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Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models
In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision ma...
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Published in: | Sustainability 2022-04, Vol.14 (8), p.4844 |
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description | In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited. |
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As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14084844</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Aging ; Birth rate ; Cities ; Decision making ; Fertility ; Geographical distribution ; Grey prediction ; Growth rate ; Hypotheses ; Labor force ; Logit models ; Metropolitan areas ; Population decline ; Population growth ; Prediction models ; Regional development ; Regional planning ; Regression analysis ; Social change ; Sustainability ; Trends ; Urban populations</subject><ispartof>Sustainability, 2022-04, Vol.14 (8), p.4844</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited.</description><subject>Accuracy</subject><subject>Aging</subject><subject>Birth rate</subject><subject>Cities</subject><subject>Decision making</subject><subject>Fertility</subject><subject>Geographical distribution</subject><subject>Grey prediction</subject><subject>Growth rate</subject><subject>Hypotheses</subject><subject>Labor force</subject><subject>Logit models</subject><subject>Metropolitan areas</subject><subject>Population decline</subject><subject>Population growth</subject><subject>Prediction models</subject><subject>Regional development</subject><subject>Regional planning</subject><subject>Regression analysis</subject><subject>Social change</subject><subject>Sustainability</subject><subject>Trends</subject><subject>Urban populations</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpNkE9Lw0AQxRdRsNRe_AQBb0J09m-SowatQosV9By2ySxuWbNxNyv47U2toHOZx_DjDe8Rck7hivMKrmOiAkpRCnFEZgwKmlOQcPxPn5JFjDuYhnNaUTUjzxs_JKdH6_tsE7Cz7Y_0JqvfbI8R91eD7ZgC5iv8RJfVdrQYs1sdscsmdp3caAeH2dp36OIZOTHaRVz87jl5vb97qR_y1dPysb5Z5S2r5JjLaitEx8EILQFNS6WoVGGM0pxphI6Vsig4gNBCqUJyw7ZAjWGtYVBCV_I5uTj4DsF_JIxjs_Mp9NPLhinJgSnB-ERdHqg2-BinKM0Q7LsOXw2FZt9a89ca_waneF3J</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Chen, Lixuan</creator><creator>Mu, Tianyu</creator><creator>Li, Xiuting</creator><creator>Dong, Jichang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-3407-2999</orcidid></search><sort><creationdate>20220401</creationdate><title>Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models</title><author>Chen, Lixuan ; Mu, Tianyu ; Li, Xiuting ; Dong, Jichang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-59b44d30f4a50efc154967ff6a32ae0d285773004a466753f2b01ff2cf2080d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Aging</topic><topic>Birth rate</topic><topic>Cities</topic><topic>Decision making</topic><topic>Fertility</topic><topic>Geographical distribution</topic><topic>Grey prediction</topic><topic>Growth rate</topic><topic>Hypotheses</topic><topic>Labor force</topic><topic>Logit models</topic><topic>Metropolitan areas</topic><topic>Population decline</topic><topic>Population growth</topic><topic>Prediction models</topic><topic>Regional development</topic><topic>Regional planning</topic><topic>Regression analysis</topic><topic>Social change</topic><topic>Sustainability</topic><topic>Trends</topic><topic>Urban populations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Lixuan</creatorcontrib><creatorcontrib>Mu, Tianyu</creatorcontrib><creatorcontrib>Li, Xiuting</creatorcontrib><creatorcontrib>Dong, Jichang</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Lixuan</au><au>Mu, Tianyu</au><au>Li, Xiuting</au><au>Dong, Jichang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models</atitle><jtitle>Sustainability</jtitle><date>2022-04-01</date><risdate>2022</risdate><volume>14</volume><issue>8</issue><spage>4844</spage><pages>4844-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>In recent years, the population growth rate has been gradually declining in China. 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The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. 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subjects | Accuracy Aging Birth rate Cities Decision making Fertility Geographical distribution Grey prediction Growth rate Hypotheses Labor force Logit models Metropolitan areas Population decline Population growth Prediction models Regional development Regional planning Regression analysis Social change Sustainability Trends Urban populations |
title | Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models |
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