Population age pyramid of Hebei Province in 2020.
<div><p>Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on t...
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2025
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| _version_ | 1852019033685098496 |
|---|---|
| author | Jin Wang (29560) |
| author2 | Shihan Ma (9362796) Qing Lv (63057) Qiang Li (8118) |
| author2_role | author author author |
| author_facet | Jin Wang (29560) Shihan Ma (9362796) Qing Lv (63057) Qiang Li (8118) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jin Wang (29560) Shihan Ma (9362796) Qing Lv (63057) Qiang Li (8118) |
| dc.date.none.fl_str_mv | 2025-06-25T17:52:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320298.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Population_age_pyramid_of_Hebei_Province_in_2020_/29406427 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm population forecasts due population dynamic monitoring mobile phone signals high population density different metaheuristic algorithms research focuses primarily mean squared error reliable predictive model div >< p 2 </ sup improve forecast accuracy better prediction performance xgboost model demonstrates proposed predictive model demographic data forecast effectively forecast research compared higher error demographic data better r combination model base model verified based various gradient validation show time characteristics reasonable parameters predicted data national regions machine learning learning models hebei province following year economic planning comparative experiments boosting machine |
| dc.title.none.fl_str_mv | Population age pyramid of Hebei Province in 2020. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R<sup>2</sup> 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2c74255dcb95c08d5fef426bbfa14ac6 |
| identifier_str_mv | 10.1371/journal.pone.0320298.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29406427 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Population age pyramid of Hebei Province in 2020.Jin Wang (29560)Shihan Ma (9362796)Qing Lv (63057)Qiang Li (8118)EcologyScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsparrow search algorithmpopulation forecasts duepopulation dynamic monitoringmobile phone signalshigh population densitydifferent metaheuristic algorithmsresearch focuses primarilymean squared errorreliable predictive modeldiv >< p2 </ supimprove forecast accuracybetter prediction performancexgboost model demonstratesproposed predictive modeldemographic data forecasteffectively forecastresearch comparedhigher errordemographic databetter rcombination modelbase modelverified basedvarious gradientvalidation showtime characteristicsreasonable parameterspredicted datanational regionsmachine learninglearning modelshebei provincefollowing yeareconomic planningcomparative experimentsboosting machine<div><p>Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R<sup>2</sup> 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.</p></div>2025-06-25T17:52:28ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0320298.g006https://figshare.com/articles/figure/Population_age_pyramid_of_Hebei_Province_in_2020_/29406427CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294064272025-06-25T17:52:28Z |
| spellingShingle | Population age pyramid of Hebei Province in 2020. Jin Wang (29560) Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm population forecasts due population dynamic monitoring mobile phone signals high population density different metaheuristic algorithms research focuses primarily mean squared error reliable predictive model div >< p 2 </ sup improve forecast accuracy better prediction performance xgboost model demonstrates proposed predictive model demographic data forecast effectively forecast research compared higher error demographic data better r combination model base model verified based various gradient validation show time characteristics reasonable parameters predicted data national regions machine learning learning models hebei province following year economic planning comparative experiments boosting machine |
| status_str | publishedVersion |
| title | Population age pyramid of Hebei Province in 2020. |
| title_full | Population age pyramid of Hebei Province in 2020. |
| title_fullStr | Population age pyramid of Hebei Province in 2020. |
| title_full_unstemmed | Population age pyramid of Hebei Province in 2020. |
| title_short | Population age pyramid of Hebei Province in 2020. |
| title_sort | Population age pyramid of Hebei Province in 2020. |
| topic | Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm population forecasts due population dynamic monitoring mobile phone signals high population density different metaheuristic algorithms research focuses primarily mean squared error reliable predictive model div >< p 2 </ sup improve forecast accuracy better prediction performance xgboost model demonstrates proposed predictive model demographic data forecast effectively forecast research compared higher error demographic data better r combination model base model verified based various gradient validation show time characteristics reasonable parameters predicted data national regions machine learning learning models hebei province following year economic planning comparative experiments boosting machine |