Measuring Urban Green Space Exposure Based on Street View Images and Machine Learning
Abstract
:1. Introduction
1.1. Assessment of GS Exposure
1.2. Potential of Street View Images in GS Exposure Studies
2. Methodology
2.1. Framework for Measuring GS Exposure
- (1)
- Availability of GSs: This metric assesses the overall quantity of GSs, representing the source of GS exposure. We quantified the total amount of GSs from a visual perspective by collecting street view images and using semantic image segmentation techniques. This method facilitates a broad and precise measurement of GS exposure at an everyday-contact scale.
- (2)
- Accessibility of GSs: This metric signifies the ease with which one can reach GSs. This affects the likelihood and frequency of individuals encountering GSs, subsequently influencing GS exposure. To align with street view image data, we used spatial syntax to compute the accessibility of urban roads, thereby characterizing the accessibility of GSs.
- (3)
- Attractiveness of GSs: Attractiveness is a human-scale, subjective perceptual metric that, to some extent, represents the quality of GSs. The attractiveness of a GS can affect the visitation intent and duration of residents, further influencing GS exposure. Based on subjective attractiveness scores from street view images, we employed a machine learning model based on a support vector machine (SVM) to predict the city-scale attractiveness of GSs.
2.2. Study Area
2.3. Street View Data Collection
2.4. Measurement of GSs’ Availability Based on Image Segmentation
2.5. Measurement of GSs’ Accessibility Based on Spatial Syntax
2.6. Measurement of GSs’ Attractiveness Based on Machine Learning
2.7. Measurement of GS Exposure
2.8. Analysis of the Association between GS Exposure and the Built Environment
2.8.1. Measurement of Micro and Macro Characteristics of Built Environment
2.8.2. Regression Analysis
2.8.3. Geodetector Analysis
3. Results
3.1. Measurement of GS Exposure Metrics
3.2. GS Exposure Measurements and Spatial Distribution Characteristics
3.3. Regression Analysis Results
3.4. Geodetector Analysis Results
4. Discussion
4.1. Multidimensional Framework for Measuring Urban GS Exposure
4.2. Spatial Variation in GS Exposure in Downtown Shanghai
4.3. Micro and Macro Influences of Built Environment on GS Exposure
4.4. Policy Suggestions
4.5. Limitations and Future Directions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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POI Types | Categories | Number | Percentage |
---|---|---|---|
Residential | Commercial building, residential building, residential community. | 39,108 | 4.54% |
Enterprise | Enterprise, company, factory. | 149,511 | 17.36% |
Commercial | Food, beverage, shopping mall, market, store, theatre, bank, security company, insurance company. | 506,769 | 58.86% |
Public service | Hospital, school, governmental organization, social group, management institution, logistics site, human services center, repair site. | 127,788 | 14.84% |
Entertainment | Scenic spot, park, open square, tourist attraction, gym, leisure place. | 37,852 | 4.40% |
Variables (Unit) | Min. | Max. | Mean | SD |
---|---|---|---|---|
Dependent variable | ||||
GS exposure | 0 | 1 | 0.177 | 0.129 |
Independent variables | ||||
Microscale built environment | ||||
Building view index | 0 | 0.552 | 0.156 | 0.094 |
