CN117649030B - Grid population distribution data space precision partition optimization method and system - Google Patents
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Abstract
The invention discloses a grid population distribution data space precision partition optimization method and system, and relates to the technical field of population spatialization. The method mainly comprises the steps of carrying out iterative division on a plurality of grid spaces of a population distribution data layer to obtain a plurality of grid partitions; calculating global population density weights of all grid partitions, partition land class weights of all grid partitions and land class area weights of all land classes of all grid partitions according to various land classes; under the constraint of global population density weight, regional class weight and regional class area weight, reassigning each grid partition; and performing downscaling optimization on each grid partition according to the partition floor type weight, the floor type building attribute weight and the global population density weight of each grid partition. The invention can more fully represent the overall situation of the whole area and improve the accuracy of population distribution data.
Description
Technical Field
The invention relates to the technical field of population spatialization, in particular to a grid population distribution data space precision partition optimization method and system.
Background
High-precision grid population distribution data can play an important role in the relevant fields of climate, environment, city management and the like. The highest spatial resolution of the currently published global grid population distribution data is 100 meters, and the data is published every year and is more suitable for large areas such as the world, the country and the like. However, this resolution data is too coarse in granularity, limited in fineness, and lacks global population space distribution data of higher resolution, especially for small or tiny regions, which is insufficient to provide accurate results. In the aspect of spatial precision optimization of population data, an area weighting method is an early-developed socioeconomic statistical data spatialization method, auxiliary data are not needed, but the premise is that socioeconomic data are uniformly distributed in a certain area. However, the population distribution does not always follow the rule, so that the multi-class partition density mapping method based on auxiliary data is more suitable for optimizing the accuracy of the population distribution data. Worlddrop data is currently widely applied grid population distribution data, the highest spatial resolution is 100 meters, and with the rapid acquisition and precision improvement of land utilization data and building data, and the need of management and research of villages and towns, parks and even smaller scale areas, the population data with the spatial resolution of 100 meters is insufficient to meet the application requirements of fine micro-scale refinement. In practical application, if the sample is split into the 25-meter grid scale directly by adopting an equal division method, a large error can be caused. Guo Zihan and the like, using the eastern camping land utilization type data, and adopting a local sampling method to optimize the spatial precision of WorldPop data by combining the regional land class weight and the area proportion of the whole area, thereby obtaining grid regional data with the spatial resolution of 25 m. The weights of the local area samples cannot fully represent the overall situation of the whole area due to the weights determined by the sampling method, and population densities of different areas are different. To solve the above problems, a weight distribution strategy that considers the population distribution difference of the whole area is needed to realize high-precision partitioning of the grid population distribution data space.
Disclosure of Invention
The invention aims to provide a grid population distribution data space precision partition optimization method and system, which solve the technical problems that the weight of a local area sample cannot fully represent the overall situation of the whole area, and the population density of different areas has difference to influence the precision of a population distribution data space.
The invention is realized by the following technical scheme:
a grid population distribution data space precision partition optimization method comprises the following steps: iteratively dividing a plurality of grid spaces of the population distribution data layer to obtain a plurality of grid partitions; calculating global population density weights of multiple land utilization types in all the grid partitions, and partition land class weights of multiple land utilization types in each grid partition; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; according to the population of each building type in each grid partition, carrying out population redistribution on a plurality of grid partitions; performing downscaling optimization on each grid partition subjected to population redistribution, wherein: for the population of the grids of each grid partition, calculating the population of each land use type in each grid after the down-scale optimization according to the land area weight of each land use type in each grid partition and the pattern area of each land use type in each grid after the down-scale optimization; and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
The population distribution data layer is obtained through the following steps: preprocessing grid population distribution data, homeland investigation data and geographic national condition data; modifying said grid demographics using official demographics; and superposing the corrected grid population distribution data, the territorial survey data and the land pattern spots in the geographic national condition data to obtain a plurality of grid spaces of the population distribution data layer.
The preprocessing grid population distribution data, homeland investigation data and geographical national condition data comprises the following steps: after unifying the grid population distribution data, the homeland investigation data and the geographical national condition data into a coordinate system, correlating an administrative region demarcation vector data layer in the homeland investigation data with official demographic data to obtain population numbers of a plurality of administrative regions of the administrative region demarcation vector data layer; obtaining the land utilization type corresponding to the land utilization data in the homeland investigation data according to the land utilization classification standard; rounding and vectorizing the grid population distribution data;
the modifying the grid demographics using official demographics includes the steps of: carrying out space superposition on the administrative region demarcation data layer and the vectorized grid population distribution data; calculating the actual grid areas of the grid partitions in different administrative partitions after superposition; for the original grid population of each administrative division of the grid population distribution data, calculating the actual population of each grid division according to the actual grid area of each grid division and the original grid area of the grid population distribution data; summarizing and counting population numbers of all the grid partitions in each administrative partition; obtaining actual population numbers of all the grid partitions in the administrative division according to the administrative division demarcation data layer; calculating a population correction coefficient of each administrative division by using the population numbers of all the grid divisions and the actual population numbers of the administrative divisions; and correcting the population of each grid partition in each administrative partition by using the population correction coefficient of each administrative partition to obtain the actual population of the grid.
