Construction method of Chinese population distribution data set

Abstract

The invention relates to the technical field of data processing systems, in particular to a method for constructing a Chinese population distribution model, which comprises the following steps of: acquiring urban and rural distribution information, habitability, natural environment factors and social and economic influence factors, and establishing population distribution suitability factors; establishing an initial data layer; superposing a middle-term data layer; overlapping a later data layer; superposing a last-stage data layer; gathering the initial, middle, later and final data layers to obtain a distributed data set; and performing precision check on the distributed data set through the MARE value. The data set obtained by the method realizes accurate evaluation of Chinese population, and the data set has high geographic space precision, so that the dynamic change of population distribution can be deeply understood; meanwhile, the data set comprehensively considers the influence of urban and rural distribution, habitability, natural environment factors, social and economic influence and the like on population distribution, and the actual situation of population distribution is reflected more accurately.

Description

Technical Field

[0001] The present application relates to a technical field specifically applicable to data processing systems, and in particular to a method for constructing a distribution data set of the Chinese population. Background Art

[0002] The spatialization of population data is a complex and important process, which involves combining population data with spatial information to reveal the spatiotemporal characteristics of population distribution and its influencing factors. At present, the commonly used method for spatialization of population data is grid simulation of population distribution. This method is based on statistical analysis theory and establishes a mapping relationship between population density and environmental factors in the spatial domain, thereby achieving a detailed characterization of population distribution.

[0003] In the existing technology, representative global population spatial data sets such as GPW and GRUMP are used to estimate and analyze population distribution, but GPW data is affected by the limitations of the original data, especially in areas with low census data accuracy. This may lead to inaccurate population distribution estimates in these areas. GRUMP data uses night light data. Although night light data can reflect the intensity and scope of human activities to a certain extent, it also has some limitations. It will overestimate the actual scope of urban areas and cannot detect areas with low electricity consumption in the world.

[0004] Although the above datasets have established a continuous global population distribution, the estimated results of the above datasets for China's population distribution are not very accurate. Summary of the invention

[0005] Through research, the inventors found that China, as a country with a large population and significant distribution differences, needs a population spatial distribution dataset with fine spatial scale and long-term resolution to provide more accurate and precise population distribution information for analyzing the spatiotemporal patterns of population evolution.

[0006] The purpose of this application is to provide a method for constructing a distribution dataset of China's population. By combining the impact of urban and rural distribution, habitability, natural environmental factors and socio-economic influences on population distribution, and comprehensively considering the impact of various factors on population distribution, a multi-layer superimposed dataset is established to solve the technical problem that the existing technology cannot provide a dataset that can more accurately estimate China's population distribution information.

[0007] According to one aspect of the present application, a method for constructing a distribution dataset of Chinese population is provided, comprising:

[0008] Step 1. Obtain urban and rural distribution information, habitability, natural environmental factors, and socioeconomic influencing factors, calculate weights, and establish population distribution suitability factors;

[0009] Step 2. Establish the initial data layer, which is the population data of urban and rural distribution information;

[0010] Step 3. Overlay the mid-term data layer, which is habitability data;

[0011] Step 4. Overlay the later data layer, which is the natural environment factor data;

[0012] Step 5. Overlay the final data layer, which is the data of socio-economic influencing factors.

[0013] Step 6. Collect the initial data layer, the mid-term data layer, the late data layer and the final data layer to obtain a distributed data set;

[0014] Step 7. Perform an accuracy test on the distribution data set by value.

[0015] In some embodiments, the urban-rural distribution information population data in step 2 includes district-level population data, district-level boundary population data, urban-rural population data, and town distribution data.

[0016] In some embodiments, the habitability data in step 3 includes nature reserve, desert and water body data information.

[0017] In some embodiments, the natural environment factor data in step 4 includes altitude and terrain undulation data information.

[0018] In some embodiments, the socioeconomic influencing factor data in step 5 includes urban economic influencing factors and rural economic influencing factors.

