Next Article in Journal
Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images
Previous Article in Journal
Indoor Positioning Using PnP Problem on Mobile Phone Images
Open AccessArticle

Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data

by Yun Zhou 1,2, Mingguo Ma 1,2,*, Kaifang Shi 1,2 and Zhenyu Peng 3
1
Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem, Ministry of Education, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2
Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715, China
3
Chongqing Engineering Research Center for Big Data Application in Spatial Planning, Chongqing Planning & Design Institute, Chongqing 401120, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 369; https://doi.org/10.3390/ijgi9060369
Received: 14 April 2020 / Revised: 29 May 2020 / Accepted: 29 May 2020 / Published: 3 June 2020
Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p < 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics. View Full-Text
Keywords: population mapping; points of interest; random forest; urban area; Chongqing population mapping; points of interest; random forest; urban area; Chongqing
Show Figures

Figure 1

MDPI and ACS Style

Zhou, Y.; Ma, M.; Shi, K.; Peng, Z. Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data. ISPRS Int. J. Geo-Inf. 2020, 9, 369.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop