CN112330033A - Population comprehensive prediction method and system - Google Patents
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Abstract
The invention relates to a population comprehensive prediction method and system. The population comprehensive prediction method and the population comprehensive prediction system are based on historical population data in all levels of areas, a mode of selecting an optimal prediction model is carried out by using multiple fitting methods, the total population of the next decade in all levels of areas is predicted to obtain a prediction result set of the population change of the next decade, the prediction result set of the population change of the next decade is subjected to consistency adjustment, the adjusted prediction result set of the population change of the next decade is adjusted based on a future economy-population balance method and a future population total planning, and then the prediction information utilization rate of the population on time change and spatial distribution can be improved, and meanwhile, the prediction result set of the population change of the next decade is provided. And the human mouth data can be further accurately predicted and adjusted based on a preferred mode of various fitting methods.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a population comprehensive prediction method and system.
Background
Population prediction is an important research direction of population science, can provide future population basic data for formulating socioeconomic development plans, and can provide evaluation basis for some guidelines and policies and socioeconomic development. The population prediction can serve for the analysis, research and management control of human beings on the development process of birth, death and migration, and can provide method-level support for the demands of various industries and the like on the future scale of human mouth. Personnel life is one of the direct disaster carriers of the disastrous earthquake, and timely and reliable time-space distribution data of future population exposure is an important basis for risk assessment of future earthquake disasters. In order to improve the accuracy and the situation of the estimation of the spatial distribution of the future population, provide accurate basic data of the future population data for earthquake insurance companies and other industries, and research on a population time sequence change prediction method is carried out by relying on public social and economic development statistical data such as a statistical yearbook, national population statistical data and the like.
The population prediction model mainly comprises a linear regression model, a BP neural network model, a GM (1,1) model, a Logistic model (block model), a Markas model, an autoregressive model, a queue factor method and the like. The models have the advantages and the limitations, for example, the linear regression model formula is simple and easy to calculate, and as time increases, population increases infinitely according to an index, so that the prediction result precision of medium-long term population is low; the GM (1,1) model is suitable for trend prediction of social and economic system indexes with multiple factors, strong comprehensiveness and good cross correlation, and requires data accumulation to be a prediction condition of 5 years; the queue factor method considers the factors of population birth rate and population death rate which influence population growth, the prediction precision is high, but the acquisition channels of factor data are few and the continuity is poor; the BP neural network model has more accurate population prediction results in a short term (within 5 years of time data accumulation), but has larger error in long-term prediction; the Chinese population prediction system software has the characteristics of simple operation, easy understanding and high prediction quality, has comprehensive requirements on data and is more suitable for long-term prediction; the blocking model takes the blocking effect of the factors of limiting population growth, such as resources, environment and the like into consideration, the required fitting data volume is small, and the model is a relatively ideal model for medium-long term population prediction. Most of the research on the future population distribution prediction in China is concentrated on national level, provincial level or a certain city, the population data source of the prediction is national census once in 10 years, the adopted data is less, the time scale is larger, the prediction precision is not high, and the prediction is lack of the overall analysis and the regional space-time growth prediction under the influence of future economic-population ratios and future urban planning factors on different administrative division scales in the country.
Therefore, it is a technical problem to be solved in the art to provide a population prediction method or system capable of accurately predicting population quantity for a long time.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a population comprehensive prediction method and a population comprehensive prediction system.
In order to achieve the purpose, the invention provides the following scheme:
a method of comprehensive population prediction, comprising:
acquiring historical population total data and GDP total data in each level of area; the each stage area includes: province, prefecture, district and county; the province, the local city, the district and the county are administrative units;
generating a population time sequence set according to the historical population total data in each level of region; the set of population time series comprises: a population total time series set, a population density time series set and a population change rate time series set;
fitting the population time sequence set by adopting a plurality of population prediction models to obtain a correlation coefficient value corresponding to each population prediction model; the plurality of population prediction models comprises: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model;
determining a final population prediction model according to the correlation coefficient value; the ultimate population prediction model is a population prediction model with the largest relation value in a plurality of population prediction models;
fitting the population time sequence set by adopting the ultimate population prediction model to obtain a first population total prediction result set of the next decade;
respectively determining an economic-population ratio value of the next decade and a population total planning value of the next decade according to the first population total prediction result set and the GDP total data; the population total planning value is the population ratio in each level of area;
carrying out consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result;
and adjusting the second population total prediction result based on the planned population total of the next decade to obtain a final population prediction result in each level of area of the next decade.
Preferably, the generating a population time sequence set according to the historical population total data in each level of area specifically includes:
acquiring dividing vector data of dividing population data in each level of area;
associating administrative units according to the vector dividing data and the historical population total data;
generating a population total time sequence set for the associated administrative units according to the historical population total data;
for unassociated administrative units, obtaining change information of the unassociated administrative units, and obtaining vector dividing data of the unassociated administrative units according to the change information;
generating a population change rate time sequence set according to the vector dividing data of the unassociated administrative unit and the historical population total data;
aiming at an administrative unit which is adjusted, removed or newly built, generating a population density time sequence set according to the historical population total data and the area of the administrative unit;
and carrying out abnormal value processing on the population total time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schottky method to obtain a population time sequence set.
