane into a large number of grids with equal areas. The annular area is divided into a large number of grids with equal areas. The area of ​​each grid is s, and the distance between the center line of the ring and point O is d. Then the number of grids in the annular interval is , the employment impact intensity of each grid is ,Right now .because and s are constants. They will be eliminated by division in the subsequent calculation of the relative relationship between the number of jobs and residences and their types. Therefore, they can be omitted. The two-dimensionalization process can be simplified as follows: . That is the distance decay function, which is a function that characterizes how the influence of job residence decays with distance.

[0053] It reflects the intensity of influence of the residence location on a point with a commuting straight-line distance d from the workplace, or the intensity of influence of the workplace location on a point with a commuting straight-line distance d from the residence. is the function model of commuting straight-line distance-number of people fitted in step 210, and d is the straight-line distance between a certain spatial position and the workplace.

[0054] The range of the independent variable is set to (0, a×x], where a×x is the maximum value of the commuting distance interval involved in the distance decay function fitting in Table 7. The impact of residence or work beyond this distance range is negligible. The surrounding area in the subsequent steps is limited to a distance less than or equal to a from the center point. It should be noted that the x in a×x here is the same as the x in the fitting of the first x rows of commuting straight-line distances from small to large.

[0055] Step S300, create equally spaced vector points, assign the number of people in the commuting data table to the vector points according to residence and work place, determine the supply and demand of jobs at the vector points; spatially connect the vector points with the supply and demand of jobs, and determine the job-housing balance index of each spatial unit.

[0056] Create vector points with an interval of s covering the study area and add coordinate information to them. Please refer to Table 8, which is a sample attribute table of vector points after adding coordinates.

[0057]

[0058] Table 8 In some embodiments, the method for determining the job-housing balance index of each space unit is specific, that is, the above step S300 can be performed by Figure 5 The steps shown achieve: Step S310, creating equally spaced vector points.

[0059] Establish equidistant vector points, and allocate the number of people in the commuting data table to the vector points based on the distance decay function, with the residence and work place as the center. The number allocated by the work place is the supply of jobs, and the number allocated by the residence is the demand for jobs. Summarize the supply and demand of jobs at the vector points into spatial units, and calculate the job-housing balance index of each spatial unit.

[0060] First, create equally spaced vector points. Specifically: establish equally spaced vector points within the research scope to replace the grid centroid. The coordinate system uses the projection coordinate system generated by the commuting data table in step S100, and adds plane coordinate information to it. The vector point attribute table contains three fields: vector point id, x coordinate, and y coordinate.

[0061] Step S320, allocate the number of people in the commuting data table to vector points according to their place of residence and place of work, and determine the supply and demand of jobs at the vector points.

[0062] Among them, the supply and demand of jobs includes: job supply and job demand.

[0063] Assuming that jobs are evenly supplied to surrounding areas according to the distance decay function of the job-residence influence intensity distribution, and the residence also evenly generates job demand for surrounding areas according to the distance decay function, the supply and demand of jobs obtained can be obtained according to the straight-line distance between the vector point and the job-residence, and the job-residence is also the residence of the employee. Using the residence location, workplace location coordinates, and vector point coordinates in the commuting data table in step S100, the straight-line distance between the vector point and each residence and workplace can be calculated, and then the straight-line distance between the vector point and each residence and workplace can be used. The number of people in the commuting data table is allocated to vector points according to their workplaces and residences, which serve as the supply and demand of jobs at the vector points.

[0064] According to the coordinates in Table 2 of the commuting data table and Table 8 of the vector point attribute table, it is easy to find the distance between the vector point and the workplace and residence. , According to the formula of job supply and demand, the number of people in the commuting data table is divided into , Assigned to each vector point as the supply and demand of the job positions of the vector point. With the help of computer program, batch processing is performed, and the calculation results are shown in Table 9 and Table 10. Connect Table 9 and Table 10 to the vector point attribute table Table 8 according to the id. Please refer to Table 9 and Table 10. Table 9 is a sample table of the supply of job positions of the vector point, and Table 10 is a sample table of the demand of job positions of the vector point.

[0065]

[0066] Table 9

[0067] Table 10 The distance decay function is used to allocate the number of people in the commuting data table to vector points according to their workplaces and residences, and the supply and demand of jobs at the vector points are determined.