[0019] In some embodiments, a distribution data set is obtained in step 6, and the data set is:

[0020]

[0021] Among them, P K Population dataset for model calculation; P Z is the urban or rural population of the county where pixel k is located; a, b, and c are relative weight indices for measuring the corresponding factors.

[0022] In some embodiments, the smaller the value in step 7, the higher the validity of the data set.

[0023] Compared with the prior art, the present application has the following advantages and beneficial effects: the data set obtained by the method of the present application realizes an accurate assessment of China's population, and can make a relatively accurate estimate of population density when data is insufficient, and the data set of the present application has a high degree of geographic spatial accuracy, which helps to more deeply understand the dynamic changes in population distribution; and this data set comprehensively considers the impact of urban and rural distribution, habitability, natural environmental factors and socio-economic influences on population distribution, more accurately reflects the actual situation of population distribution, and is more suitable for the study of China's population spatial distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a flow chart of the construction method of this application.

[0026] Legend:

[0027] none. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0029] This embodiment provides a method for constructing a distribution dataset of the Chinese population. The method for constructing a distribution dataset of the Chinese population is already in the testing stage. Figure 1 Give a description.

[0030] In order to better understand the technology of this application, some terms are now uniformly explained:

[0031] Pixel: 1 km by 1 km grid;

[0032] Example

[0033] This embodiment provides a method for constructing a distribution dataset of Chinese population, including the following contents:

[0034] First, in step 1, obtain urban and rural distribution information, habitability, natural environmental factors, and socioeconomic factors, calculate weights, and establish population distribution suitability factors.

[0035] In this step, it is necessary to calculate the weights of habitability, natural environment factors and socio-economic factors, among which:

[0036] (1) The weight of habitability is calculated as follows:

[0037] Areas with an altitude of more than 5000m and areas with an altitude of more than 1km will be excluded. 2 The water bodies and nature reserves are regarded as habitable areas, the pixel value of habitable areas is 1, and the pixel value of uninhabitable areas is 0;

[0038] (2) The weight calculation method of natural environment factors is:

[0039] 1. Altitude:

[0040] Altitude varies relatively greatly within the county, and the data resolution is high, which plays an important role in influencing population distribution. There is a strong linear relationship between population density and mean altitude. Considering the obvious regional differences in China's natural conditions and population distribution, this method uses a regional approach and is established based on the division of China's nine major agricultural regions.

[0041] Using 1 km elevation grid data, the mean elevation of each county can be calculated. The relationship between elevation and population density is obtained based on logarithmic univariate linear regression partitioning, and the elevation factor weight w is calculated by partitioning. 1k , the specific formula group is:

[0042] ln(PD i )=m u z i +b

[0043] Where D represents population density; z represents the average elevation of the county; i represents the county; m u is the fitting parameter of the agricultural area obtained according to the above formula;

[0044]

[0045] Where k represents the grid, located inside i.

[0046] 2. Terrain relief

[0047] There are many natural factors that affect population distribution, and topographic relief is also one of the important factors affecting population distribution. In order to further improve the accuracy of the data set, topographic relief is taken into consideration. There is a strong correlation between topographic relief and population density across the country, and the logarithmic curve fitting degree of the two is as high as 0.91. Therefore, the relationship between topographic relief and population density can be obtained based on the logarithmic univariate linear regression partition, and the topographic relief factor w can be calculated. 2k , the specific formula is:

[0048] ln(PDi ) = t u z+c

[0049] Where PD represents population density, z represents the mean of the county’s topographic relief, i represents the county; t u is the fitting parameter of agricultural area u obtained according to the above formula;

[0050]

[0051] k represents the grid, which is located inside i.

[0052] (3) The weights of socio-economic factors are calculated as follows:

[0053] Socioeconomic factors also have a certain impact on population distribution. This dataset divides the calculation of socioeconomic impact factors into two parts: urban and rural.

[0054] For cities, the urban population density decreases as the distance from the city center increases. We use the inverse power function to describe the impact of socioeconomic factors on urban pixels as space changes. Specifically,

[0055]

[0056] Among them, k is located in urban areas, represents the city center, λ represents the distance decay exponent, Indicates the distance from the grid to the city center;

[0057] For rural areas, the distribution of rural population is affected by factors such as the gravity of surrounding urban centers and transportation infrastructure. The closer to the city center, the more conducive it is to its economic development. The potential gravity model is used to describe the impact of socioeconomic factors on rural pixels as space changes.