Preferably, the fitting of the ultimate population prediction model to the population time sequence set to obtain a first population total prediction result set of the next decade specifically includes:
fitting the population time sequence set by adopting the ultimate population prediction model to respectively obtain a population density change set of the next decade and a population change set of the next decade;
determining to obtain a first total population change prediction result set of the next decade according to the population density change set of the next decade and the area of each level of area;
determining to obtain a second population total change prediction result set of the next decade according to the population change set of the next decade and the historical population total data;
determining a population total change prediction result set with consistency in time and space for the next decade according to the first population total change prediction result set and the second population total change prediction result set; the population total prediction result set of the next decade with consistency in time and space is the first population total prediction result set.
Preferably, the performing a consistent adjustment on the first population total prediction result set based on the economic-population ratio value of the next decade and the population total planning value of the next decade to obtain a second population total prediction result specifically includes:
carrying out consistency adjustment on the sum of provincial and subordinate district-level and city-level population sums by adopting a consistency adjustment algorithm to obtain a first adjusted population sum prediction result set, and carrying out consistency adjustment on the first adjusted population sum prediction result set to obtain a second adjusted population sum prediction result set;
and performing consistency adjustment on the second adjustment population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
Preferably, the adjusting the second population total prediction result based on the planned population total in the next decade to obtain the final population prediction result in each level of area in the next decade specifically includes:
acquiring population growth rates in all levels of areas;
and after the second population total prediction result is replaced by the planning population total, determining a final population prediction result in each level of area in the next decade according to the population growth rate.
Corresponding to the population comprehensive prediction method, the invention also provides the following prediction system:
a population synthesis prediction system, comprising:
the total data acquisition module is used for acquiring historical population total data and GDP total data in each level of area; the each stage area includes: province, prefecture, district and county; the province, the local city, the district and the county are administrative units;
a population time sequence set generation module for generating a population time sequence set according to the historical population total data in each level of area; the set of population time series comprises: a population total time series set, a population density time series set and a population change rate time series set;
the correlation coefficient value determining module is used for fitting the population time sequence set by adopting a plurality of population prediction models to obtain correlation coefficient values corresponding to the population prediction models; the plurality of population prediction models comprises: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model;
the ultimate population prediction model determining module is used for determining an ultimate population prediction model according to the correlation coefficient value; the ultimate population prediction model is a population prediction model with the largest relation value in a plurality of population prediction models;
the first population total prediction result set determining module is used for fitting the population time sequence set by adopting the ultimate population prediction model to obtain a first population total prediction result set of the next decade;
a ratio determination module for determining an economic-population ratio of the next decade and a population total planning value of the next decade according to the first population total prediction result set and the GDP total data; the population total planning value is the population ratio in each level of area;
the second population total prediction result set determining module is used for carrying out consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result;
and the population prediction module is used for adjusting the second population total prediction result based on the planned population total of the next decade to obtain the final population prediction result in each level of area of the next decade.
Preferably, the population time series set generating module specifically includes:
the first dividing vector data acquisition unit is used for acquiring dividing vector data of divided population data in each level of area;
the administrative unit association unit is used for associating the administrative unit according to the vector dividing data and the historical population total data;
the population total time sequence set generation unit is used for generating a population total time sequence set for the associated administrative unit according to the historical population total data;
the second division vector data acquisition unit is used for acquiring the change information of the unassociated administrative units for the unassociated administrative units and acquiring the division vector data of the unassociated administrative units according to the change information;
the population change rate time sequence set generating unit is used for generating a population change rate time sequence set according to the dividing vector data of the unassociated administrative unit and the historical population total data;
the population density time sequence set generating unit is used for generating a population density time sequence set according to the historical population total data according to the area of an administrative unit which is adjusted, removed or newly built;
and the population time sequence set generation and determination unit is used for performing abnormal value processing on the population total amount time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schottky method to obtain a population time sequence set.
Preferably, the first population total prediction result set determining module specifically includes:
a change set determining unit, configured to fit the population time sequence set with the ultimate population prediction model to obtain a population density change set of the next decade and a population change set of the next decade, respectively;
the first population total change prediction result set determining unit is used for determining and obtaining a first population total change prediction result set of the next decade according to the population density change set of the next decade and the area of each level of area;
the second population total change prediction result set determining unit is used for determining and obtaining a second population total change prediction result set of the next decade according to the population change set of the next decade and the historical population total data;
a first population total change prediction result set determining unit, configured to determine a population total change prediction result set of the next decade having temporal and spatial consistency according to the first population total change prediction result set and the second population total change prediction result set; the population total prediction result set of the next decade with consistency in time and space is the first population total prediction result set.
Preferably, the second population total prediction result determining module specifically includes:
the adjusting unit is used for carrying out consistency adjustment on the sum of provincial and subordinated all-grade city grade population total amount by adopting a consistency adjustment algorithm to obtain a first adjustment population total amount prediction result set, and then carrying out consistency adjustment on the first adjustment population total amount prediction result set to obtain a second adjustment population total amount prediction result set;
and the second population total prediction result determining unit is used for carrying out consistent adjustment on the second adjusted population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
Preferably, the population prediction module specifically includes:
the population growth rate acquisition unit is used for acquiring population growth rates in all levels of areas;
and the population prediction unit is used for determining a final population prediction result in each level of area in the next decade according to the population growth rate after replacing the second population total prediction result with the planning population total.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the population comprehensive prediction method and system provided by the invention, based on historical population data in each level of area, a mode of selecting an optimal prediction model is carried out by using multiple fitting methods, the population total amount of the next decade in each level of area is predicted to obtain a prediction result set of population change of the next decade, the prediction result set of population change of the next decade is subjected to consistent adjustment, and the adjusted prediction result set of population change of the next decade is adjusted based on a future economic-population balance method and a future population total amount planning, so that the prediction result set of population change of the next decade can be provided while the utilization degree of prediction information of population on time change and spatial distribution is improved. And the human mouth data can be further accurately predicted and adjusted based on a preferred mode of various fitting methods.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a population comprehensive prediction method provided by the present invention;
FIG. 2 is a block diagram of an overall population comprehensive prediction method according to an embodiment of the present invention;
FIG. 3 is a graph of the predicted results of the GM (1,1) method model in an embodiment of the present invention;
fig. 4 is a schematic structural diagram of the population comprehensive prediction system provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a population comprehensive prediction method and a population comprehensive prediction system capable of accurately predicting population quantity for a long time, which are used for accurately predicting population data by comprehensively using various fitting methods based on historical time series population data and selecting an optimal population prediction model, and can be used for carrying out prediction adjustment in real time by combining a future economy-population ratio and a future population total.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a population comprehensive prediction method provided by the present invention, and fig. 2 is an overall framework diagram of a population comprehensive prediction method provided by the present invention implemented in an embodiment of the present invention. As shown in fig. 1 and 2, a population comprehensive prediction method includes:
step 100: and acquiring historical population total data and GDP total data in each level of area. Each stage area includes: province, prefecture, district and county. Provinces, prefectures, districts and counties are administrative units.
Step 101: and generating a population time sequence set according to historical population total data in each level of area. The population time series set includes: a population total time series set, a population density time series set, and a population change rate time series set.
and acquiring dividing vector data of dividing population data in each level of area.
And associating administrative units according to the vector dividing data and the historical population total data.
And generating a population total time sequence set for the associated administration unit according to the historical population total data.
And for the unassociated administration units, acquiring the change information of the unassociated administration units, and acquiring the vector dividing data of the unassociated administration units according to the change information.
And generating a population change rate time sequence set according to the dividing vector data of the unassociated administrative units and the historical population total data.
And aiming at the administrative unit which is adjusted, removed or newly built, generating a population density time sequence set according to the area of the administrative unit and the historical population total data.
And carrying out abnormal value processing on the population total time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schottky method to obtain a population time sequence set.
The process of implementing step 101 is further embodied as:
and given historical population amount and GDP amount data of province level, city level and district-county level of many years, automatically associating the data according to unified administrative division vector data and demographic data.
And generating a population total time sequence set for the administrative units capable of being automatically associated according to the administrative division vector data and the historical demographic data. And searching change information of the corresponding administrative divisions for the administrative units which cannot be associated, unifying the administrative divisions of the vector data and the statistical data according to the change information, and forming a population change rate time sequence set. And forming a population density time sequence set according to the administrative area aiming at the conditions of adjustment, new construction and withdrawal of the administrative unit.
And carrying out abnormal value processing on the population total time sequence set, the population density time sequence set and the population change rate time sequence set by adopting a Xiaovier method to obtain a smooth population time sequence set.
Step 102: and fitting the population time sequence set by adopting various population prediction models to obtain a correlation coefficient value corresponding to each population prediction model. Various demographic prediction models include: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model.
Step 103: and determining a final population prediction model according to the correlation coefficient value. The ultimate population prediction model is the population prediction model with the largest relation value in the multiple population prediction models.
Step 104: and fitting the population time sequence set by adopting a final population prediction model to obtain a first population total prediction result set of the next ten years.
Step 104 specifically includes: and fitting the population time sequence set by adopting an ultimate population prediction model to obtain a population density change set of the next decade and a population change set of the next decade respectively.
And determining to obtain a first total population change prediction result set of the next ten years according to the population density change set of the next ten years and the area of each level.
And determining to obtain a second population total change prediction result set of the next ten years according to the population change set of the next ten years and the historical population total data.
And determining a population total change prediction result set with consistency in time and space for the next decade according to the first population total change prediction result set and the second population total change prediction result set. The total population prediction result set of the next decade with consistency in time and space is the first total population prediction result set.
The specific implementation process of step 104 may further be:
and fitting and analyzing the smoothed time sequence set by using a retardation model, an exponential model, a logarithmic model, a linear model and a GM (1,1) method, and selecting a fitting method of the maximum value of the correlation coefficient to predict the change of the next decade. And multiplying the predicted population density change set of the next decade by the area of the administrative district to obtain a predicted result set of the total population change of the next decade. And for the predicted future decade population change set, calculating a future decade population change prediction result set according to the historical population, so as to obtain a temporally and spatially consistent future decade population change prediction result set and obtain a first population change prediction result set.
And (3) carrying out consistent adjustment on provincial level, regional level, city level and prefecture level population total amount on the obtained first population total amount prediction result set, wherein the specific adjustment steps are as follows:
for the forecasting result set of the population total amount of the next decade with consistency in time and space, the sum of the population of a certain provincial level and the total amount of all the population of the local level and the city level of the subordinate thereof is adjusted in a consistent way. And then the adjusted regional and municipal population sum and the sum of the regional and county population sums subordinate to the regional and county population sums are subjected to consistent adjustment. Ensuring that the adjusted population sum is equal to the sum of the population sums of all administrative districts under the adjusted population sum. The consistency adjustment algorithm on the administrative division is as follows:
wherein, Propop is the total population of a certain administrative region,
is the sum of all administrative regions population volumes subordinate to a certain administrative region, K is the number of administrative regions subordinate to a certain administrative region, City appleiDividing the total population of any administrative district under a certain administrative district, nCitypopiIs the adjusted population amount of a certain administrative area.
Step 105: and respectively determining an economic-population ratio value of the next decade and a population total planning value of the next decade according to the first population total prediction result set and the GDP total data. The total population planning value is the population ratio in each level of area.
The specific determination process of the economic-population balance ratio and the gross population planning value of the coming decade comprises the following steps:
the economic-population balance ratio algorithm is as follows:
GP＝(iGDP/Sgdp)/(iPOP/Spop)(2)
GP is the economic-population ratio of a certain administrative district, iGDP/Sgdp is the GDP ratio, and iPOP/Spop is the population ratio. The iGDP and the iPOP are the GDP total amount and the population total amount of a certain administrative district respectively, and the Sgdp and the Spop are the GDP total amount and the population total amount of a superior administrative district to which the certain administrative district belongs.
And calculating the total quantity of the GDP of each province level and each local level in the future decade by adopting a GM (1,1) method based on the historical GDP total quantity time sequence set, and calculating the ratio of the GDP of each province level and each local level in the every year. Based on the historical provincial-level and regional-level city-level economy-population ratio time sequence sets, a block model method is adopted to calculate the future provincial-level and regional-level city-level ten-year economy-population ratio. And (3) knowing the GDP ratio and the future economic-population ratio of each province level, each local level and city level every year, and further calculating the future population total ratio of each province level and each local level according to the formula (2).
Step 106: and carrying out consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
The method specifically comprises the following steps:
and (3) carrying out consistency adjustment on the sum of the provincial and all the regional and urban population sums belonging to the provincial and the regional and urban population sums by adopting a consistency adjustment algorithm to obtain a first adjusted population sum prediction result set, and then carrying out consistency adjustment on the first adjusted population sum prediction result set to obtain a second adjusted population sum prediction result set.
And performing consistency adjustment on the second adjustment population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
The specific process for implementing the step is as follows:
the obtained population total of the future decade of the whole country is calculated according to the proportion of the population total of each province every year, and the population total of each grade city of the future decade is calculated based on the calculated population total of each province of the future decade and according to the proportion of the population total of each grade city. And obtaining the total amount of the city population of each district grade in the next decade based on the data, and adjusting the data set of the total amount of the city population of each district grade in the next decade according to the formula (1).
Step 107: and adjusting the second population total prediction result based on the planned population total of the next decade to obtain the final population prediction result in each level of area of the next decade.
The method specifically comprises the following steps:
and acquiring the population growth rate in each level of area.
And after the second population total prediction result is replaced by the planning population total, determining a final population prediction result in each level of area in the next decade according to the population growth rate.
The specific process for implementing step 107 is as follows:
for the second population total prediction result obtained by the adjustment, the predicted population total of the province is replaced according to the known provincial future planned population total, and the future population total of the province is recalculated according to the growth rate of the predicted population total of the province. Meanwhile, the national predicted population total is kept unchanged, the population total of other provinces is adjusted year by year according to the formula (1), and then the regional city and county population total are adjusted according to the adjusted population total of the provinces and the formula (1). If the total amount of the planned population in the grade city is known, replacing the predicted total amount of the population in the grade city, keeping the growth rate of the predicted total amount of the population in the grade city unchanged, recalculating the total amount of the future population in the grade city, keeping the total amount of the population in the affiliated administrative district of the grade city unchanged, and adjusting the total amount of the population in other grade cities year by year according to the formula (1). And then adjusting and adjusting the population total of each subordinate district and county according to the adjusted population total of each local city and the formula (1). If the future planning population sum of the county level is known, replacing the predicted population sum of the county level, keeping the growth rate of the predicted population sum of the county level unchanged, recalculating the future population sum of the county level, keeping the upper level city population of the county level unchanged, and adjusting the population sum of other counties year by year according to a formula (1)
The following provides a specific embodiment to further illustrate the solution of the present invention, in the specific embodiment of the present invention, based on the overall framework diagram of the population comprehensive prediction method provided by implementing the present invention as shown in fig. 2, the GM (1,1) method model is used to fit the population time sequence set as an example, and in a specific application, the solution of the present invention is also applicable to other population prediction models or methods.
The GM (1,1) model is a grey prediction model proposed by professor dungdong, 1990, the basic idea is to transform an irregular data sequence to obtain a sequence with a certain regularity, so that the approximation can be performed by a curve. The model is suitable for a change system with a strong exponential law. The advantage of the GM (1,1) model is that less data is needed, and by modeling only 4 or more data (liu si feng et al, 2008), GM (1,1) reflects a first order differential function of a variable with respect to time, and its corresponding differential equation is:
in the formula, x(1)Is the sequence generated by one accumulation. t is time. a, u are the parameters to be estimated, called development gray number and endogenous control gray number, respectively (Leiweiwu et al, 1998).
Establishing a primary accumulation generation sequence, and setting an original sequence as follows:
x(0)＝{x(0)(1),x(0)(2),x(0)(3),…,x(0)(n)},i＝1,2,…,n
performing one-time accumulation according to the following method to obtain a generated number sequence (n is a sample space):
solving parameters a and u by using a least square method, and setting:
yn＝[x(0)(2),x(0)(3),…，x(0)(n)]T，
parameter identification a, u:
model for GM (1, 1):
and (5) checking the accuracy of the model. The test method comprises residual error test, relevance test and posterior difference test, and the posterior difference test is adopted in the research.
First, the original sequence x is calculated(0)(i) Mean square error of S0. It is defined as:
then calculate the residual series
Mean square error of S1. It is defined as:
the variance ratio is thus calculated:
and small error probability:
and finally, dividing the table according to the prediction precision grade (see table 1), and checking to obtain the prediction precision of the model.
TABLE 1GM (1,1) model prediction accuracy class Table
| Small error probability p value | Variance ratio c value | Prediction of accuracy level |
| >0.95 | <0.35 | Good taste |
| >0.80 | <0.5 | Qualified |
| >0.70 | <0.65 | Is just barely qualified |
| ≤0.70 | ≥0.65 | Fail to be qualified |
If the test is qualified, the model can be used for prediction. Namely, the method comprises the following steps:
The overall population in the east chuan district was analyzed using the GM (1,1) model based on the 1999-2012 data and a comparison of historical data and fitted values for the overall population in the east chuan district predicted by the GM (1,1) model is given in table 2.
TABLE 2 comparison of historical data and fitting values (predicted values) for general population in Dongchuan region
Based on this, the total population prediction model in the present embodiment is:
the prediction result of the GM (1,1) method model in this example is shown in fig. 3.
Based on the scheme disclosed by the invention, compared with the method in the prior art, the biggest difference is that the total population prediction result set of the next decade is obtained after population prediction is carried out by adopting the time sequence sets of the total population, population density and population change rate which are consistent in space and time under different administrative divisions. The traditional method only aims at prediction of national level, provincial level or a certain city based on the total population of many years, and spatial consistency is not considered. In addition, a forecasting result set of the total population of the coming ten years with consistent time and space can be obtained, and consistent adjustment is carried out on the total population of provincial level, district level, city level and county level based on a future economy-population balance method and a future planning population. In the past method, a ten-year population total prediction result set is obtained by directly using a prediction method.
In addition, corresponding to the population comprehensive prediction method provided above, the present invention also provides a population comprehensive prediction system, as shown in fig. 4, the population comprehensive prediction system includes: the system comprises a total data acquisition module 1, a population time sequence set generation module 2, a correlation coefficient value determination module 3, a final population prediction model determination module 4, a first population total prediction result set determination module 5, a ratio determination module 6, a second population total prediction result set determination module 7 and a population prediction module 8.
The total data acquisition module 1 is configured to acquire historical population total data and GDP total data in each level of area. Each stage area includes: province, prefecture, district and county. Provinces, prefectures, districts and counties are administrative units.
