Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data
Dados Bibliográficos
| ID | 22034281 |
|---|---|
| Autores | Yun Zhou (0000-0002-3396-5867, Southwest University), Mingguo Ma (0000-0002-6397-2929, Southwest University, autor correspondente), Kaifang Shi (0000-0001-9047-2885, Southwest University), Zhenyu Peng (0000-0003-4574-425X, Chongqing Municipal Health Commission) |
| Ano | 2020 |
| Volume | 9 |
| Fascículo | 6 |
| Páginas | 369 |
| Data de publicação | 2020-06-03 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | ISPRS International Journal of Geo-Information (JOURNAL) |
| Identificadores do periódico | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Editora | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9060369 |
| OpenAlex | W3032953096 |
| Idioma | EN |
| Citações recebidas | 4 |
| Referências citadas | 65 |
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
Cartography · Estimation · Geography · Impervious surface · Population · Random forest · Regression · Regression analysis · Statistics · Computer Science · Demography · Environmental Science · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Land Use and Ecosystem Services · Mathematics · Artificial Intelligence · Ecology
Building-Level Population Estimation Method Using a Bayesian-Informed Hierarchical Learning Model
Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy
Population Density Prediction at Township Scale Supported by Machine Learning Method
A two-level random forest model for predicting the population distributions of urban functional zones
Variable Importance Assessment in Regression
A random forest guided tour
Taking Advantage of the Improved Availability of Census Data
Spatially disaggregated population estimates in the absence of national population and housing census data
Dynamic population mapping using mobile phone data
Global demographic trends and future carbon emissions
WorldPop, open data for spatial demography
MGWR
Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
Mining point-of-interest data from social networks for urban land use classification and disaggregation
Geographically Weighted Regression
Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model
A new look at the statistical model identification
Greedy function approximation
Random Forests
Geographically Weighted Regression
Urban cooling island effect of main river on a landscape scale in Chongqing, China
Causal Interpretations of Black-Box Models
World population in a grid of spherical quadrilaterals
Impacts of land finance on urban sprawl in China
Spatiotemporal distribution characteristics and mechanism analysis of urban population density
City profile
Density effect and optimum density of the urban population in China
Social Sensing
A Framework for the Areal Interpolation of Socioeconomic Data
Mapping the results of local statistics
Multimodel Inference
| Obras citantes distintas | 4 |
|---|---|
| Citações por ano | 0,67 |
| Intervalo de citações | 2020 - 2026 (7) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 4 |