The performing population reassignment on the plurality of grid partitions according to the population count of each building type in each grid partition comprises: performing superposition analysis on the corrected grid population distribution data and land utilization type pattern spots in the homeland investigation data, and calculating land areas of all land utilization types in each grid partition; performing superposition analysis on the corrected grid population distribution data and building pattern spot data in the geographical national condition data, and calculating the building area of each building type in each grid partition; obtaining weight coefficients of various building types according to the density of multiple buildings, independent buildings and the number of building layers; calculating the population of each building type in each grid partition according to the area of each building in each grid partition, the weight coefficient of the corresponding building type, and the area and weight coefficient of all the buildings of the building type; and (3) carrying out population redistribution on a plurality of the grid partitions according to the population count of each building type in each grid partition.
The performing downscaling optimization on each grid partition after population redistribution comprises the following steps: dividing the multiple grid partitions after the population redistribution into 100-meter grids, and dividing each grid partition into 25-meter grids according to a quadtree principle; and generating population distribution vector data according to all the 25-meter grids, and converting the generated population distribution vector data into a grid data format to form a plurality of 25-meter grids after the grid partition downscaling optimization.
The calculation of the population of grids for each grid partition according to the land area weight of each land use type in each grid partition and the spot area of each land use type in each grid after the down-scale optimization, includes: carrying out superposition analysis on each 25-meter grid and the corresponding 100-meter grid to obtain the area of each land utilization type pattern spot, the area of each building type and the number of grid openings in each 25-meter grid; for the population of the grids of 100 meters, calculating to obtain the population of the land in each 25 meters of grids according to the land area weight of each land utilization type in each 100 meters of grids and the pattern area corresponding to each land utilization type in each 25 meters of grids; summarizing the area of the pattern spots of each land utilization type in all 25-meter grids to obtain the area occupation ratio of the pattern spots of each land utilization type; calculating the population of each building type in each 25 m grid according to the area ratio of the pattern spots of each land utilization type in each 25 m grid and the area of each building type in each 25 m grid for the area of each building type in each 100 m grid;
Obtaining the optimized population in each grid after the down-scaling optimization according to the population of each land use type in each grid after the down-scaling optimization and the population of each building type in each grid after the down-scaling optimization, including: and calculating the optimized population of each 25-meter grid according to the ground population and the preset weight of the building population.
The iterative division of the plurality of grid spaces of the population distribution data layer to obtain a plurality of grid partitions includes: classifying a plurality of grid spaces according to grid population averages of the plurality of grid spaces of the population distribution data layer; calculating the classified population mean values of different types of the grid spaces; repeating the step of secondarily classifying the plurality of grid spaces according to the classifying population mean value obtained after the previous classification for a plurality of times, and recalculating the classifying population mean values of the grid spaces of different types until the change value of the difference value between the grid space smaller than the classifying population mean value and the grid space larger than the classifying population mean value is lower than a preset stability threshold value, and finally obtaining a plurality of divided grid partitions.
The global population density weights of the land utilization types in all the grid partitions are calculated by the following steps: counting the area occupation ratio of the land utilization types in each grid partition according to the population distribution data layer; screening the grid partitions containing a single land utilization type according to the area occupation ratio of the land utilization types in each grid partition to obtain a single land type grid; calculating global population density weights of various land utilization types in each grid partition according to the population numbers of all the single land grid types of each land utilization type and the ratio of the total area of the single land grid types of the land utilization type; when the single land utilization type is water, setting the global population density weight to 0;
the partition land class weight of each grid partition in the plurality of land utilization types is obtained through the following steps: and calculating the regional land class weight of all the single land class grids classified each time according to the population number of each land use type in each single land class grid and the ratio of the total area of the land use type in all the single land class grids.
The calculating of the global population density weight for each of the grid partitions based on the land use types, the partition land class weight for each of the grid partitions, and the land class area occupation ratio for each of the grid partitions, comprises: calculating final land class weights of all the grid partitions of the land utilization types according to the global population density weights of all the single land class grids of the land utilization types and the partition land class weights of all the single land class grids of the land utilization types; normalizing the final class weights of the land use types in each grid partition according to the final class weights of the land use types in each grid partition in different classifications; calculating the land area occupation ratio of each land type in each grid partition according to the ratio of the land area of each land type in different grid partitions to the grid area of each grid partition; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; normalizing the land area weights of the land types in each grid partition according to the land area weights of the land types in each grid partition; obtaining land population numbers of the land use types in all the grid partitions according to the land area weight of each land use type in each grid partition and the population number of the grid;
According to the population of each building type in each grid partition, carrying out population redistribution on a plurality of the grid partitions, wherein the method comprises the following steps: screening all the grid partitions except for the water body which contains a single land utilization type; obtaining the land population of each land type in each grid partition according to the land area weights of each land type in each grid partition and the population of the grids; the reassignment result of all the grid partitions containing a single land utilization type is kept unchanged; and distributing all the grid partitions except the single land utilization type which is a water body according to the population numbers of the land types of the grid partitions according to each land utilization type to obtain a plurality of new grid partitions.
A grid demographic data space accuracy zoning optimization system comprising: space partitioning module: the method comprises the steps of performing iterative division on a plurality of grid spaces of a population distribution data layer to obtain a plurality of grid partitions; the grid analysis module: the global population density weights of the land utilization types in all the grid partitions are calculated, and partition land class weights of the land utilization types in each grid partition are calculated; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; population distribution module: the system is used for carrying out population reassignment on a plurality of the grid partitions according to the population number of each building type in each grid partition; and a scale optimization module: the method is used for carrying out downscaling optimization on each grid partition after population redistribution, wherein: for the population of the grids of each grid partition, calculating the population of each land use type in each grid after the down-scale optimization according to the land area weight of each land use type in each grid partition and the pattern area of each land use type in each grid after the down-scale optimization; and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention relates to a grid population distribution data space precision partition optimization method, which combines population distribution unbalance of different areas in an integral area and population distribution unbalance characteristics of the same area in different areas, combines factors of the area, the area and house building attribute weight to realize grid partition distribution data space downscaling and space precision optimization, and performs space partition on original grid partition distribution data to obtain grid partition distribution data with higher resolution. By combining official demographic data and fusing multi-source data, the invention can improve the spatial resolution of the existing grid demographic data, so that the demographic distribution is more refined and the real demographic distribution situation is better reflected. The population distribution difference of different areas in the whole area and the population distribution difference characteristic of the same area in different areas are considered, so that the spatial positioning precision of the grid partition distribution data is fitted more accurately, the spatial self-adaptive conversion between the population distribution grid scales can be realized, and the accuracy of the population distribution data in a small area range is ensured. The method solves the problem that the weight of the local area sample cannot fully represent the overall situation of the whole area, and the population density of different areas has difference to influence the spatial precision of population distribution data, and is beneficial to providing finer and more reliable data support for the fields of land space planning and implementation management, man-ground relation research and application and the like.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method for spatial precision zoning optimization of grid demographic data according to embodiment 1 of the present application;
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for optimizing spatial precision partitioning of data of population distribution of a grid, including: iteratively dividing a plurality of grid spaces of the population distribution data layer to obtain a plurality of grid partitions; calculating global population density weights of multiple land utilization types in all the grid partitions, and partition land class weights of multiple land utilization types in each grid partition; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; according to the population of each building type in each grid partition, carrying out population redistribution on a plurality of grid partitions; performing downscaling optimization on each grid partition subjected to population redistribution, wherein: for the population of the grids of each grid partition, calculating the population of each land use type in each grid after the down-scale optimization according to the land area weight of each land use type in each grid partition and the pattern area of each land use type in each grid after the down-scale optimization; and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
The population distribution data layer is obtained through the following steps: preprocessing grid population distribution data, homeland investigation data and geographic national condition data; modifying said grid demographics using official demographics; and superposing the corrected grid population distribution data, the territorial survey data and the land pattern spots in the geographic national condition data to obtain a plurality of grid spaces of the population distribution data layer.
The preprocessing grid population distribution data, homeland investigation data and geographical national condition data comprises the following steps: after unifying the grid population distribution data, the homeland investigation data and the geographical national condition data into a coordinate system, correlating an administrative region demarcation vector data layer in the homeland investigation data with official demographic data to obtain population numbers of a plurality of administrative regions of the administrative region demarcation vector data layer; obtaining the land utilization type corresponding to the land utilization data in the homeland investigation data according to the land utilization classification standard; rounding and vectorizing the grid population distribution data;
the modifying the grid demographics using official demographics includes the steps of: carrying out space superposition on the administrative region demarcation data layer and the vectorized grid population distribution data; calculating the actual grid areas of the grid partitions in different administrative partitions after superposition; for the original grid population of each administrative division of the grid population distribution data, calculating the actual population of each grid division according to the actual grid area of each grid division and the original grid area of the grid population distribution data; summarizing and counting population numbers of all the grid partitions in each administrative partition; obtaining actual population numbers of all the grid partitions in the administrative division according to the administrative division demarcation data layer; calculating a population correction coefficient of each administrative division by using the population numbers of all the grid divisions and the actual population numbers of the administrative divisions; and correcting the population of each grid partition in each administrative partition by using the population correction coefficient of each administrative partition to obtain the actual population of the grid.
The performing population reassignment on the plurality of grid partitions according to the population count of each building type in each grid partition comprises: performing superposition analysis on the corrected grid population distribution data and land utilization type pattern spots in the homeland investigation data, and calculating land areas of all land utilization types in each grid partition; performing superposition analysis on the corrected grid population distribution data and building pattern spot data in the geographical national condition data, and calculating the building area of each building type in each grid partition; obtaining weight coefficients of various building types according to the density of multiple buildings, independent buildings and the number of building layers; calculating the population of each building type in each grid partition according to the area of each building in each grid partition, the weight coefficient of the corresponding building type, and the area and weight coefficient of all the buildings of the building type; and (3) carrying out population redistribution on a plurality of the grid partitions according to the population count of each building type in each grid partition.
The performing downscaling optimization on each grid partition after population redistribution comprises the following steps: dividing the multiple grid partitions after the population redistribution into 100-meter grids, and dividing each grid partition into 25-meter grids according to a quadtree principle; and generating population distribution vector data according to all the 25-meter grids, and converting the generated population distribution vector data into a grid data format to form a plurality of 25-meter grids after the grid partition downscaling optimization.
