The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning
Abstract
:1. Introduction
1.1. Measuring Large-Scale Perceived Greenery Using Street Views and Deep Learning
1.2. A Novel Hedonic Price Modeling Framework Based on MGWR
2. Materials and Methods
2.1. Study Area
2.2. Analytical Framework
2.3. The Calculation of Neighborhood-Level Perceived Greenery
2.4. Hedonic Price Models (HPMs) and Characteristic Statistics
2.4.1. The Ordinary Least Square (OLS) Model
2.4.2. The Geographical Weighted Regression (GWR) Model
2.4.3. The Multiscale GWR Model
2.4.4. Characteristic Description and Statistics
3. Results and Discussion
3.1. Distribution Map of Neighborhood-Level Perceived Greenery
3.2. The Empirical Results and Hedonic Premium Analysis
3.2.1. The Regression Result of the Global OLS Model
3.2.2. Performance Comparison
3.2.3. Explanation of Marginal Premiums
3.3. The Spatial Patterns of Perceived Greenery Premiums
3.4. Policy Recommendations
3.5. Limitations and Future Work
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Variables | Description | Mean | Standard Error |
---|---|---|---|---|
Dependent variable | LNHP | Log selling price in 10,000 RMB (Chinese currency, US $1 = RMB 6.497) | 5.667 | 0.536 |
Structure characteristics | AREA | Average usable area in the home (m2) | 88.450 | 46.84 |
ORI | Dummy variable; 1 if the building windows face south | 0.783 | 0.412 | |
FLOOR | Average number of floors in the building | 11.539 | 11.861 | |
AGE | 2021 minus the year of construction of the building | 19.641 | 13.747 | |
PR | Floor-area ratio | 2.524 | 1.549 | |
GR | Green coverage rate (%) | 32.462 | 7.486 | |
PF | Property management fee (RMB/m2/ month) | 1.764 | 1.419 | |
Neighborhood characteristics | BUS_D | Road distance to the nearest bus station (km) | 0.236 | 0.205 |
ENT_D | Road distance to the nearest entertainment facility (km) | 0.132 | 0.215 | |
HSP_D | Road distance to the nearest hospital (km) | 0.182 | 0.219 | |
EDU_D | Road distance to the nearest school (km) | 0.182 | 0.239 | |
SOP_D | Road distance to the nearest store (km) | 0.097 | 0.172 | |
SUB_D | Road distance to the nearest subway station (km) | 1.471 | 1.334 | |
GRE_D | Road distance to the nearest green space (km) | 0.253 | 0.245 | |
WAT_D | Road distance to the nearest water body (km) | 0.732 | 0.503 | |
LNPG | Logarithmic of average perceived greenery at the house level | 2.798 | 0.337 |
Variables | Model 1: OLS Regression | ||
---|---|---|---|
Unstandardized Coefficients | Standard Error | p-Value | |
Constant | 4.752 ** | 0.071 | 0.000 |
Structure characteristics | |||
AREA | 0.007 ** | 0.000 | 0.000 |
ORI | 0.012 | 0.017 | 0.484 |
FLOOR | 0.004 ** | 0.001 | 0.000 |
AGE | 0.001 ** | 0.001 | 0.001 |
PR | 0.005 | 0.004 | 0.248 |
GR | 0.003 * | 0.001 | 0.003 |
PF | 0.056 ** | 0.006 | 0.000 |
Neighborhood characteristics | |||
BUS_DIS | 0.173 ** | 0.034 | 0.000 |
ENT_DIS | −0.075 | 0.040 | 0.061 |
HSP_DIS | −0.104 * | 0.040 | 0.010 |
EDU_DIS | −0.091 * | 0.031 | 0.004 |
SOP_DIS | −0.102 | 0.057 | 0.072 |
SUB_DIS | −0.069 ** | 0.005 | 0.000 |
GRE_DIS | −0.012 * | 0.028 | 0.010 |
WAT_DIS | 0.060 ** | 0.013 | 0.000 |
LNPG | 0.105 ** | 0.020 | 0.000 |
R2 | 0.653 | ||
Adjusted R2 | 0.650 | ||
AICc | 2723 | ||
RSS | 433 |
Model 2: GWR | Model 3: MGWR | |||||
---|---|---|---|---|---|---|
Variables | Unstandardized Coefficients (Mean) | Standard Error | Bandwidth | Unstandardized Coefficients (Mean) | Standard Error | Bandwidth |
Constant | 4.750 ** | 0.446 | 360 | 4.560 ** | 0.240 | 54 |
Structure characteristics | ||||||
AREA | 0.008 ** | 0.001 | - | 0.008 ** | 0.001 | 122 |
ORI | 0.073 | 0.005 | - | 0.007 | 0.001 | 3175 |
FLOOR | 0.002 ** | 0.005 | - | 0.004 ** | 0.000 | 3175 |
AGE | −0.002 ** | 0.003 | - | −0.002 ** | 0.000 | 3175 |
PR | −0.007 | 0.011 | - | −0.009 | 0.000 | 3175 |
GR | 0.002 ** | 0.004 | - | 0.003 ** | 0.000 | 3175 |
PF | 0.044 ** | 0.037 | - | 0.020 ** | 0.000 | 3175 |
Neighborhood characteristics | ||||||
BUS_D | 0.034 ** | 0.117 | - | 0.031 ** | 0.004 | 3172 |
ENT_D | 0.045 * | 0.219 | - | 0.062 * | 0.056 | 1735 |
HSP_D | 0.035 * | 0.144 | - | 0.026 * | 0.003 | 3175 |
EDU_D | −0.039 * | 0.173 | - | −0.028 * | 0.003 | 519 |
SOP_D | 0.038 | 0.235 | - | 0.028 | 0.009 | 3132 |
SUB_D | −0.016 ** | 0.052 | - | −0.019 ** | 0.001 | 3175 |
GRE_D | −0.003 ** | 0.124 | - | −0.019 ** | 0.003 | 3175 |
WAT_D | 0.024 ** | 0.107 | - | 0.083 ** | 0.029 | 1063 |
LNPG | 0.019 ** | 0.121 | - | 0.041 ** | 0.000 | 3175 |
R2 | 0.810 | 0.814 | ||||
Adjusted R2 | 0.782 | 0.802 | ||||
AICc | 883 | 378 | ||||
RSS | 185 | 179 |
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Zhang, Y.; Fu, X.; Lv, C.; Li, S. The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning. Int. J. Environ. Res. Public Health 2021, 18, 6809. https://doi.org/10.3390/ijerph18136809
Zhang Y, Fu X, Lv C, Li S. The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning. International Journal of Environmental Research and Public Health. 2021; 18(13):6809. https://doi.org/10.3390/ijerph18136809
Chicago/Turabian StyleZhang, Yonglin, Xiao Fu, Chencan Lv, and Shanlin Li. 2021. "The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning" International Journal of Environmental Research and Public Health 18, no. 13: 6809. https://doi.org/10.3390/ijerph18136809