The population time sequence set generation module 2 is used for generating population time sequence sets according to historical population total data in all levels of regions. The population time series set includes: a population total time series set, a population density time series set, and a population change rate time series set.
The correlation coefficient value determination module 3 is configured to fit the population time sequence set by using multiple population prediction models to obtain a correlation coefficient value corresponding to each population prediction model. Various demographic prediction models include: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model.
The ultimate population prediction model determining module 4 is used for determining an ultimate population prediction model according to the correlation coefficient value. The ultimate population prediction model is the population prediction model with the largest relation value in the multiple population prediction models.
The first population total prediction result set determining module 5 is used for fitting the population time sequence set by adopting a final population prediction model to obtain a first population total prediction result set of the next decade.
The ratio determination module 6 is used for determining an economic-population ratio of the next decade and a population planning value of the next decade respectively according to the first population total prediction result set and the GDP total data. The total population planning value is the population ratio in each level of area.
The second population total prediction result set determining module 7 is used for performing consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
And the population prediction module 8 is used for adjusting the second population total prediction result based on the planned population total of the next decade to obtain a final population prediction result in each level of area of the next decade.
As a preferred embodiment of the present invention, the population time series set generating module 2 specifically includes: the system comprises a first dividing vector data acquisition unit, an administrative unit association unit, a population total amount time series set generation unit, a second dividing vector data acquisition unit, a population change rate time series set generation unit, a population density time series set generation unit and a population time series set generation and determination unit.
The first dividing vector data acquisition unit is used for acquiring dividing vector data of dividing population data in each level of area.
And the administrative unit association unit is used for associating the administrative unit according to the dividing vector data and the historical population total data.
And the population total time sequence set generating unit is used for generating a population total time sequence set for the associated administrative units according to the historical population total data.
The second division vector data acquisition unit is used for acquiring the change information of the unassociated administrative units for the unassociated administrative units and acquiring the division vector data of the unassociated administrative units according to the change information.
And the population change rate time sequence set generating unit is used for generating a population change rate time sequence set according to the dividing vector data of the unassociated administration units and the historical population total data.
And the population density time-series set generating unit is used for generating a population density time-series set according to the historical population total data according to the area of the administrative unit aiming at the administrative unit which is adjusted, removed or newly built.
The population time sequence set generation and determination unit is used for performing abnormal value processing on the population total amount time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schauverit method to obtain a population time sequence set.
As another preferred embodiment of the present invention, the first population total prediction result set determining module 5 specifically includes: the system comprises a change set determining unit, a first population total change prediction result set determining unit, a second population total change prediction result set determining unit and a first population total change prediction result set determining unit.
The change set determining unit is used for fitting the population time sequence set by adopting an ultimate population prediction model to respectively obtain a population density change set of the next decade and a population change set of the next decade.
The first population total change prediction result set determining unit is used for determining and obtaining a first population total change prediction result set of the next decade according to population density change sets and region areas of all levels of the next decade.
The second population total change prediction result set determining unit is used for determining a second population total change prediction result set of the next decade according to the population change set of the next decade and the historical population total data.
The first population total prediction result set determining unit is used for determining a population total prediction result set of the next decade with consistency in time and space according to the first population total change prediction result set and the second population total change prediction result set. The total population prediction result set of the next decade with consistency in time and space is the first total population prediction result set.
As still another preferred embodiment of the present invention, the second population total prediction result determining module 7 specifically includes: an adjusting unit and a second population total prediction result determining unit.
The adjusting unit is used for performing consistency adjustment on the sum of provincial and subordinated city grade population total amounts by adopting a consistency adjustment algorithm to obtain a first adjusted population total amount prediction result set, and then performing consistency adjustment on the first adjusted population total amount prediction result set to obtain a second adjusted population total amount prediction result set.
And the second population total prediction result determining unit is used for carrying out consistent adjustment on the second adjusted population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
As another preferred embodiment of the present invention, the population prediction module 8 specifically includes: a population growth rate acquisition unit and a population prediction unit.
The population growth rate acquiring unit is used for acquiring population growth rates in all levels of areas.
And the population prediction unit is used for determining the final population prediction result in each level of area in the next decade according to the population growth rate after replacing the second population prediction result with the planning population.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for comprehensive population prediction, comprising:
acquiring historical population total data and GDP total data in each level of area; the each stage area includes: province, prefecture, district and county; the province, the local city, the district and the county are administrative units;
generating a population time sequence set according to the historical population total data in each level of region; the set of population time series comprises: a population total time series set, a population density time series set and a population change rate time series set;
fitting the population time sequence set by adopting a plurality of population prediction models to obtain a correlation coefficient value corresponding to each population prediction model; the plurality of population prediction models comprises: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model;
determining a final population prediction model according to the correlation coefficient value; the ultimate population prediction model is a population prediction model with the largest relation value in a plurality of population prediction models;
fitting the population time sequence set by adopting the ultimate population prediction model to obtain a first population total prediction result set of the next decade;
respectively determining an economic-population ratio value of the next decade and a population total planning value of the next decade according to the first population total prediction result set and the GDP total data; the population total planning value is the population ratio in each level of area;
carrying out consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result;
and adjusting the second population total prediction result based on the planned population total of the next decade to obtain a final population prediction result in each level of area of the next decade.
2. The comprehensive population prediction method according to claim 1, wherein the generating of the population time series set according to the historical population total data in each level of area specifically comprises:
acquiring dividing vector data of dividing population data in each level of area;
associating administrative units according to the vector dividing data and the historical population total data;
generating a population total time sequence set for the associated administrative units according to the historical population total data;
for unassociated administrative units, obtaining change information of the unassociated administrative units, and obtaining vector dividing data of the unassociated administrative units according to the change information;
generating a population change rate time sequence set according to the vector dividing data of the unassociated administrative unit and the historical population total data;
aiming at an administrative unit which is adjusted, removed or newly built, generating a population density time sequence set according to the historical population total data and the area of the administrative unit;
and carrying out abnormal value processing on the population total time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schottky method to obtain a population time sequence set.
3. The method for comprehensive population prediction according to claim 1, wherein the fitting of the population time series set with the ultimate population prediction model to obtain a first population total prediction result set for a next decade specifically comprises:
fitting the population time sequence set by adopting the ultimate population prediction model to respectively obtain a population density change set of the next decade and a population change set of the next decade;
determining to obtain a first total population change prediction result set of the next decade according to the population density change set of the next decade and the area of each level of area;
determining to obtain a second population total change prediction result set of the next decade according to the population change set of the next decade and the historical population total data;
determining a population total change prediction result set with consistency in time and space for the next decade according to the first population total change prediction result set and the second population total change prediction result set; the population total prediction result set of the next decade with consistency in time and space is the first population total prediction result set.
4. The method for integrated population prediction according to claim 1, wherein the consistent adjustment of the first population total prediction result set based on the economic-population ratio value of the next decade and the population total planning value of the next decade to obtain the second population total prediction result comprises:
carrying out consistency adjustment on the sum of provincial and subordinate district-level and city-level population sums by adopting a consistency adjustment algorithm to obtain a first adjusted population sum prediction result set, and carrying out consistency adjustment on the first adjusted population sum prediction result set to obtain a second adjusted population sum prediction result set;
and performing consistency adjustment on the second adjustment population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
5. The comprehensive population prediction method according to claim 1, wherein the adjusting the second population total prediction result based on the planned population total in the next decade to obtain the final population prediction result in each level of area in the next decade comprises:
acquiring population growth rates in all levels of areas;
and after the second population total prediction result is replaced by the planning population total, determining a final population prediction result in each level of area in the next decade according to the population growth rate.
6. A system for integrated population prediction, comprising:
the total data acquisition module is used for acquiring historical population total data and GDP total data in each level of area; the each stage area includes: province, prefecture, district and county; the province, the local city, the district and the county are administrative units;
a population time sequence set generation module for generating a population time sequence set according to the historical population total data in each level of area; the set of population time series comprises: a population total time series set, a population density time series set and a population change rate time series set;
the correlation coefficient value determining module is used for fitting the population time sequence set by adopting a plurality of population prediction models to obtain correlation coefficient values corresponding to the population prediction models; the plurality of population prediction models comprises: a retardation model, an exponential model, a logarithmic model, a linear model, and a GM (1,1) method model;
the ultimate population prediction model determining module is used for determining an ultimate population prediction model according to the correlation coefficient value; the ultimate population prediction model is a population prediction model with the largest relation value in a plurality of population prediction models;
the first population total prediction result set determining module is used for fitting the population time sequence set by adopting the ultimate population prediction model to obtain a first population total prediction result set of the next decade;
a ratio determination module for determining an economic-population ratio of the next decade and a population total planning value of the next decade according to the first population total prediction result set and the GDP total data; the population total planning value is the population ratio in each level of area;
the second population total prediction result set determining module is used for carrying out consistent adjustment on the first population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result;
and the population prediction module is used for adjusting the second population total prediction result based on the planned population total of the next decade to obtain the final population prediction result in each level of area of the next decade.