The calculation of the population of grids for each grid partition according to the land area weight of each land use type in each grid partition and the spot area of each land use type in each grid after the down-scale optimization, includes: carrying out superposition analysis on each 25-meter grid and the corresponding 100-meter grid to obtain the area of each land utilization type pattern spot, the area of each building type and the number of grid openings in each 25-meter grid; for the population of the grids of 100 meters, calculating to obtain the population of the land in each 25 meters of grids according to the land area weight of each land utilization type in each 100 meters of grids and the pattern area corresponding to each land utilization type in each 25 meters of grids; summarizing the area of the pattern spots of each land utilization type in all 25-meter grids to obtain the area occupation ratio of the pattern spots of each land utilization type; calculating the population of each building type in each 25 m grid according to the area ratio of the pattern spots of each land utilization type in each 25 m grid and the area of each building type in each 25 m grid for the area of each building type in each 100 m grid;
Obtaining the optimized population in each grid after the down-scaling optimization according to the population of each land use type in each grid after the down-scaling optimization and the population of each building type in each grid after the down-scaling optimization, including: and calculating the optimized population of each 25-meter grid according to the ground population and the preset weight of the building population.
The iterative division of the plurality of grid spaces of the population distribution data layer to obtain a plurality of grid partitions includes: classifying a plurality of grid spaces according to grid population averages of the plurality of grid spaces of the population distribution data layer; calculating the classified population mean values of different types of the grid spaces; repeating the step of secondarily classifying the plurality of grid spaces according to the classifying population mean value obtained after the previous classification for a plurality of times, and recalculating the classifying population mean values of the grid spaces of different types until the change value of the difference value between the grid space smaller than the classifying population mean value and the grid space larger than the classifying population mean value is lower than a preset stability threshold value, and finally obtaining a plurality of divided grid partitions.
The global population density weights of the land utilization types in all the grid partitions are calculated by the following steps: the global population density weights of the land utilization types in all the grid partitions are calculated by the following steps: counting the area occupation ratio of the land utilization types in each grid partition according to the population distribution data layer; screening the grid partitions containing a single land utilization type according to the area occupation ratio of the land utilization types in each grid partition to obtain a single land type grid; calculating global population density weights of various land utilization types in each grid partition according to the population numbers of all the single land grid types of each land utilization type and the ratio of the total area of the single land grid types of the land utilization type; when the single land utilization type is water, setting the global population density weight to 0;
the partition land class weight of each grid partition in the plurality of land utilization types is obtained through the following steps: and calculating the regional land class weight of all the single land class grids classified each time according to the population number of each land use type in each single land class grid and the ratio of the total area of the land use type in all the single land class grids.
The calculating of the global population density weight for each of the grid partitions based on the land use types, the partition land class weight for each of the grid partitions, and the land class area occupation ratio for each of the grid partitions, comprises: calculating final land class weights of all the grid partitions of the land utilization types according to the global population density weights of all the single land class grids of the land utilization types and the partition land class weights of all the single land class grids of the land utilization types; normalizing the final class weights of the land use types in each grid partition according to the final class weights of the land use types in each grid partition in different classifications; calculating the land area occupation ratio of each land type in each grid partition according to the ratio of the land area of each land type in different grid partitions to the grid area of each grid partition; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; normalizing the land area weights of the land types in each grid partition according to the land area weights of the land types in each grid partition; obtaining land population numbers of the land use types in all the grid partitions according to the land area weight of each land use type in each grid partition and the population number of the grid;
According to the population of each building type in each grid partition, carrying out population redistribution on a plurality of the grid partitions, wherein the method comprises the following steps: screening all the grid partitions except for the water body which contains a single land utilization type; obtaining the land population of each land type in each grid partition according to the land area weights of each land type in each grid partition and the population of the grids; the reassignment result of all the grid partitions containing a single land utilization type is kept unchanged; and distributing all the grid partitions except the single land utilization type which is a water body according to the population numbers of the land types of the grid partitions according to each land utilization type to obtain a plurality of new grid partitions.
When preprocessing grid population distribution data, homeland investigation data and geographical national condition data, all the acquired WorldPop data, homeland investigation data, geographical national condition data and the like can be unified into a CGCS2000 coordinate system. And associating the county-level and village-level official demographic data issued by the statistical bureau with a administrative district demarcation vector data layer in the homeland survey data, and matching the population into an attribute table of the administrative district demarcation vector data layer. And according to land utilization classification standards, encoding land utilization data in the homeland investigation data according to land types. The territory survey data are summarized into 12 primary categories (cultivated land, garden land, forest land, grassland, commercial land, industrial and mining storage land, residential land, public management and public service land, special land, transportation land, water area, water conservancy facility land and other land). The WorldPop population distribution data with the spatial resolution of 100 meters is rounded, vectorization is carried out, the FID in the vectorization layer attribute table is marked as grid coding, and an identification code field ID is newly added.
And when the administrative region demarcation data layer and the vectorized grid population distribution data are subjected to space superposition, the administrative region demarcation data layer is connected with the statistics bureau population in a matching way, and the administrative region demarcation data layer and the vectorized 100 m WorldPop population distribution data are subjected to space superposition.
