The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Materials
2.1.1. Study Area
2.1.2. Data
2.2. Methods
2.2.1. HSL Transformation and Saturation Calibration of ISS Photography
2.2.2. Generation of Functional Zones Based on POI and LSMM
2.2.3. Population Spatialization Based on the Random Forest Regression
- (1)
- Preparation of training data: To avoid meaningless accumulation of functional types as traditional methods did, urban functional zones were employed to slice ISS NTL into different layers. This combination produced five NTL layers covered by functions of residential areas, commercial areas, scenic spots, public service, and “others”. It is noted that the function of public service was merged into commercial areas due to its small proportion and relatively similar distribution patterns. Besides, the type of subdistricts was determined by thresholding as 16,000 people/km2, which divided the study area into two relatively homogeneous parts including high and low density. This paper selected four NTL function layers, population-density types, and urban height on the district level as independent variables of training data.
- (2)
- Samples and growth: In order to build b regression trees, b training samples were extracted from n cases of origin dataset based on the Bootstrap method. The rest samples were employed as out-of-bag (OOB) for validation. When constructing the Regression tree, n (n < 6) features were randomly chosen as candidate branch nodes from 6 independent variables. They were further determined as optimal branches based on feature bagging. Each Regression tree grew recursively from top to bottom and the number of trees was the termination condition for this growth.
- (3)
- OOB validation: These b Regression trees constitute the Random Forest model. The prediction performance of this model was evaluated by accuracy of out-of-bag (Equation (3)).
- (4)
- Prediction: Pixel population was predicted by averaging over predictions of the Random Forest Regression model. As a result, 25 m population distribution in the study area was generated based on RF model.
3. Results
3.1. Results of HSL and VANUI
3.2. Urban Functional Zones Based on Social Sensing Data
3.3. Fine-resolution Population Distribution
4. Discussion
4.1. Comparing Ability of Mapping Population Between Nighttime Light Data
4.2. Variable Importance of the Random Forset Model
4.3. Demographics for Spatialization and WorldPop for Validation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Li, K.; Chen, Y.; Li, Y. The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data. Remote Sens. 2018, 10, 1650. https://doi.org/10.3390/rs10101650
Li K, Chen Y, Li Y. The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data. Remote Sensing. 2018; 10(10):1650. https://doi.org/10.3390/rs10101650
Chicago/Turabian StyleLi, Kangning, Yunhao Chen, and Ying Li. 2018. "The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data" Remote Sensing 10, no. 10: 1650. https://doi.org/10.3390/rs10101650