Urban population spatialization correction method under support of weight reconciliation building table data

Abstract

The invention provides an urban population spatialization correction method under the support of authority reconciliation building table data, which comprises the following steps of: S1, determining an area range and a space reference for implementing correction, and obtaining area boundary data, authority survey data, population spatialization result data and population statistical data; s2, determining a statistical grid; s3, unifying spatial references of the region boundary data, the authority survey data and the population spatialization result data; s4, cutting population spatialization result data; s5, matching the right survey data, the cut population spatialization result data and the statistical grid to obtain grid population data and grid building data; s6, calculating the per household population of the grid; and step S7, correcting the grid population data. According to the method, the influence of signaling data quality, a residence time threshold value, uneven base station density, a space threshold value and the like on urban population spatialization can be eliminated, a regional population data spatialization result is corrected, and related city updating work is supported.

Description

Technical Field

[0001] The invention relates to the field of surveying and mapping geographic information technology, in particular to a method for spatial correction of urban population supported by weight adjustment and building table data. Background Art

[0002] Real estate title investigation is carried out on plots of land and sea as units, with the goal of clarifying the status of real estate units consisting of plots of land, sea and their houses, trees and other fixtures, including plot information, sea information, house (building and structure) information, forest and tree information, etc. The basic methods of investigation include the setting and coding of real estate units, real estate ownership investigation, and real estate measurement.

[0003] Spatialization of population data is to discretize demographic data based on a population spatial distribution model or using certain calculation methods or algorithms, to discover and display the implicit spatial information, so as to simulate or reproduce the objective world's population geographical distribution.

[0004] The practice of public space renewal in urban renewal has entered a stage influenced by the needs of the human living environment, in which the configuration of living service facilities plays an important role in improving the living environment. According to the "Urban Residential Area Planning and Design Standard" (GB50180-2018) (referred to as the "Standard"), corresponding living service facilities are built in various levels of living circles and residential neighborhoods in accordance with the classification of residential areas. The scale of residential area classification control is based on the principle that residents can meet their basic living needs within a reasonable walking distance. The standard divides the scale of residential areas into four levels, and each level of residential area corresponds to a different number of residential units and resident population.

[0005] In the context of urban renewal, rationally evaluating the configuration of living service facilities in residential areas of various sizes has become one of the tasks of urban governance. The use of spatialization results of title survey data and urban population data can directly analyze the number of residential units and resident population at all levels of residential areas, effectively supporting this work. However, there are significant differences in the production methods and application fields of the two results, and their joint use requires integration and correction.

[0006] Existing work uses survey data at the scale of plots and buildings, without further utilizing the number of households and uses of buildings; the reliability of mobile phone signaling data cannot completely eliminate the impact of data quality, dwell time threshold, uneven base station density and spatial threshold. This method uses the spatial and attribute data of the building and household level of title survey data to calibrate the spatialization results of regional population data and support related urban renewal work. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a method for spatial correction of urban population with the support of property rights and real estate table data. It can use the spatial and attribute data at the building and household level of property rights survey data to correct the spatial results of regional population data and support related urban renewal work.

[0008] The technical solution adopted by the present invention is:

[0009] A method for spatial correction of urban population based on weighted adjustment and real estate table data, which includes the following steps:

[0010] Step S1. Determine the area scope and spatial reference for the correction, obtain the regional boundary data, title survey data, population spatialization results data, and population statistics at the target time point within the target area;

[0011] Step S2. Determine a statistical grid based on the target area range and spatial reference;

[0012] Step S3. Unify the spatial reference of regional boundary data, title survey data, and population spatialization result data;

[0013] Step S4. Projecting and clipping the population spatialization result data using the regional boundary data of the regional range;

[0014] Step S5. Spatially match the title survey data and the cropped population spatialization result data with the statistical grid to obtain grid population data and grid real estate data;

[0015] Step S6. Calculate the average number of people per household in the grid based on the grid population data and grid property data;

[0016] Step S7. Select a demographic indicator of the demographic data, and set the demographic indicator as a correction target parameter, determine a data correction model based on the correction target parameter, and correct the grid population data based on the correction target parameter and the data correction model.

[0017] Preferably, in the method for spatializing urban population supported by weighted adjustment and real estate table data, the average number of people per household in the grid in step S6 is the grid population divided by the number of residential units in the grid.

[0018] Preferably, the urban population spatialization correction method supported by weight adjustment and real estate table data, wherein: the demographic indicator in step S7 is the average number of people per household.

[0019] Preferably, the urban population spatialization correction method supported by weight adjustment and real estate table data, wherein: the correction process in step S7 is specifically as follows:

[0020] Step S71. Obtain demographic data of several existing regions, determine demographic indicators based on the demographic data, and set the average household population as ANPH by region. i , the average maximum threshold per household is T, and the original population grid of each area is G i , the number of grids in the region is n;

[0021] Step S72: determine a correction function, and use the correction function to correct the distribution of the average number of people per household in the grid population data to obtain corrected data.

[0022] Preferably, the urban population spatialization correction method supported by weight adjustment and real estate table data, wherein: the correction function in step S72 is as follows:

[0023]

[0024] Among them, x i is the average population per grid household in the region, is the average number of people per household in the grids in the region, and n is the number of grids in the region.

[0025] Preferably, the method for spatializing urban population supported by weight adjustment and real estate table data, wherein: step S72 specifically includes:

[0026] Step S721. Input n, ANPH i, T,G i, M,P m , m=0, set the loop stop condition as the number of loops m exceeds the maximum number of iterations M, or the number of grids allowed to exceed the threshold value p is less than Pm. When the number of loops is less than M and the number of grids allowed to exceed the threshold value is greater than Pm, execute step S722, otherwise stop;

[0027] Step S722. Calculate the average number of households per grid h for region i j ;

[0028] Step S723. The average number of grid households in the area where the average number of households exceeds the threshold T is set to T, and the average number of regional households H is calculated based on the correction function;

[0029] Step S724. Calculate ANPH i The ratio k to H;

[0030] Step S725. Calculate h for each grid j *k;

[0031] Step S726. Count the number of grids p whose average household population exceeds the threshold T;

[0032] Step S727. When the number of grids p is greater than Pm, repeat steps S722 to S726 until the loop stop condition is reached;

[0033] Step S778. Output grid result CG i .

[0034] Preferably, the above-mentioned method for spatializing urban population supported by weighted adjustment and real estate table data, wherein: the average number of households per grid in step S772 is h j It is the grid population divided by the number of residential units in the grid.

[0035] Advantages of the present invention:

[0036] (1) The urban population spatialization correction method supported by the property rights and property table data of the present invention can completely eliminate the influence of data quality, residence time threshold, uneven base station density and spatial threshold on the urban population spatialization. The correction method of the present invention uses the spatial and attribute data of the building and household level of the property rights survey data to correct the spatialization results of the regional population data, and support related urban renewal work.

[0037] (2) With the support of title survey data, the present invention utilizes the spatial and attribute data at the natural building and household level to discover and correct the problems of the original population spatialization data; by setting the maximum threshold of the average number of people per household in the grid, the average number of people per household in the grid is corrected; based on the average number of people per household as the correction target parameter, the correction function is used to correct the average number of people per household in the grid population data. The correction result can provide the spatial distribution of the number of residential units and the resident population in the area. Combined with the "standard" indicator parameters, it can better support the rationality evaluation of the configuration of living service facilities in residential areas in urban renewal work. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flow chart of the method for spatial correction of urban population supported by real estate table data. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0040] Example 1

[0041] like Figure 1 , a method for spatial correction of urban population based on weighted adjustment and real estate table data, which includes the following steps:

[0042] Step S1. Determine the area scope and spatial reference for the correction, obtain the regional boundary data, title survey data, population spatialization results data, and population statistics at the target time point within the target area;

[0043] Step S2. Determine a statistical grid based on the target area range and spatial reference;

[0044] Step S3. Unify the spatial reference of regional boundary data, title survey data, and population spatialization result data;

[0045] Step S4. Projecting and clipping the population spatialization result data using the regional boundary data of the regional range;

[0046] Step S5. Spatially match the title survey data and the cropped population spatialization result data with the statistical grid to obtain grid population data and grid real estate data;

[0047] Step S6. Calculate the average number of people per household in the grid based on the grid population data and the grid real estate data. The average number of people per household in the grid is the grid population divided by the number of residential units in the grid.

[0048] Step S7. Select a demographic indicator of the demographic data, the demographic indicator being the average number of people per household, and set the demographic indicator as a correction target parameter, determine a data correction model based on the correction target parameter, and correct the grid population data based on the correction target parameter and the data correction model.

[0049] The calibration process in step S7 is specifically as follows:

[0050] Step S71. Obtain demographic data of several existing regions, determine demographic indicators based on the demographic data, and set the average household population as ANPH by region. i , the average maximum threshold per household is T, the original population grid of each area is Gi, and the number of grids in the area is n;

[0051] Step S72: determine a correction function, and use the correction function to correct the distribution of the average number of people per household in the grid population data to obtain corrected data.

[0052] The correction function in step S72 is as follows:

[0053]

[0054] Among them, x i is the average population per grid household in the region, is the average number of people per household in the grids in the region, and n is the number of grids in the region.