[0058] Specifically:

[0059]

[0060] in, Indicates the number of cities within a certain distance (a certain distance represents the radiation radius of a city. China is divided into eastern, central and western cities, and the radiation radius is set to 75km, 85km and 95km respectively); represents the population of the city where the city center is located; Indicates cities within the region.

[0061] Next, in step 2, an initial data layer is established, and the initial data layer is population data of urban and rural distribution information;

[0062] Further, in step 3, a mid-term data layer is superimposed, the mid-term data layer being habitability data;

[0063] Further, in step 4, a later data layer is superimposed, and the later data layer is natural environment factor data;

[0064] Furthermore, in step 5, the final data layer is superimposed, and the final data layer is the data of social and economic influencing factors.

[0065] Furthermore, in step 6, the initial data layer, the mid-term data layer, the late data layer and the final data layer are collected to obtain a distributed data set, in detail:

[0066] In step 6, we get the distribution data set, which is:

[0067]

[0068] Among them, P K Population dataset for model calculation; P Z is the urban or rural population of the county where pixel k is located; a, b, c are relative weight indices for measuring the corresponding factors; w 0k 、w 1k 、w 2k and w 3k is the influence coefficient value of each layer of each pixel K calculated in the previous article.

[0069] Finally, in step 7, the MARE value is used to perform an accuracy test on the distribution dataset. Specifically, the simulation results are aggregated to the street level in combination with the 2010 street-level census data, and the differences between the census data and the simulation results are compared. The MARE value verification model is:

[0070]

[0071] Among them, N represents the number of verification data units, C is the unit of verification data (township / street level), O represents verification data, and M represents the model simulation data after integration into verification data units. At the same time, the smaller the MARE value in step 7, the higher the validity of the data set.

[0072] It should be noted that the urban-rural distribution information population data in step 2 includes district-level population data, district-level boundary population data, urban-rural population data and town distribution data; the habitability data in step 3 includes data information on nature reserves, deserts and water bodies; the natural environment factor data in step 4 includes data information on altitude and terrain undulation; the socio-economic influencing factor data in step 5 includes urban economic influencing factors and rural economic influencing factors;

[0073] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present application is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0074] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for constructing a distribution dataset of Chinese population, characterized in that: include: Step 1. Obtain urban and rural distribution information, habitability, natural environmental factors, and socioeconomic influencing factors, calculate weights, and establish population distribution suitability factors; Step 2. Establish the initial data layer, which is the population data of urban and rural distribution information; Step 3. Overlay the mid-term data layer, which is habitability data; Step 4. Overlay the later data layer, which is the natural environment factor data; Step 5. Overlay the final data layer, which is the data of socio-economic influencing factors; Step 6. Collect the initial data layer, the mid-term data layer, the late data layer and the final data layer to obtain a distributed data set; Step 7. Perform accuracy test on the distribution dataset through MARE value.

2. The construction method according to claim 1, characterized in that: The urban-rural distribution information population data in step 2 includes district-county level population data, district-county level boundary population data, urban-rural population data and town distribution data.

3. The construction method according to claim 1, characterized in that: The habitability data in step 3 includes data information of nature reserves, deserts and water bodies.

4. The construction method according to claim 1, characterized in that: The natural environment factor data in step 4 includes altitude and terrain undulation data information.

5. The construction method according to claim 1, characterized in that: The socioeconomic influencing factor data in step 5 include urban economic influencing factors and rural economic influencing factors.

6. The construction method according to claim 1, characterized in that: In step 6, we get the distribution data set, which is: Among them, P K Population dataset for model calculation; P Z is the urban or rural population of the county where pixel k is located; a, b, and c are relative weight indices for measuring the corresponding factors.

7. The construction method according to claim 1, characterized in that: The smaller the MARE value in step 7, the higher the validity of the data set.

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