And calculating the actual grid areas of the grid partitions of the overlapped administrative division ranges, and calculating the world pop population of the grid. Then:
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in the method, in the process of the invention,numbering the new overlapped grids>Numbering the original 100 m grids>For the new grid population->Is the population of the original grid.
WorldPop is classified according to administrative division fields, the population of WorldPop of the corresponding administrative division is summarized and counted, and the administrative division is calculated according to the actual populationThe calculation formula is as follows:
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in the method, in the process of the invention,for administrative division->Actual demographic number,/->For administrative division->Is the primary population of WorldPop.
According to the classification of different administrative regions, respectively calculating different grids in the corresponding administrative regionsCorrected world pop populationThe calculation formula is as follows:
。
and carrying out superposition analysis on the corrected WorldPop100 m vector data and land utilization data, and calculating 12 primary land areas in each 100 m grid partition. For example, the land area after grid partitioning as shown in table 1 is obtained:
TABLE 1
And carrying out superposition analysis on the corrected WorldPop100 m vector data and the building pattern spot data, and calculating to obtain the area of each type of building in each 100 m grid partition.
When the grid spaces of the population distribution data layer are iteratively divided to obtain the grid partitions, different space partitions can be iteratively divided according to the size relation between the population numbers of the grids and the average value.
Optionally, the population numbers in the 100-meter grids are firstly ordered in the order from big to small, the grids are used as abscissa, the population numbers in each grid are used as ordinate, and a population number graph is drawn.
And then calculating the average value of all the grid population numbers, and filling different numerical values into the ID fields in the grid layer attribute table according to whether the grid population numbers are smaller or larger than the average value. If the grid population is less than the average, the ID field is filled with 0; if the grid population is greater than the average, the ID field is filled with 1. Thus, a first partitioning result is obtained.
In the first partitioning result, a grid population distribution data layer with an ID of 1 is extracted. Then, the mean of these grid demographics is calculated. And filling different values into the ID field in the layer attribute table according to the size relation of the grid population and the average population. Updating the ID field to 2 for grids with population smaller than the average value; for grids with population greater than the mean, the ID field is updated to 3. This gives a second discrimination result.
And repeating the steps, so that space partition is continuously carried out based on the last partition result, the change value of the grid number ratio of the average population is subtracted from the grid number ratio of the average population, and the change value is reduced to the corresponding point of the preset stability threshold. This enables finer optimization of the demographics when partitioned according to different spaces of the grid demographics data. For example, the spatial partitioning results for different levels based on the size relationship of the grid population to the mean are shown in table 2 below:
TABLE 2
And (3) reassigning the original WorldPop population to the ground class in the 100-meter grid under triple constraints set on the basis of zonal ground class weights (global population density weights of the whole region), zonal ground class weights (global population density weights of the whole population in each population zone) and area weights (ground class area weights).
The area of each land class in the grid partition is calculated by counting the areas of different land classes in each 100-meter grid, and the area occupation ratio of each land class to all grids is counted.
The grids which are completely covered by a single ground class of 12 ground classes in all grids are the single ground class full-coverage grids. For example, a grid completely covered by a single class is shown in Table 3:
TABLE 3 Table 3
According to the land area of different land utilization types in each grid partition and the corresponding partition land weight, calculating to obtain land area weights, and carrying out normalization processing on each result to obtain the weight of the land area weights, so that the constraint on redistribution is realized by using the weight of the land area weights.
For each of the 12 categories, the population of the category in the partition fully covered by all the single categories is counted, and then the average population density is calculated by dividing the total area of the category in the corresponding partition to obtain the global population density weight, so that the category is obtainedThe following are examples: />
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In the method, in the process of the invention,is of the type of ground->Weight of->Is of the type of ground->The population count of the full-coverage grid,is of the type of ground->The total area of the mesh is covered.
For a land type that is a body of water, the land weight is set to 0 because normally no one will live in the water region:。
when the class weight of the partition is calculated, the data is split according to different population area (ID) values, and then the space partitions of different levels are obtained. And screening the subareas covered by the single land utilization type to obtain a single land grid-like network.
For each of the 12 categories, the population of all the single category grids within the category is summed and then divided by the total area of the category in all the single category grids to calculate the average population density, resulting in a zoned category weight. For example, when the average population density is calculated for each hierarchical partition of population (for different ID values), the partition floor class weight is obtained for each of 12 floor classes The following are examples:
；
obtaining the ground class through the coupling calculation of global and regional ground class weightsIs based on the final class weights of:
。
partitioning all population level IDsNormalization is carried out, and a normalization result of final class weights of all levels is calculated>. Taking id=0 as an example:
；
wherein,the minimum value of the final land class weights representing all class levels of the various land use types,representing the maximum value of the final land class weights for all classification levels of the various land use types.
When calculating area weights of different types, firstly calculating the area weights in each gridThe floor area ratio of the floor is as follows: in the gridGround area/grid area. In the form of a gridThe following are examples:
；
in the method, in the process of the invention,is a grid->Middle->Floor area ratio of floor>Is a grid->Middle->The area of the ground class is defined as,is a grid->Is a total area of (c).
