[1] Wu Shuosheng, Qiu Xiaoming, Wang Le. Population Estimation Methods in GIS and Remote Sensing:A Review[J]. GIScience and Remote Sensing, 2005, 42(1):80-96.
[2] Flowerdew R, Green M. Developments in Areal Interpolation Methods and GIS[J]. The Annals of Regional Science, 1992, 26(1):67-78.
[3] Goodchild M F, Anselin L, Deichmann U. A Framework for the Areal Interpolation of Socioeconomic Data[J]. Environment and Planning A, 1993, 25(3):383-397.
[4] Mennis J. Generating Surface Models of Population Using Dasymetric Mapping[J]. The Professional Geographer, 2003, 55(1):31-42.
[5] Su M D, Lin R C, Hsieh R I, et al. Multi-layer Multi-class Dasymetric Mapping to Estimate Population Distribution[J]. Science of the Total Environment, 2010, 408(20):4807-4816.
[6] Zeng Chuiqing, Zhou Yi, Wang Shixin, et al. Population Spatialization in China Based on Night-Time Imagery and Land Use Data[J]. International Journal of Remote Sensing, 2011, 32(24):9599-9620.
[7] Yang Xuchao, Ye Tingting, Zhao Naizhuo, et al. Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data[J]. Remote Sensing, 2019, 11(5):574.
[8] Ye Tingting, Zhao Naizhuo, Yang Xuchao, et al. Improved Population Mapping for China Using Remotely Sensed and Points-Of-Interest Data Within a Random Forests Model[J]. Science of the Total Environment, 2019, 658:936-946.
[9] Sinha P, Gaughan A E, Stevens F R, et al. Assessing the Spatial Sensitivity of a Random Forest Model:Application in Gridded Population Modeling[J]. Computers, Environment and Urban Systems, 2019, 75:132-145.
[10] Robinson C, Hohman F, Dilkina B. A Deep Learning Approach for Population Estimation From Satellite Imagery[C]. Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, Los Angeles, 2017.
[11] Chen Jie, Pei Tao, Shaw Shih-Lung, et al. Fine-Grained Prediction of Urban Population Using Mobile Phone Location Data[J]. International Journal of Geographical Information Science, 2018, 32(1):1-17.
[12] Zhao Song, Liu Yanxu, Zhang Rui, et al. China's Population Spatialization Based on Three Machine Learning Models[J]. Journal of Cleaner Production, 2020, 256:120644.
[13] Leyk S, Gaughan A E. The Spatial Allocation of Population:A Review of Large-Scale Gridded Population Data Products and Their Fitness for Use[J]. Earth System Science Data, 2019, 11(3):1385-1409.
[14] Yu Bailang, Lian Ting, Huang Yixiu, et al. Integration of Nighttime Light Remote Sensing Images and Taxi GPS Tracking Data for Population Surface Enhancement[J]. International Journal of Geographical Information Science, 2019, 33(4):687-706.
[15] Langford M. Obtaining Population Estimates in Non-census Reporting Zones:An Evaluation of the 3-class Dasymetric Method[J]. Computers Environment and Urban Systems, 2006, 30(2):161-180.
[16] Chainey S P. Examining the Influence of Cell Size and Bandwidth Size on Kernel Density Estimation Crime Hotspot Maps for Predicting Spatial Patterns of Crime[J]. Bulletin of the Geographical Society of Liege, 2013, 60(1):7-19.
[17] Lin Yupin, Chu Hone-Jay, Wu Chenfa, et al. Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques[J]. International Journal of Environmental Research and Public Health, 2011, 8(1):75-88.
[18] Du Guoming, Zhang Shuwen, Zhang Youquan. Analyzing Spatial Auto-Correlation of Population Distribution:A case of Shenyang city[J]. Geographical Research, 2007, 26(2):383-390.
[19] Yuan Kunxiaojia, Cheng Xiaoqiang, Gui Zhipeng, et al. A Quad-Tree-Based Fast and Adaptive Kernel Density Estimation Algorithm for Heat-Map Generation[J]. International Journal of Geographical Information Science, 2019, 33(12):2455-2476.