How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Urban Vibrancy Indicators
- (1)
- City Social Vibrancy Index
- (2)
- City Cultural Vibrancy Index
- (3)
- City Economic Vibrancy Index
- (4)
- Comprehensive City Vibrancy Assessment
2.3.2. Built Environment Elements
3. Results
3.1. Spatial Distribution of Urban Vibrancy
3.2. Results of the Global Regression Model
3.3. Results of Geographically Weighted Regression
3.3.1. Diversity of Land-Use
3.3.2. Supporting Infrastructure
3.3.3. Road Transportation Networks
4. Discussion
4.1. Quantification of Urban Vibrancy and Big Data
4.2. City Vibrancy and Influencing Factors
4.2.1. Spatial Distribution Characteristics of Urban Vibrancy
4.2.2. Linkage between Built Environment Elements and Urban Vibrancy
- (1)
- Diversity of land-use
- (2)
- Supporting infrastructure
- (3)
- Road transportation networks
4.3. Recommendations to Promote Urban Vibrancy
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CRITIC | Criteria Importance Though Inter-criteria Correlation |
BHM | Baidu Heat Map |
NTL | Nighttime light |
POI | Point-of-interest |
Suomi-NPP | The Suomi National Polar-orbiting Partnership |
VIIRS | The visible infrared imaging radiometer suit |
GDP | Gross domestic product |
OLS | Ordinary least squares |
GWR | Geographically weighted regression |
GTWR | Geographically and temporally weighted regression |
USV | Urban Social Vibrancy |
UEV | Urban Economic Vibrancy |
UCV | Urban Cultural Vibrancy |
UV | Urban Comprehensive Vibrancy |
LMI | Land-use Mixture Index |
RLI | Residential Land Index |
CLI | Commercial Land Index |
PALI | Public Amenity Land Index |
DI | Distance Index |
RCI | Road Closeness Index |
RBI | Road Betweenness Index |
Appendix A
Integration Category | Specific Type | Counts |
---|---|---|
Traffic service | Bus stop/Place name and address information | 2187 |
Education and culture | Education and culture services/Museums/Libraries/Theatres and concert halls/ Exhibition halls/Scientific research institutions and schools/Place name and address information | 1095 |
Catering service | Catering services/Place name and address information | 5841 |
Shopping service | Shopping services/Cars or motorcycle sales/Place name and address information | 10,982 |
Life service | Life services/Public facilities/Access facilities/Indoor facilities/Place name and address information | 6882 |
Corporate business | Access facilities/Place name and address information | 2190 |
Government body | Government agencies/Social organizations/Place name and address information | 1912 |
Accommodation service | Accommodation services/Place name and address information | 1928 |
Residential area | Business residence/Place name and address information | 1878 |
Health care | Health care services/Place name and address information | 1502 |
Leisure and entertainment | Sports and leisure services/Famous tourist sites/Place name and address information | 1772 |
Financial service | Financial insurance services/Place name and address information | 431 |
Variables | Definition | Mean | Standard Deviation | Data Source |
---|---|---|---|---|
Land-use Mixture | the Simpson index of the mixed use of 12 POI types | 0.157 | 0.285 | (b) 1 |
Percentage of Residential Land | The proportion of residential land areas in each unit | 0.031 | 0.119 | (a) 1 |
Percentage of Commercial Land | The proportion of commercial land areas in each unit | 0.011 | 0.063 | (a) 1 |
Percentage of Public Amenity Land | The proportion of public amenity land areas in each unit | 0.004 | 0.035 | (a) 1 |
Road Closeness | a measure of how easy it is for a road to reach other roads within the search radius | 34.370 | 61.333 | (c) 1 |
Road Betweenness | a measure of the probability of a road segment being passed by traffic flow | 770.402 | 1144.076 | (c) 1 |
Distance to the bus stops | minimum distance from the grid center point to the nearest bus stop | 180.155 | 261.953 | (d) 1 |
OLS Models | Coef | Beta Coef | p-Value | R Square | Ajusted R Square |
---|---|---|---|---|---|
Model 1 (r = 400) | 0.629860839 | 0.629240989 | |||
Intercept | 0.000 | 0.842 | |||
LMI | 0.057 | 0.132 | 0.000 | ||
RLI | 0.541 | 0.528 | 0.000 | ||
CLI | 0.437 | 0.216 | 0.000 | ||
PALI | 0.385 | 0.145 | 0.000 | ||
DI | −0.007 | −0.017 | 0.098 | ||
RCI | 0.171 | 0.199 | 0.000 | ||
RBI | −0.081 | −0.085 | 0.000 | ||
Model 2 (r = 800) | 0.682883193 | 0.682352136 | |||
Intercept | −0.004 | 0.025 | |||
LMI | 0.041 | 0.094 | 0.000 | ||
RLI | 0.432 | 0.422 | 0.000 | ||
CLI | 0.362 | 0.179 | 0.000 | ||
PALI | 0.336 | 0.127 | 0.000 | ||
DI | −0.022 | −0.049 | 0.000 | ||
RCI | 0.362 | 0.387 | 0.000 | ||
RBI | −0.112 | −0.118 | 0.000 | ||
Model 3 (r = 1200) | 0.716157379 | 0.715682045 | |||
Intercept | −0.004 | 0.024 | |||
LMI | 0.032 | 0.075 | 0.000 | ||
RLI | 0.361 | 0.353 | 0.000 | ||
CLI | 0.326 | 0.162 | 0.000 | ||
PALI | 0.294 | 0.111 | 0.000 | ||
DI | −0.022 | −0.050 | 0.000 | ||
RCI | 0.503 | 0.474 | 0.000 | ||
RBI | −0.078 | −0.084 | 0.000 | ||
Model 4 (r = 5000) | 0.720615665 | 0.720147797 | |||
Intercept | 0.001 | 0.339 | |||
LMI | 0.025 | 0.057 | 0.000 | ||
RLI | 0.343 | 0.335 | 0.000 | ||
CLI | 0.291 | 0.144 | 0.000 | ||
PALI | 0.280 | 0.106 | 0.000 | ||
DI | −0.030 | −0.067 | 0.000 | ||
RCI | 0.490 | 0.547 | 0.000 | ||
RBI | −0.163 | −0.111 | 0.000 | ||
Model 5 (r = 8000) | 0.683390959 | 0.682860752 | |||
Intercept | 0.000 | 0.825 | |||
LMI | 0.033 | 0.075 | 0.000 | ||
RLI | 0.414 | 0.405 | 0.000 | ||
CLI | 0.329 | 0.163 | 0.000 | ||
PALI | 0.321 | 0.121 | 0.000 | ||
DI | −0.033 | −0.075 | 0.000 | ||