[0068] The calculation formula for the supply of jobs at a vector point is: ; Among them, S is the supply of jobs at the vector point; is the straight-line distance between the vector point and the workplace in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function; The calculation formula for the job demand at the vector point is: ; Among them, D is the job demand at the vector point; is the straight-line distance between the vector point and the residence in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function. The supply and demand of jobs at a vector point represents the supply and demand of jobs in the grid where it is located.

[0069] Step S330, spatially connect the vector points with the supply and demand of job positions to determine the job-housing quantity balance index of each spatial unit.

[0070] Connect the vector points with the spatial units for which the job-housing type matching index and job-housing quantity balance index need to be calculated in the same coordinate system, add the id field of the spatial unit where the vector points are located, and the attribute table of the connected vector points must include four fields: vector point id, spatial unit id, job supply, and job demand. Please refer to Table 11, which is the attribute table of the connected vector points.

[0071]

[0072] Table 11 In the vector point attribute table, the job supply S and job demand D of each vector point contained in each spatial unit are calculated and summarized according to the spatial unit id field to obtain the job supply of the spatial unit. and job demand That is, the job supply S of all vector points in the spatial unit is summed up to obtain the job supply of the spatial unit ; Sum the job demand D of all vector points in the spatial unit to obtain the job supply of the spatial unit Please refer to Table 12, which is a sample table of the supply and demand of space unit jobs.

[0073]

[0074] Table 12 The function model used to calculate the job-housing balance index is as follows: ; in, is the job-housing balance index of the space unit; j is the number of workers in the space unit; h is the number of residents in the space unit.

[0075] The number of jobs in a space unit is used instead of the number of workers. , using the number of jobs required per space unit instead of the number of residents , and the corresponding function model of the job-housing balance index of the spatial unit is obtained: ; in, is the job-housing quantity balance index of the spatial unit; The supply of jobs for the space unit; The number of jobs required for a space unit.

[0076] When the third job-housing quantitative balance index is equal to 0, it indicates that the supply and demand of jobs within the spatial unit are balanced; when the job-housing quantitative balance index is greater than 0, it indicates that the supply of jobs is greater than the demand for jobs; when the job-housing quantitative balance index is less than 0, it indicates that the demand for jobs is greater than the supply of jobs.

[0077] Please refer to Table 13, which is a sample table of the spatial unit job-housing quantity balance index.

[0078]

[0079] Table 13 Step S400, combining the average housing price and average job salary in the spatial unit, to determine the job-housing type matching index of each spatial unit.

[0080] Taking the enterprise as the center, the product of the number of employees and wages in the enterprise data set is distributed to vector points according to the distance decay function, and then aggregated to the spatial unit to measure the overall salary level of jobs in and around the spatial unit. The average housing price in the spatial unit is calculated using housing data, and then distributed to vector points according to the distance decay function, and then aggregated to the spatial unit. The above two data aggregated by spatial unit are respectively processed by range normalization, and the job-housing type matching index of each spatial unit is calculated.

[0081] The average housing price in a spatial unit is measured by the average housing price in the residential area. Using the distance decay function of the intensity distribution of the job-residence impact, the average housing price of available housing opportunities around the vector point is calculated. The method for obtaining the average housing price in a spatial unit is: Calculate the mean housing price of the housing opportunities accumulated by the vector point from the surrounding areas. The calculation formula is: ; in, is the mean of the housing prices of the housing opportunities accumulated from the surrounding areas by the i-th vector point in the spatial unit; is the straight-line distance between the i-th vector point and the centroid of the j-th residential area; The independent variable is The output value of the distance decay function; is the number of housing units in the jth residential area; is the housing price of the jth residential area; J is the number of residential areas in the spatial unit; Then, yes Perform range normalization to obtain the vector point standardized average housing price .

[0082] Please refer to Table 14, which is a statistical table of the standardized average housing prices of vector points.

[0083]

[0084] Table 14 In some embodiments of the present invention, The calculation formula is: ;in, is the mean of the housing prices of the housing opportunities accumulated from the surrounding areas by the i-th vector point in the spatial unit; It is the minimum value of the housing price of all vector points within the surrounding area of ​​the study; is the maximum value of the housing prices of all vector points within the scope of the studied surrounding area. It should be noted that the surrounding area here takes into account the problem that some functions can be allocated at infinite distances, so in the embodiment of the present invention, a maximum circular space range of allocation is set as the surrounding area, and the radius of the maximum circular space range is determined according to the maximum commuting distance involved in the fitting in step S220. This maximum circular space range can include more than 95% of the commuting behaviors from the vector point to and from.

[0085] Furthermore, the corresponding vector points in each space unit are calculated. The average of the values ​​is taken as the average housing price of each spatial unit.

[0086] Calculate the normalized range of each vector point in the spatial unit The resulting value is taken as the average housing price of the spatial unit.

[0087] The average housing price is calculated as follows: ; is the average housing price per space unit, is the number of vector points in the space unit.

[0088] Please refer to Table 15, which is a sample statistical table of average housing prices per spatial unit.

[0089]

[0090] Table 15 Average job salary in a spatial unit is measured by the average salary of a job. Using the distance decay function of the job-residence impact intensity distribution, the average job salary of the available job opportunities around the vector point is calculated. The calculation formula is: ; in, is the average job salary of the i-th vector point in the spatial unit that can obtain employment opportunities from the surrounding areas; is the straight-line distance between the i-th vector point and the centroid of the location of the j-th enterprise; The independent variable is The output value of the distance decay function; is the number of employees in the k-th enterprise; is the average salary of the kth enterprise; K is the number of enterprises in the spatial unit.

[0091] right Perform range normalization to obtain the average job salary after vector table normalization ; Calculate the corresponding vector points in each space unit The average of the values ​​is taken as the average job salary of each spatial unit.

[0092] Please refer to Table 16, which is a sample statistical table of average job salaries after vector table standardization.

[0093]

[0094] Table 16 In some embodiments of the present invention, The calculation formula is: ;in, is the average job salary that the i-th vector point in the spatial unit can obtain from the surrounding areas; It is the minimum salary of all vector points in the surrounding area of ​​the study; It is the maximum value of the job salary of all vector points within the surrounding area of ​​the study.

[0095] In some embodiments of the present invention, ; Where n is the number of vector points in the space unit; is the average job salary of the space unit.

[0096] Please refer to Table 17, which is a sample statistical table of average job salaries for space units.

[0097]

[0098] Table 17 After determining the average housing price and average job salary within the spatial unit, the job-residence type matching index of each spatial unit is determined based on the average housing price and average job salary within the spatial unit.

[0099] The calculation formula for the job-residence matching index is: ; in, is the job-residence type matching index; is the average job salary of the space unit; is the average housing price per space unit.

[0100] Please refer to Table 18, which is a sample statistical table of the job-residence type matching index of the spatial unit.

[0101]

[0102] Table 18 Step S500: classify the space units according to the job-residence quantity balance index and the job-residence type matching index.

[0103] Classify the spatial units according to the job-housing quantity balance index and the job-housing type matching index, conduct a job-housing balance evaluation, and identify the types of job-housing imbalance problems in the spatial units.

[0104] The job-housing balance index and the job-housing type matching index are classified. Referring to the commonly used classification interval of the job-housing ratio, it is generally believed that the job-housing ratio is between 0.8 and 1.2, which is converted into a job-housing balance index of about -0.11-0.09. Therefore, ±0.1 is used as the classification breakpoint of the job-housing balance index and the job-housing type matching index. The specific classification method is as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of the job-housing balance evaluation results based on the job-housing quantity balance index and the job-housing type matching index. After the spatial units are divided into 9 categories, job-housing balance countermeasures can be formulated according to the specific type corresponding to each spatial unit.

[0105] Please refer to Table 19, which is a sample table of work-life balance evaluation results.

[0106]

[0107] Table 19 It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0109] The above contents are only specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating job-housing balance that takes into account both job-housing quantity balance and type matching, characterized in that: The method comprises the following steps: Get the commuting data table, housing price data set and enterprise data set; the commuting data table contains the location of residence, workplace and number of people for each commuting flow line; Determine the commuting straight-line distance according to the straight-line distance between the residence and the workplace; fit a distance decay function according to the commuting straight-line distance and the corresponding number of people, wherein the distance decay function is a function that characterizes the decay of the influence of the workplace and residence with distance; Create equally spaced vector points, assign the number of people in the commuting data table to the vector points based on their place of residence and place of work, and determine the supply and demand of jobs at the vector points; spatially connect the vector points with the supply and demand of jobs to determine the job-housing balance index of each spatial unit; Combine the average housing price and average job salary in the space unit to determine the job-housing matching index of each space unit; The spatial units are classified according to the job-housing quantity balance index and the job-housing type matching index.

2. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The housing price data set includes data on the location of the community, the number of housing units and the average housing price; the enterprise data set includes data on the location of the enterprise, the average salary and the number of employees.

3. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: Determining the commuting straight-line distance based on the straight-line distance between the residence and the workplace includes: Calculate the straight-line distance between the residence and the workplace as the commuting distance; divide the commuting distance into equal intervals, and count the number of people corresponding to each commuting distance interval; assign the midpoint value of each commuting distance interval to the interval as the commuting straight-line distance of the interval.

4. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The distance decay function is fitted according to the commuting straight-line distance and the corresponding number of people, including: Take the commuting straight-line distance as the independent variable and the number of people as the dependent variable to fit the function. Select the exponential function, power function, and logarithmic function models for fitting respectively, and select the function model f(d) with the goodness of fit r² closest to 1; The function model f(d) is transformed into a form suitable for two-dimensional plane calculation, and the function obtained after two-dimensional plane transformation is as a distance decay function.

5. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The method creates equally spaced vector points, allocates the number of people in the commuting data table to the vector points according to their place of residence and place of work, and determines the supply and demand of jobs at the vector points. include: Among them, the supply and demand of jobs include: the supply of jobs and the demand for jobs; Use the distance decay function to allocate the number of people in the commuting data table to vector points according to their workplaces and residences, and determine the supply and demand of jobs at the vector points; The calculation formula for the supply of jobs at a vector point is: ; Among them, S is the supply of jobs at the vector point; is the straight-line distance between the vector point and the workplace in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function; The calculation formula for the job demand at the vector point is: ; Among them, D is the job demand at the vector point; is the straight-line distance between the vector point and the residence in the commuting record in the i-th row of the commuting data table; is the number of people in the commuting record in row i of the commuting data table; n is the number of rows in the commuting data table; The independent variable is The output value of the distance decay function.

6. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The vector points are spatially connected with the supply and demand of jobs to determine the job-housing balance index of each spatial unit, including: Connect the vector points with the spatial units for which the job-housing quantity balance index and job-housing type matching index need to be calculated in the same coordinate system, and add the id field of the spatial unit where the vector points are located. The attribute table of the connected vector points needs to include four fields: vector point id, spatial unit id, job supply, and job demand. The job supply S and job demand D of each vector point in each spatial unit are calculated by summarizing the spatial unit ID field to obtain the job supply of the spatial unit. and job demand ; The function model of the job-housing balance index is: ; in, The job-housing balance index; The supply of jobs for the space unit; The number of jobs required for a space unit.

7. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The steps to obtain the average housing price within a spatial unit are: Calculate the mean housing price of the housing opportunities accumulated by the vector point from the surrounding areas. The calculation formula is: ； in, is the mean of the housing prices of the housing opportunities accumulated from the surrounding areas by the i-th vector point in the spatial unit; is the straight-line distance between the i-th vector point and the centroid of the j-th residential area; The independent variable is The output value of the distance decay function; is the number of housing units in the jth residential area; is the housing price of the jth residential area; J is the number of residential areas in the spatial unit; right Perform range normalization to obtain the vector point standardized average housing price ; Calculate the corresponding vector points in each space unit The average of the values ​​is taken as the average housing price of each spatial unit.

8. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The steps to obtain the average job salary within the space unit are: Calculate the average salary of the jobs that the vector point can obtain from the surrounding area. The calculation formula is: ； in, is the average job salary that the i-th vector point in the spatial unit can obtain from the surrounding areas; is the straight-line distance between the i-th vector point and the centroid of the location of the j-th enterprise; The independent variable is The output value of the distance decay function; is the number of employees in the k-th enterprise; is the average salary of the kth enterprise; K is the number of enterprises in the spatial unit; right Perform range normalization to obtain the average job salary after vector table normalization ; Calculate the corresponding vector points in each space unit The average of the values ​​is taken as the average job salary of each spatial unit.

9. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The above-mentioned combination of the average housing price and the average job salary in the spatial unit determines the job-housing type matching index of each spatial unit, including: ； in, is the job-residence type matching index; is the average job salary of the space unit; is the average housing price per space unit.

10. The method for evaluating job-housing balance that takes into account job-housing quantity balance and type matching according to claim 1 is characterized in that: The classifying of the spatial units according to the job-housing quantity balance index and the job-housing type matching index includes: classifying the spatial units into 9 types, and formulating job-housing balance strategies according to the specific types corresponding to each spatial unit.

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