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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\u2014including random forest, XGBoost, and LightGBM\u2014were 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.<\/jats:p>","DOI":"10.3390\/ijgi13120426","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T02:57:08Z","timestamp":1732849028000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Population Density Prediction at Township Scale Supported by Machine Learning Method: A Case Study in Inner Mongolia"],"prefix":"10.3390","volume":"13","author":[{"given":"Chenxi","family":"Cui","sequence":"first","affiliation":[{"name":"College of Geographic Sciences, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6251","authenticated-orcid":false,"given":"Yunfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yuhai","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Geographic Sciences, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","first-page":"1295","article-title":"Research progress and perspective on the spatialization of population data","volume":"18","author":"Dong","year":"2016","journal-title":"J. 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