RCI | 0.302 | 0.432 | 0.000 | ||
RBI | −0.119 | −0.078 | 0.000 | ||
Model 6 (r = N) | 0.64689031 | 0.646298978 | |||
Intercept | −0.005 | 0.002 | |||
LMI | 0.045 | 0.103 | 0.000 | ||
RLI | 0.498 | 0.486 | 0.000 | ||
CLI | 0.402 | 0.199 | 0.000 | ||
PALI | 0.359 | 0.136 | 0.000 | ||
DI | −0.027 | −0.061 | 0.000 | ||
RCI | 0.126 | 0.256 | 0.000 | ||
RBI | 0.023 | 0.010 | 0.323 |
OLS Models (UCV) | Coef | Beta Coef | p-Value | R Square | Ajusted R Square |
---|---|---|---|---|---|
Model 1 (r = 400) | 0.558 | 0.557 | |||
Intercept | 0.000 | ||||
LMI | 0.038 | 0.137 | 0.000 | ||
RLI | 0.361 | 0.556 | 0.000 | ||
CLI | 0.190 | 0.148 | 0.000 | ||
PALI | 0.270 | 0.161 | 0.000 | ||
DI | −0.014 | −0.048 | 0.000 | ||
RCI | 0.044 | 0.080 | 0.000 | ||
RBI | −0.030 | −0.049 | 0.000 | ||
Model 2 (r = 800) | 0.601 | 0.600 | |||
Intercept | −0.002 | 0.057 | |||
LMI | 0.027 | 0.097 | 0.000 | ||
RLI | 0.301 | 0.464 | 0.000 | ||
CLI | 0.145 | 0.113 | 0.000 | ||
PALI | 0.245 | 0.146 | 0.000 | ||
DI | −0.026 | −0.093 | 0.000 | ||
RCI | 0.182 | 0.306 | 0.000 | ||
RBI | −0.070 | −0.117 | 0.000 | ||
Model 3 (r = 1200) | 0.658 | 0.658 | |||
Intercept | −0.003 | 0.010 | |||
LMI | 0.018 | 0.065 | 0.000 | ||
RLI | 0.241 | 0.371 | 0.000 | ||
CLI | 0.110 | 0.085 | 0.000 | ||
PALI | 0.212 | 0.126 | 0.000 | ||
DI | −0.031 | −0.109 | 0.000 | ||
RCI | 0.314 | 0.467 | 0.000 | ||
RBI | −0.068 | −0.115 | 0.000 | ||
Model 4 (r = 5000) | 0.668 | 0.668 | |||
Intercept | 0.001 | 0.440 | |||
LMI | 0.014 | 0.050 | 0.000 | ||
RLI | 0.224 | 0.345 | 0.000 | ||
CLI | 0.082 | 0.064 | 0.000 | ||
PALI | 0.203 | 0.121 | 0.000 | ||
DI | −0.034 | −0.121 | 0.000 | ||
RCI | 0.325 | 0.573 | 0.000 | ||
RBI | −0.164 | −0.175 | 0.000 | ||
Model 5 (r = 8000) | 0.610 | 0.609 | |||
Intercept | −0.001 | 0.540 | |||
LMI | 0.021 | 0.078 | 0.000 | ||
RLI | 0.284 | 0.437 | 0.000 | ||
CLI | 0.119 | 0.093 | 0.000 | ||
PALI | 0.237 | 0.141 | 0.000 | ||
DI | −0.033 | −0.117 | 0.000 | ||
RCI | 0.174 | 0.391 | 0.000 | ||
RBI | −0.113 | −0.118 | 0.000 | ||
Model 6 (r = N) | 0.569 | 0.568 | |||
Intercept | −0.003 | 0.024 | |||
LMI | 0.031 | 0.113 | 0.000 | ||
RLI | 0.341 | 0.526 | 0.000 | ||
CLI | 0.172 | 0.134 | 0.000 | ||
PALI | 0.260 | 0.155 | 0.000 | ||
DI | −0.025 | −0.087 | 0.000 | ||
RCI | 0.049 | 0.156 | 0.000 | ||
RBI | 0.007 | 0.005 | 0.679 |
OLS Models(UEV) | Coef | Beta Coef | p-Value | R Square | Ajusted R Square |
---|---|---|---|---|---|
Model 1 (r = 400) | 0.567 | 0.567 | |||
Intercept | 0.010 | 0.000 | |||
LMI | 0.048 | 0.177 | 0.000 | ||
RLI | 0.288 | 0.452 | 0.000 | ||
CLI | 0.282 | 0.224 | 0.000 | ||
PALI | 0.236 | 0.143 | 0.000 | ||
DI | 0.006 | 0.022 | 0.048 | ||
RCI | 0.089 | 0.167 | 0.000 | ||
RBI | −0.050 | −0.085 | 0.000 | ||
Model 2 (r = 800) | 0.608 | 0.608 | |||
Intercept | 0.008 | 0.000 | |||
LMI | 0.039 | 0.144 | 0.000 | ||
RLI | 0.229 | 0.360 | 0.000 | ||
CLI | 0.242 | 0.192 | 0.000 | ||
PALI | 0.209 | 0.127 | 0.000 | ||
DI | −0.002 | −0.008 | 0.457 | ||
RCI | 0.196 | 0.336 | 0.000 | ||
RBI | −0.069 | −0.117 | 0.000 | ||
Model 3 (r = 1200) | 0.653 | 0.652 | |||
Intercept | 0.008 | 0.000 | |||
LMI | 0.032 | 0.119 | 0.000 | ||
RLI | 0.178 | 0.280 | 0.000 | ||
CLI | 0.213 | 0.170 | 0.000 | ||
PALI | 0.181 | 0.110 | 0.000 | ||
DI | −0.004 | −0.016 | 0.091 | ||
RCI | 0.304 | 0.460 | 0.000 | ||
RBI | −0.059 | −0.101 | 0.000 | ||
Model 4 (r = 5000) | 0.695 | 0.695 | |||
Intercept | 0.011 | 0.000 | |||
LMI | 0.024 | 0.087 | 0.000 | ||
RLI | 0.144 | 0.226 | 0.000 | ||
CLI | 0.173 | 0.137 | 0.000 | ||
PALI | 0.162 | 0.098 | 0.000 | ||
DI | −0.013 | −0.046 | 0.000 | ||
RCI | 0.351 | 0.629 | 0.000 | ||
RBI | −0.145 | −0.158 | 0.000 | ||
Model 5 (r = 8000) | 0.655 | 0.655 | |||
Intercept | 0.009 | 0.000 | |||
LMI | 0.028 | 0.105 | 0.000 | ||
RLI | 0.190 | 0.298 | 0.000 | ||
CLI | 0.194 | 0.155 | 0.000 | ||
PALI | 0.191 | 0.116 | 0.000 | ||
DI | −0.016 | −0.058 | 0.000 | ||
RCI | 0.230 | 0.527 | 0.000 | ||
RBI | −0.140 | −0.148 | 0.000 | ||
Model 6 (r = N) | 0.589 | 0.588 | |||
Intercept | 0.006 | 0.000 | |||
LMI | 0.039 | 0.145 | 0.000 | ||
RLI | 0.260 | 0.408 | 0.000 | ||
CLI | 0.259 | 0.206 | 0.000 | ||
PALI | 0.220 | 0.134 | 0.000 | ||
DI | −0.008 | −0.029 | 0.010 | ||
RCI | 0.075 | 0.246 | 0.000 | ||
RBI | 0.018 | 0.013 | 0.230 |
OLS Models | Coef | Beta Coef | p-Value | R Square | Ajusted R Square |
---|---|---|---|---|---|
Model 1 (r = 400) | 0.655 | 0.654 | |||
Intercept | 0.003 | 0.013 | |||
LMI | 0.053 | 0.154 | 0.000 | ||
RLI | 0.448 | 0.551 | 0.000 | ||
CLI | 0.328 | 0.205 | 0.000 | ||
PALI | 0.335 | 0.160 | 0.000 | ||
DI | −0.007 | −0.020 | 0.040 | ||
RCI | 0.107 | 0.156 | 0.000 | ||
RBI | −0.057 | −0.076 | 0.000 | ||
Model 2 (r = 800) | 0.708 | 0.708 | |||
Intercept | 0.000 | 0.761 | |||
LMI | 0.039 | 0.114 | 0.000 | ||
RLI | 0.364 | 0.448 | 0.000 | ||
CLI | 0.269 | 0.168 | 0.000 | ||
PALI | 0.298 | 0.142 | 0.000 | ||
DI | −0.021 | −0.058 | 0.000 | ||
RCI | 0.271 | 0.365 | 0.000 | ||
RBI | −0.093 | −0.124 | 0.000 | ||
Model 3 (r = 1200) | 0.760 | 0.759 | |||
Intercept | 9.172 0.000 | 0.937 | |||
LMI | 0.030 | 0.087 | 0.000 | ||
RLI | 0.294 | 0.362 | 0.000 | ||
CLI | 0.230 | 0.144 | 0.000 | ||
PALI | 0.259 | 0.124 | 0.000 | ||
DI | −0.024 | −0.067 | 0.000 | ||
RCI | 0.418 | 0.497 | 0.000 | ||
RBI | −0.078 | −0.106 | 0.000 | ||
Model 4 (r = 5000) | 0.777 | 0.777 | |||
Intercept | 0.004 | 0.000 | |||
LMI | 0.023 | 0.066 | 0.000 | ||
RLI | 0.268 | 0.330 | 0.000 | ||
CLI | 0.192 | 0.120 | 0.000 | ||
PALI | 0.244 | 0.116 | 0.000 | ||
DI | −0.031 | −0.087 | 0.000 | ||
RCI | 0.435 | 0.612 | 0.000 | ||
RBI | −0.181 | −0.155 | 0.000 | ||
Model 5 (r = 8000) | 0.725 | 0.724 | |||
Intercept | 0.003 | 0.023 | |||
LMI | 0.030 | 0.089 | 0.000 | ||
RLI | 0.336 | 0.414 | 0.000 | ||
CLI | 0.229 | 0.143 | 0.000 | ||
PALI | 0.283 | 0.135 | 0.000 | ||
DI | −0.032 | −0.091 | 0.000 | ||
RCI | 0.260 | 0.467 | 0.000 | ||
RBI | −0.141 | −0.117 | 0.000 | ||
Model 6 (r = N) | 0.673 | 0.673 | |||
Intercept | −0.001 | 0.455 | |||
LMI | 0.043 | 0.125 | 0.000 | ||
RLI | 0.415 | 0.511 | 0.000 | ||
CLI | 0.301 | 0.188 | 0.000 | ||
PALI | 0.317 | 0.151 | 0.000 | ||
DI | −0.024 | −0.066 | 0.000 | ||
RCI | 0.090 | 0.230 | 0.000 | ||
RBI | 0.017 | 0.009 | 0.337 |
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Index | Abbreviation | Mean | Standard Deviation |
---|---|---|---|
Land-use Mixture Index | LMI | 0.17615635 | 0.31882732 |
Residential Land Index | RLI | 0.03536129 | 0.13483714 |
Commercial Land Index | CLI | 0.01238231 | 0.06834059 |
Public Amenity Land Index | PALI | 0.00598902 | 0.05220615 |
Distance Index | DI | 0.21211727 | 0.30846335 |
Road Closeness Index | RCI | 0.08632102 | 0.15405602 |
Road Betweenness Index | RBI | 0.06313485 | 0.09376886 |
OLS Models | Coef | Beta Coef | p-Value | Std. | Vif | R Square | Adjusted R Square | AICc |
---|---|---|---|---|---|---|---|---|
Model 1 (USV) 1 | 0.721 | 0.720 | −10,023.679 | |||||
Intercept | 0.001 | 0.339 | 0.005 | |||||
RLI | 0.343 | 0.335 | 0.000 | 0.074 | 1.661 | |||
CLI | 0.291 | 0.144 | 0.000 | 0.097 | 1.163 | |||
PALI | 0.280 | 0.106 | 0.000 | 0.113 | 1.079 | |||
DI | −0.030 | −0.067 | 0.000 | 0.017 | 1.142 | |||
RCI | 0.490 | 0.547 | 0.000 | 0.131 | 2.966 | |||
RBI | −0.163 | −0.111 | 0.000 | 0.071 | 1.696 | |||
Model 2 (UCV) 1 | 0.668 | 0.668 | −13,120.566 | |||||
Intercept | 0.001 | 0.440 | 0.004 | |||||
LMI | 0.014 | 0.050 | 0.000 | 0.006 | 1.357 | |||
RLI | 0.224 | 0.345 | 0.000 | 0.072 | 1.661 | |||
CLI | 0.082 | 0.064 | 0.000 | 0.084 | 1.163 | |||
PALI | 0.203 | 0.121 | 0.000 | 0.148 | 1.079 | |||
DI | −0.034 | −0.121 | 0.000 | 0.020 | 1.142 | |||
RCI | 0.325 | 0.573 | 0.000 | 0.142 | 2.966 | |||
RBI | −0.164 | −0.175 | 0.000 | 0.097 | 1.696 | |||
Model 3 (UEV) 1 | 0.695 | 0.695 | −13,626.702 | |||||
Intercept | 0.011 | 0.000 | 0.004 | |||||
LMI | 0.024 | 0.087 | 0.000 | 0.009 | 1.357 | |||
RLI | 0.144 | 0.226 | 0.000 | 0.056 | 1.661 | |||
CLI | 0.173 | 0.137 | 0.000 | 0.072 | 1.163 | |||
PALI | 0.162 | 0.098 | 0.000 | 0.096 | 1.079 | |||
DI | −0.013 | −0.046 | 0.000 | 0.012 | 1.142 | |||
RCI | 0.351 | 0.629 | 0.000 | 0.074 | 2.966 | |||
RBI | −0.145 | −0.158 | 0.000 | 0.073 | 1.696 | |||
Model 4 (UV) 1 | 0.777 | 0.776 | −12,905.897 | |||||
Intercept | 0.004 | 0.000 | 0.003 | |||||
LMI | 0.023 | 0.066 | 0.000 | 0.009 | 1.357 | |||
RLI | 0.268 | 0.330 | 0.000 | 0.060 | 1.661 | |||
CLI | 0.192 | 0.120 | 0.000 | 0.080 | 1.163 | |||
PALI | 0.244 | 0.116 | 0.000 | 0.092 | 1.079 | |||
DI | −0.031 | −0.087 | 0.000 | 0.017 | 1.142 | |||
RCI | 0.435 | 0.612 | 0.000 | 0.131 | 2.966 | |||
RBI | −0.181 | −0.155 | 0.000 | 0.087 | 1.696 |
GWR Models | Min | Median | Max | Mean | R Square | Adjusted R Square | AICc |
---|---|---|---|---|---|---|---|
Model 1 (USV) | 0.758 | 0.756 | −10,578.473 | ||||
Intercept | −0.021 | 0.000 | 0.007 | −0.001 | |||
LMI | −0.010 | 0.028 | 0.052 | 0.024 | |||
RLI | 0.027 | 0.292 | 0.375 | 0.278 | |||
CLI | 0.091 | 0.262 | 0.642 | 0.267 | |||
PALI | 0.179 | 0.293 | 0.823 | 0.333 | |||
DI | −0.052 | −0.014 | 0.021 | −0.016 | |||
RCI | 0.203 | 0.498 | 0.686 | 0.455 | |||
RBI | −0.264 | −0.093 | −0.004 | −0.106 | |||
Model 2 (UCV) | 0.748 | 0.745 | −14,210.057 | ||||
Intercept | −0.018 | 0.000 | 0.005 | −0.001 | |||
LMI | 0.003 | 0.015 | 0.026 | 0.014 | |||
RLI | 0.018 | 0.166 | 0.325 | 0.180 | |||
CLI | −0.033 | 0.098 | 0.419 | 0.110 | |||
PALI | 0.112 | 0.238 | 0.875 | 0.281 | |||
DI | −0.063 | −0.015 | 0.015 | −0.017 | |||
RCI | 0.025 | 0.226 | 0.517 | 0.250 | |||
RBI | −0.314 | −0.069 | 0.015 | −0.102 | |||
Model 3 (UEV) | 0.744 | 0.741 | −14,307.063 | ||||
Intercept | 0.007 | 0.011 | 0.026 | 0.012 | |||
LMI | 0.005 | 0.022 | 0.043 | 0.022 | |||
RLI | −0.012 | 0.110 | 0.194 | 0.107 | |||
CLI | 0.021 | 0.131 | 0.404 | 0.142 | |||
PALI | −0.097 | 0.111 | 0.263 | 0.118 | |||
DI | −0.032 | 0.000 | 0.025 | −0.001 | |||
RCI | 0.208 | 0.304 | 0.467 | 0.323 | |||
RBI | −0.267 | −0.086 | −0.012 | −0.104 | |||
Model 4 (UV) | 0.825 | 0.823 | −13,864.880 | ||||
Intercept | −0.009 | 0.003 | 0.011 | 0.003 | |||
LMI | 0.004 | 0.023 | 0.040 | 0.022 | |||
RLI | 0.014 | 0.216 | 0.313 | 0.213 | |||
CLI | 0.037 | 0.171 | 0.482 | 0.188 | |||
PALI | 0.175 | 0.260 | 0.663 | 0.285 | |||
DI | −0.056 | −0.010 | 0.011 | −0.014 | |||
RCI | 0.160 | 0.367 | 0.619 | 0.377 | |||
RBI | −0.313 | −0.097 | −0.001 | −0.119 |
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Gong, H.; Wang, X.; Wang, Z.; Liu, Z.; Li, Q.; Zhang, Y. How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment. Int. J. Environ. Res. Public Health 2022, 19, 12178. https://doi.org/10.3390/ijerph191912178
Gong H, Wang X, Wang Z, Liu Z, Li Q, Zhang Y. How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment. International Journal of Environmental Research and Public Health. 2022; 19(19):12178. https://doi.org/10.3390/ijerph191912178
Chicago/Turabian StyleGong, Hongyu, Xiaozihan Wang, Zihao Wang, Ziyi Liu, Qiushan Li, and Yunhan Zhang. 2022. "How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment" International Journal of Environmental Research and Public Health 19, no. 19: 12178. https://doi.org/10.3390/ijerph191912178