7. The system according to claim 6, wherein the population time series set generating module specifically comprises:
the first dividing vector data acquisition unit is used for acquiring dividing vector data of divided population data in each level of area;
the administrative unit association unit is used for associating the administrative unit according to the vector dividing data and the historical population total data;
the population total time sequence set generation unit is used for generating a population total time sequence set for the associated administrative unit according to the historical population total data;
the second division vector data acquisition unit is used for acquiring the change information of the unassociated administrative units for the unassociated administrative units and acquiring the division vector data of the unassociated administrative units according to the change information;
the population change rate time sequence set generating unit is used for generating a population change rate time sequence set according to the dividing vector data of the unassociated administrative unit and the historical population total data;
the population density time sequence set generating unit is used for generating a population density time sequence set according to the historical population total data according to the area of an administrative unit which is adjusted, removed or newly built;
and the population time sequence set generation and determination unit is used for performing abnormal value processing on the population total amount time sequence set, the population change rate time sequence set and the population density time sequence set by adopting a Schottky method to obtain a population time sequence set.
8. The system of claim 6, wherein the first demographic prediction result set determination module specifically comprises:
a change set determining unit, configured to fit the population time sequence set with the ultimate population prediction model to obtain a population density change set of the next decade and a population change set of the next decade, respectively;
the first population total change prediction result set determining unit is used for determining and obtaining a first population total change prediction result set of the next decade according to the population density change set of the next decade and the area of each level of area;
the second population total change prediction result set determining unit is used for determining and obtaining a second population total change prediction result set of the next decade according to the population change set of the next decade and the historical population total data;
a first population total change prediction result set determining unit, configured to determine a population total change prediction result set of the next decade having temporal and spatial consistency according to the first population total change prediction result set and the second population total change prediction result set; the population total prediction result set of the next decade with consistency in time and space is the first population total prediction result set.
9. The population comprehensive prediction method according to claim 6, wherein the second population total prediction result determining module specifically comprises:
the adjusting unit is used for carrying out consistency adjustment on the sum of provincial and subordinated all-grade city grade population total amount by adopting a consistency adjustment algorithm to obtain a first adjustment population total amount prediction result set, and then carrying out consistency adjustment on the first adjustment population total amount prediction result set to obtain a second adjustment population total amount prediction result set;
and the second population total prediction result determining unit is used for carrying out consistent adjustment on the second adjusted population total prediction result set based on the economic-population ratio of the next decade and the population total planning value of the next decade to obtain a second population total prediction result.
10. The system of claim 6, wherein the population prediction module comprises:
the population growth rate acquisition unit is used for acquiring population growth rates in all levels of areas;
and the population prediction unit is used for determining a final population prediction result in each level of area in the next decade according to the population growth rate after replacing the second population total prediction result with the planning population total.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117473327A (en) * | 2023-12-26 | 2024-01-30 | 中电科新型智慧城市研究院有限公司 | Regional population model training method and regional population prediction method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107301467A (en) * | 2017-04-11 | 2017-10-27 | 程在舒 | Chinese Future population number predicted method |
| CN107515842A (en) * | 2017-07-19 | 2017-12-26 | 中南大学 | A kind of urban population density dynamic prediction method and system |
| CN108830402A (en) * | 2018-05-07 | 2018-11-16 | 北京百分点信息科技有限公司 | A kind of Ways of Population Prediction and device |
| CN110458048A (en) * | 2019-07-23 | 2019-11-15 | 南京林业大学 | Take population distribution Spatio-temporal Evolution and the cognition of town pattern feature into account |
| US10657480B1 (en) * | 2014-01-31 | 2020-05-19 | Express Scripts Strategic Development, Inc. | Methods and systems for pharmacy modeling |
-
2020
- 2020-11-10 CN CN202011243728.9A patent/CN112330033A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10657480B1 (en) * | 2014-01-31 | 2020-05-19 | Express Scripts Strategic Development, Inc. | Methods and systems for pharmacy modeling |
| CN107301467A (en) * | 2017-04-11 | 2017-10-27 | 程在舒 | Chinese Future population number predicted method |
| CN107515842A (en) * | 2017-07-19 | 2017-12-26 | 中南大学 | A kind of urban population density dynamic prediction method and system |
| CN108830402A (en) * | 2018-05-07 | 2018-11-16 | 北京百分点信息科技有限公司 | A kind of Ways of Population Prediction and device |
| CN110458048A (en) * | 2019-07-23 | 2019-11-15 | 南京林业大学 | Take population distribution Spatio-temporal Evolution and the cognition of town pattern feature into account |
Non-Patent Citations (4)
| Title |
|---|
| 上海市人口普查办公室: "《世纪之交的中国人口（上海卷）》", 30 April 2005, 中国统计出版社 * |
| 刘飞: "《高级统计师实务教程》", 31 January 2012, 陕西人民出版社 * |
| 王露: "基于分县尺度的2020-2030年中国未来人口分布", 《地理研究》 * |
| 赵进文: "中国人口总量与GDP总量关系模型研究", 《中国人口科学》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117473327A (en) * | 2023-12-26 | 2024-01-30 | 中电科新型智慧城市研究院有限公司 | Regional population model training method and regional population prediction method |
| CN117473327B (en) * | 2023-12-26 | 2024-04-26 | 中电科新型智慧城市研究院有限公司 | Regional population model training method and regional population prediction method |
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