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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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| 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. |
| format | Article |
| id | doaj-art-2dda337b8a3c4340933d5c2d2ae9e7c4 |
| 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 |
| work_keys_str_mv | AT chenxicui populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia AT yunfenghu populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia AT yuhaibao populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia AT haoli populationdensitypredictionattownshipscalesupportedbymachinelearningmethodacasestudyininnermongolia |