Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia

With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-so...

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Main Authors: Chenxi Cui, Yunfeng Hu, Yuhai Bao, Hao Li
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/13/12/426
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author Chenxi Cui
Yunfeng Hu
Yuhai Bao
Hao Li
author_facet Chenxi Cui
Yunfeng Hu
Yuhai Bao
Hao Li
author_sort Chenxi Cui
collection DOAJ
description With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-source data, such as nighttime light indices and road network density, various machine learning models—including random forest, XGBoost, and LightGBM—were employed to significantly improve prediction accuracy. Interpretable machine learning techniques were utilized to quantitatively analyze the contribution of various variables to population distribution. The results indicate that nighttime light indices and road network density are key influencing factors, revealing their complex nonlinear relationships with population density. This study provides new methodological support for predicting population density in Inner Mongolia and similar regions, demonstrating the potential of machine learning in regional population research. While machine learning models effectively capture correlations between variables, they do not reveal causal relationships. Future research should introduce more detailed data and causal inference models to deepen our understanding of population distribution and its influencing factors.
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institution Kabale University
issn 2220-9964
language English
publishDate 2024-11-01
publisher MDPI AG
record_format Article
series ISPRS International Journal of Geo-Information
spelling doaj-art-2dda337b8a3c4340933d5c2d2ae9e7c42024-12-27T14:30:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-11-01131242610.3390/ijgi13120426Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner MongoliaChenxi Cui0Yunfeng Hu1Yuhai Bao2Hao Li3College of Geographic Sciences, Inner Mongolia Normal University, Hohhot 010022, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Geographic Sciences, Inner Mongolia Normal University, Hohhot 010022, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaWith the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management. This study focuses on predicting population density at the township level in Inner Mongolia. By integrating multi-source data, such as nighttime light indices and road network density, various machine learning models—including random forest, XGBoost, and LightGBM—were employed to significantly improve prediction accuracy. Interpretable machine learning techniques were utilized to quantitatively analyze the contribution of various variables to population distribution. The results indicate that nighttime light indices and road network density are key influencing factors, revealing their complex nonlinear relationships with population density. This study provides new methodological support for predicting population density in Inner Mongolia and similar regions, demonstrating the potential of machine learning in regional population research. While machine learning models effectively capture correlations between variables, they do not reveal causal relationships. Future research should introduce more detailed data and causal inference models to deepen our understanding of population distribution and its influencing factors.https://www.mdpi.com/2220-9964/13/12/426Inner Mongoliapopulation densitymachine learningSHAP
spellingShingle Chenxi Cui
Yunfeng Hu
Yuhai Bao
Hao Li
Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
ISPRS International Journal of Geo-Information
Inner Mongolia
population density
machine learning
SHAP
title Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
title_full Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
title_fullStr Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
title_full_unstemmed Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
title_short Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia
title_sort population density prediction at township scale supported by machine learning method a case study in inner mongolia
topic Inner Mongolia
population density
machine learning
SHAP
url https://www.mdpi.com/2220-9964/13/12/426
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AT yunfenghu populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia
AT yuhaibao populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia
AT haoli populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia