Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China

Predicting fine-grained population distribution is crucial for effective urban planning. However, existing models widely ignore Region-Distinctive Geographical Factors (RDGF) in regional population modeling. This omission may compromise prediction accuracy, particularly in coastal zones where over 5...

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主要な著者:	Ku Gao, Xiaomei Yang, Yueming Liu, Qingyang Zhang, Zhihua Wang
フォーマット:	Artigo
言語:	Inglês
出版事項:	Elsevier 2026-03-01
シリーズ:	International Journal of Applied Earth Observations and Geoinformation
主題:	Urban population distribution Fine-grained predicting Machine learning Geographical factor Coastal zones
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S1569843226000841
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