Calculation ofThe ground area weight is as follows: />Floor area ratio x final->Partition class weights, taking id=0 as an example:
；
repeating calculation to obtain all area weights of the land class in the grid, and normalizing according to all calculation results:；/>
wherein,the sum of the floor area weights representing all of the floor classes within each grid.
During reassignment, the population of each local pattern spot in each 100 m grid partition is calculated, such as grid Ground class->The population of (2) is as follows:
；
because the water body partition land class weight is set to 0, in the 100-meter grid partition in the step, if the land class comprises water bodies and other land classes, the water body population in the grid is 0, and the population is distributed to the other land classes; if there is only a water floor class within the grid, then the reassignment result within the grid is 0. Therefore, in order not to change the WorldPop population distribution data with the spatial resolution of 100 meters, the step is to screen a full-coverage grid of a water body with the size of 100 meters, and the numerical value is the same as that of the original data.
When reassigning considering the number of floors and density of buildings, the WorldPop population is assigned to buildings within a 100 meter grid based on the building spot area and the building spot type.
Buildings can be classified into various types according to the building density class and the building floor class. Such as: high-density multi-layer house, low-density multi-layer house, high-density low house, low-density low house, multi-layer independent house, medium-high-layer independent house, super-high-layer independent house, low-low independent house and waste house.
And determining a weight coefficient of each building type, and correspondingly obtaining the building type with corresponding layer number and density according to the set population weight. Wherein the weight of the abandoned house and the super high-rise independent house is 0, and the weight coefficient of the other houses is Wb. Assuming that the weight coefficient of the single-layer independent house is 1, the number of house floors and the density are averaged according to the description of the house buildings, and the corresponding building types are estimated by the number of building floors, the density of multiple buildings and the actual values of the independent buildings. Specifically, the weight coefficients of various houses are set as shown in table 4:
TABLE 4 Table 4
Based on the grid numbers, the total area of each type of building in each 100 m grid is counted together. Calculation gridInside, building->The population of (2) is:
；
in the method, in the process of the invention,is a buildingIs defined by the area of the (c),is a buildingWeight coefficients corresponding to house types.
Downscaling of a demographic data space, comprising:
(1) Based on 100-meter grid data, splitting the 100-meter grid into 25-meter grids according to a quadtree principle and a quadtree method, and recording a one-to-one mapping relation between 100-meter grid identification codes ID and 25-meter grid identification codes ID.
(2) And carrying out superposition analysis on each 25m grid and each 100 m grid to obtain the land utilization type area, the building area of each type and the corresponding population number in each 25m grid.
(3) According to the calculated result, the area of the pattern spot and the area ratio of the land class corresponding to the 100-meter grid are calculated proportionally to obtain the population of the land class in the pattern spot of 25 m:
；
in the method, in the process of the invention,numbering 100 meters of grids, < >>Numbering 25m grids->Is a ground class.
And summarizing according to the 25m grid IDs to obtain all land areas in the grid, and obtaining the population of the 25m grid scale.
And (3) according to the calculated result of the pattern spot area in the step (2) and the area ratio of the buildings of all building types, calculating the population of the different types of buildings in the pattern spot of 25m proportionally.
；
Setting the population weight of the ground class and the population weight ratio of the building as 1:1, the population of the 25m grids is:
；
(7) And converting the 25m grid population data vector data format into a raster data format, namely forming the population distribution data set with the optimized spatial resolution of 25 meters.
Example 2
The embodiment of the application provides a grid population distribution data space precision partition optimization system, which comprises the following steps: space partitioning module: the method comprises the steps of performing iterative division on a plurality of grid spaces of a population distribution data layer to obtain a plurality of grid partitions; the grid analysis module: the global population density weights of the land utilization types in all the grid partitions are calculated, and partition land class weights of the land utilization types in each grid partition are calculated; calculating a land area weight for each of the land types in each of the grid partitions based on the global population density weights for each of the grid partitions for each of the land types, the partition land type weights for all of the grid partitions, and the land area ratios for each of the grid partitions for each of the land types; population distribution module: the system is used for carrying out population reassignment on a plurality of the grid partitions according to the population number of each building type in each grid partition; and a scale optimization module: the method is used for carrying out downscaling optimization on each grid partition after population redistribution, wherein: for the population of the grids of each grid partition, calculating the population of each land use type in each grid after the down-scale optimization according to the land area weight of each land use type in each grid partition and the pattern area of each land use type in each grid after the down-scale optimization; and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
In summary, the embodiment of the invention provides a grid population distribution data space precision partition optimization method and a grid population distribution data space precision partition optimization system: according to the invention, multisource data such as homeland investigation data and geographical national condition data are fused, the population distribution imbalance of different areas in the whole area and the population distribution imbalance characteristic of the same area in different areas are considered, and the population grid distribution data with higher resolution is obtained by carrying out regional space drilling on original population grid distribution data by combining multiple factors such as ground type, area, house building attribute weight and the like. By combining official demographic data issued by authorities and fusing multisource high-precision data, the invention can improve the spatial resolution of the existing grid population distribution data, so that the population distribution is more refined and the real population distribution situation is better reflected; according to the invention, the population distribution difference of different types in the whole area and the population distribution difference characteristic of the same type in different areas are considered, so that the spatial positioning precision of fitting population grid distribution data can be more accurately realized, the spatial self-adaptive conversion between population distribution grid scales based on quadtree subdivision can be realized, and the easy acquisition, the accuracy and the local detail characteristic of population distribution data in a small area range are ensured. The method is beneficial to providing finer and more reliable data support for the fields of territorial space planning, implementation management, man-ground relation research, application and the like.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The grid population distribution data space precision partition optimization method is characterized by comprising the following steps of:
iteratively dividing a plurality of grid spaces of the population distribution data layer to obtain a plurality of grid partitions;
calculating global population density weights of multiple land utilization types in all grid partitions, and partition land class weights of multiple land utilization types in each grid partition;
calculating a land area weight of each land type in each grid partition according to the global population density weight of each land type in each grid partition, the partition land type weight of each land type in the corresponding grid partition, and the land area occupation ratio of each land type in each grid partition;
According to the population of each building type in each grid partition, carrying out population redistribution on a plurality of grid partitions;
performing downscaling optimization on each grid partition subjected to population redistribution, wherein:
for the population of grids of each grid partition, calculating to obtain the population of land types of each land type in each grid after the downscaling optimization according to the land type area weight of each land type in each grid partition and the pattern area of each land type in each grid after the downscaling optimization;
and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
2. The method for spatially accurate zoning optimization of grid demographic data of claim 1, wherein the demographic data layer is obtained by:
preprocessing grid population distribution data, homeland investigation data and geographic national condition data;
modifying the grid demographics data with official demographics data;
And superposing the corrected grid population distribution data, the territorial survey data and the land pattern spots in the geographic national condition data to obtain a plurality of grid spaces of the population distribution data layer.
3. The method for optimizing spatial precision partitioning of grid demographic data according to claim 2, wherein the preprocessing of grid demographic data, homeland survey data and geographical national condition data comprises:
after unifying the grid population distribution data, the homeland investigation data and the geographic national condition data into a coordinate system, associating an administrative region demarcation vector data image layer in the homeland investigation data with official demographic data to obtain population numbers of a plurality of administrative regions of the administrative region demarcation vector data image layer; according to land utilization classification standards, obtaining the land utilization types corresponding to the land utilization data in the homeland investigation data; rounding and vectorizing the grid population distribution data;
said modifying said grid demographic data with official demographic data comprising the steps of:
carrying out space superposition on the administrative region demarcation data layer and the vectorized grid population distribution data;
Calculating the actual grid areas of the grid partitions in different administrative partitions after superposition; for the original grid population of each administrative division of the grid population distribution data, calculating the actual population of each grid division according to the actual grid area of each grid division and the original grid area of the grid population distribution data;
summarizing and counting population numbers of all the grid partitions in each administrative partition; obtaining the actual population of all the grid partitions in the administrative division according to the administrative division demarcation data layer; calculating population correction coefficients of all the administrative regions by using population numbers of all the grid regions and the actual total population numbers in the administrative regions;
and correcting the population of each grid partition in each administrative division by using the population correction coefficient of each administrative division to obtain the actual population of the grid.
4. The method of optimizing spatial precision partitioning of grid demographics data as set forth in claim 2, wherein said reassigning a plurality of said grid partitions according to the demographics of each building type within each of said grid partitions comprises:
Performing superposition analysis on the corrected grid population distribution data and land utilization type pattern spots in the homeland investigation data, and calculating land areas of all land utilization types in each grid partition;
performing superposition analysis on the corrected grid population distribution data and building pattern spot data in the geographical national condition data, and calculating the building area of each building type in each grid partition;
obtaining weight coefficients of various building types according to the multi-building density, the independent buildings and the number of building layers;
calculating population numbers of each building type in each grid partition according to the area of each building in each grid partition, the weight coefficient of the corresponding building type, and the area and the weight coefficient of all the buildings of the building type;
and carrying out population redistribution on a plurality of grid partitions according to the population count of each building type in each grid partition.
5. A method for optimizing spatial precision partitioning of data of a demographic profile of a grid as defined in claim 4, wherein said downscaling each of said grid partitions after said demographics comprises:
Dividing the multiple grid partitions after the population redistribution into 100-meter grids, and dividing each grid partition into 25-meter grids according to a quadtree principle;
and generating population distribution vector data according to all the 25-meter grids, and converting the generated population distribution vector data into a grid data format to form a plurality of 25-meter grids after the grid partitions are downscaled and optimized.
6. The method for optimizing spatial precision partitioning of grid demographics data according to claim 4, wherein the calculating the population of grids for each grid partition according to the land area weight of each land use type in each grid partition and the spot area of each land use type in each grid after downscaling optimization comprises:
carrying out superposition analysis on each 25-meter grid and the corresponding 100-meter grid to obtain the area of each land utilization type pattern spot, the area of each building type and the number of grid openings in each 25-meter grid;
for the population of the grids of 100 meters, calculating to obtain the population of the land in each 25 meters of grids according to the land area weight of each land utilization type in each 100 meters of grids and the pattern area of each land utilization type in the corresponding 25 meters of grids;
Summarizing the area of each land utilization type in all 25-meter grids to obtain the land pattern area occupation ratio of each land utilization type;
calculating, for each building type of building area within each 100 meter grid, a population of buildings for each of said building types within each 25 meter grid based on said spot area duty for each of said land use types within each 25 meter grid and said building area for each of all building types within each 25 meter grid;
obtaining the optimized population in each grid after the down-scaling optimization according to the population of each land utilization type in each grid after the down-scaling optimization and the population of each building type of each building in each grid after the down-scaling optimization, including:
and calculating the optimized population of each 25-meter grid according to the ground population and the preset weight of the building population.