[0055] Step S72 specifically includes:

[0056] Step S721. Input n, ANPH i ,T,G i ,M,P m, m=0, set the loop stop condition as the number of loops m exceeds the maximum number of iterations M, or the number of grids allowed to exceed the threshold value p is less than Pm. When the number of loops is less than M and the number of grids allowed to exceed the threshold value is greater than Pm, execute step S722, otherwise stop;

[0057] Step S722. Calculate the average number of households per grid h for region i j， The average number of households in each grid h j It is the grid population divided by the number of residential units in the grid;

[0058] Step S723. The average number of grid households in the area where the average number of households exceeds the threshold T is set to T, and the average number of regional households H is calculated based on the correction function;

[0059] Step S724. Calculate the ratio k of ANPHi to H;

[0060] Step S725. Calculate h for each grid j *k;

[0061] Step S726. Count the number of grids p whose average household population exceeds the threshold T;

[0062] Step S727. When the number of grids p is greater than Pm ， Repeat steps S722 to S726 until the loop stop condition is reached;

[0063] Step S778. Output grid result CG i .

[0064] The urban population spatialization correction method supported by the rights and property table data of the present invention can completely eliminate the influence of data quality, residence time threshold, uneven base station density and spatial threshold on the urban population spatialization. The correction method of the present invention uses the spatial and attribute data of the building and household level of the title survey data to correct the spatialization results of the regional population data, and support related urban renewal work.

[0065] Example 2

[0066] The comparison between the city's permanent population and the population spatialization results in the statistical yearbook is shown in Table 1. It can be seen from Table 1 that the original spatial population data has different degrees of errors in various regions, but the overall error is relatively low.

[0067] Table 1 Comparison of population in the whole city

[0068]

[0069] In the test, the maximum threshold of the average household in the grid is set to 7, and the correction function is used to correct the distribution of the average number of people per household. The average number of people per household in each region is set to ANPH iThe correction results of each region are shown in Table 2.

[0070] Table 2 Calibration results

[0071]

[0072] It can be concluded from Table 2 that after correction, the relative errors of Liangxi District and Economic Development Zone are greatly reduced, indicating that the correction method of the present invention is effective.

[0073] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for spatial correction of urban population based on weight adjustment and real estate table data, characterized by: The following steps are involved: Step S1. Determine the area scope and spatial reference for the correction, obtain the regional boundary data, title survey data, population spatialization results data, and population statistics at the target time point within the target area; Step S2. Determine a statistical grid based on the target area range and spatial reference; Step S3. Unify the spatial reference of regional boundary data, title survey data, and population spatialization result data; Step S4. Projecting and clipping the population spatialization result data using the regional boundary data of the regional range; Step S5. Spatially match the title survey data and the cropped population spatialization result data with the statistical grid to obtain grid population data and grid real estate data; Step S6. Calculate the average number of people per household in the grid based on the grid population data and grid property data; Step S7. Select a demographic indicator of the demographic data, and set the demographic indicator as a correction target parameter, determine a data correction model according to the correction target parameter, and correct the grid population data based on the correction target parameter and the data correction model.

2. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 1 is characterized by: In step S6, the average number of people per grid household is the grid population divided by the number of residential units in the grid.

3. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 1 is characterized by: The demographic indicator in step S7 is the average number of people per household.

4. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 3 is characterized by: The calibration process in step S7 is specifically as follows: Step S71. Obtain the demographic data of several existing regions, determine the demographic indicators based on the demographic data, and set the average household population as ANPH according to the region. i , the average maximum threshold per household is T, and the original population grid of each area is G i , the number of grids in the region is n; Step S72: determine a correction function, and use the correction function to correct the distribution of the average number of people per household in the grid population data to obtain corrected data.

5. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 4 is characterized by: The correction function in step S72 is as follows: Among them, x i is the average population per grid household in the region, is the average number of people per household in the grids in the region, and n is the number of grids in the region.

6. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 5 is characterized by: Step S72 specifically includes: Step S721. Input n, ANPH i, T,G i, M,P m , m=0, set the loop stop condition as the number of loops m exceeds the maximum number of iterations M, or the number of grids allowed to exceed the threshold value p is less than Pm. When the number of loops is less than M and the number of grids allowed to exceed the threshold value is greater than Pm, execute step S722, otherwise stop; Step S722. Calculate the average number of households per grid h for region i j ; Step S723. The average number of grid households in the area where the average number of households exceeds the threshold T is set to T, and the average number of regional households H is calculated based on the correction function; Step S724. Calculate ANPH i The ratio k to H; Step S725. Calculate h for each grid j *k; Step S726. Count the number of grids p whose average household population exceeds the threshold T; Step S727. When the number of grids p is greater than Pm, repeat steps S722 to S726 until the loop stop condition is reached; Step S778. Output grid result CG i .

7. The method for spatializing urban population based on weight adjustment and real estate table data according to claim 6 is characterized by: The average number of households in each grid in step S772 is h j It is the grid population divided by the number of residential units in the grid.

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