7. The method for optimizing spatial precision partitioning of demographic data of a grid as set forth in claim 1, wherein the iteratively partitioning the plurality of grid spaces of the demographic data layer into a plurality of grid partitions comprises:
Classifying a plurality of grid spaces according to grid population averages of the plurality of grid spaces of the population distribution data layer; calculating classified population average values of different types of the grid spaces;
repeating the step of secondarily classifying the plurality of grid spaces according to the classified population mean value obtained after the last classification for a plurality of times, and recalculating the classified population mean values of the grid spaces of different types until the change value of the difference value between the grid space smaller than the classified population mean value and the grid space larger than the classified population mean value is lower than a preset stability threshold value, and finally obtaining a plurality of divided grid partitions.
8. The method for optimizing spatial precision partitioning of grid demographics data according to claim 1, wherein the global population density weights of the plurality of land utilization types in all the grid partitions are calculated by the steps of:
counting the area occupation ratio of a plurality of land utilization types in each grid partition according to the population distribution data layer;
screening the grid partitions containing a single land utilization type according to the area occupation ratio of the plurality of land utilization types in each grid partition to obtain a single land type grid;
Calculating global population density weights of various land utilization types in each grid partition according to the population numbers of all the single land grid types of each land utilization type and the ratio of the total area of the land utilization type in the single land grid type;
when the single land utilization type is a water body, setting the global population density weight to 0;
the partition land class weight of the plurality of land utilization types in each grid partition is obtained through the following steps:
and calculating the regional land class weights of all the single land class grids classified each time according to the population of each land use type in each single land class grid and the ratio of the population of the land use type in the total area of all the single land class grids.
9. A grid demographics data space accuracy zoning optimization method as defined in claim 8, wherein said calculating a floor area weight for each of said land usage types in each of said grid partitions based on said global population density weights for each of said grid partitions for each of said land usage types, a zone floor class weight for each of said grid partitions for each of said land usage types, and a floor area ratio for each of said land usage types in each of said grid partitions comprises:
Calculating to obtain the final land class weight of each grid partition of various land utilization types according to the global population density weight of each single land class grid of various land utilization types and the partition land class weights of the land utilization types in all the single land class grids;
normalizing the final class weights of the land use types in each grid partition according to the final class weights of the land use types in each grid partition in different classifications;
calculating the land area occupation ratio of each land utilization type in each grid partition according to the ratio of the land area of each land utilization type in different grid partitions to the grid area of each grid partition;
calculating the land area weight of each land use type in each grid partition according to the land area occupation ratio of each land use type in each grid partition and the final land weight of each land use type in each grid partition;
normalizing the land area weights of the land use types in each grid partition according to the land area weights of the land use types in each grid partition;
Obtaining land population numbers of the land use types in all the grid partitions according to the land area weight of each land use type in each grid partition and the population number of the grid;
according to the population of each building type in each grid partition, carrying out population redistribution on a plurality of grid partitions, wherein the population redistribution comprises the following steps:
screening all the grid partitions except for the water body which contains a single land utilization type;
obtaining the land population of each land utilization type in each grid partition according to the land area weight of each land utilization type in each grid partition and the population of the grid;
the reassignment result of all the grid partitions containing a single land utilization type water body is kept unchanged;
and distributing all the grid partitions except for the water body containing the single land utilization type in the population of each grid partition according to each land utilization type to obtain a plurality of new grid partitions.
10. A grid demographic data space accuracy zoning optimization system, comprising:
Space partitioning module: the method comprises the steps of performing iterative division on a plurality of grid spaces of a population distribution data layer to obtain a plurality of grid partitions;
the grid analysis module: the method comprises the steps of calculating global population density weights of multiple land utilization types in all grid partitions, and partition land class weights of multiple land utilization types in each grid partition; calculating a land area weight of each land type in each grid partition according to the global population density weight of each land type in each grid partition, the partition land type weight of each land type in the corresponding grid partition, and the land area occupation ratio of each land type in each grid partition;
population distribution module: the system is used for carrying out population redistribution on a plurality of grid partitions according to the population of each building type in each grid partition;
and a scale optimization module: the method is used for carrying out downscaling optimization on each grid partition subjected to population redistribution, wherein: for the population of grids of each grid partition, calculating to obtain the population of land types of each land type in each grid after the downscaling optimization according to the land type area weight of each land type in each grid partition and the pattern area of each land type in each grid after the downscaling optimization; and obtaining the optimized population number in each grid after the downscaling optimization according to the population number of each land utilization type in each grid after the downscaling optimization and the population number of each building type in each grid after the downscaling optimization.
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