Sky view index | 0 | 0.504 | 0.190 | 0.088 |
Roadway view index | 0.001 | 0.407 | 0.241 | 0.057 |
Sidewalk view index | 0 | 0.251 | 0.054 | 0.028 |
Macroscale built environment | ||||
Building density | 0 | 5.225 | 1.224 | 0.724 |
Street intersections (N) | 0 | 193 | 13.794 | 18.636 |
Street density (m/m2) | 0 | 57.466 | 12.515 | 8.088 |
POI diversity | 0 | 1.591 | 0.981 | 0.345 |
POI quantity (N) | 0 | 2088 | 136.485 | 190.507 |
Residential POIs (N) | 0 | 70 | 8.244 | 9.572 |
Enterprise POIs (N) | 0 | 236 | 18.156 | 25.872 |
Commercial POIs (N) | 0 | 1959 | 88.481 | 146.685 |
Public service POIs (N) | 0 | 303 | 15.072 | 20.648 |
Entertainment POIs (N) | 0 | 90 | 6.532 | 9.573 |
Transit stops (N) | 0 | 75 | 2.141 | 2.829 |
Principal Component | Eigenvalues | Variance Explained (%) | Cumulative Variance Explained (%) |
---|---|---|---|
PC1 | 1.603 | 53.4 | 53.4 |
PC2 | 1.006 | 33.5 | 86.9 |
PC3 | 0.393 | 13.1 | 100.0 |
Metrics | PC1 | PC2 | PC3 |
---|---|---|---|
Availability | 0.000 | 0.000 | 1.000 |
Accessibility | 0.000 | 1.000 | 0.000 |
Attractiveness | 1.000 | 0.000 | 0.000 |
Microscale Built Environment | Macroscale Built Environment | ||
---|---|---|---|
F-Statistic | 128.839 *** | F-Statistic | 13.449 *** |
Breusch–Pagan test | 253.335 *** | Breusch–Pagan test | 50.083 *** |
Koenker–Bassett test | 99.787 *** | Koenker–Bassett test | 22.656 *** |
Lagrange multiplier (lag) | 640.411 *** | Lagrange multiplier (lag) | 855.456 *** |
Robust LM (lag) | 60.517 *** | Robust LM (lag) | 34.975 *** |
Lagrange multiplier (error) | 583.391 *** | Lagrange multiplier (error) | 822.696 *** |
Robust LM (error) | 3.497 *** | Robust LM (error) | 2.216 *** |
Variables | OLS | SLM | SEM | |||
---|---|---|---|---|---|---|
Coef. (SE) | p-Value | Coef. (SE) | p-Value | Coef. (SE) | p-Value | |
Microscale built environment | ||||||
Building view index | −0.479 (0.031) | <0.001 *** | −0.373 (0.028) | <0.001 *** | −0.376 (0.035) | <0.001 *** |
Sky view index | −0.694 (0.036) | <0.001 *** | −0.498 (0.033) | <0.001 *** | −0.548 (0.038) | <0.001 *** |
Roadway view index | 0.040 (0.042) | 0.338 | −0.029 (0.038) | 0.449 | −0.029 (0.046) | 0.523 |
Sidewalk view index | 0.296 (0.105) | <0.001 *** | 0.263 (0.094) | 0.005 ** | 0.301 (0.102) | <0.001 *** |
Micro R2 | 0.162 | 0.321 | 0.315 | |||
Micro LL | 1910 | 2128 | 2112 | |||
Micro AIC | −3809 | −4244 | −4213 | |||
Macroscale built environment | ||||||
Building density | 0.017 (0.004) | <0.001 *** | 0.012 (0.003) | <0.001 *** | 0.016 (0.004) | <0.001 *** |
Street intersection | 1.275 × 10−4 (1.524 × 10−4) | 0.403 | 1.833 × 10−4 (1.332 × 10−4) | 0.169 | 2.867 × 10−4 (1.524 × 10−4) | 0.060 |
Street density | 8.987 × 10−4 (3.529 × 10−4) | 0.011 * | 2.147 × 10−4 (3.085 × 10−4) | 0.487 | 1.648 × 10−4 (3.502 × 10−4) | 0.638 |
POI diversity | 0.036 (0.007) | <0.001 *** | 0.018 (0.007) | 0.007 ** | 0.014 (0.007) | 0.049 * |
POI quantity | −3.228 × 10−5 (1.584 × 10−5) | 0.042 * | −2.607 × 10−5 (1.384 × 10−5) | 0.060 | −1.333 × 10−5 (1.572 × 10−5) | 0.396 |
Transit stops | 0.001 (0.001) | 0.297 | 0.001 (0.001) | 0.410 | 0.001 (0.001) | 0.402 |
Macro-R2 | 0.029 | 0.257 | 0.256 | |||
Macro-LL | 1713 | 1988 | 1985 | |||
Macro-AIC | −3413 | −3960 | −3956 |
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Zhang, T.; Wang, L.; Hu, Y.; Zhang, W.; Liu, Y. Measuring Urban Green Space Exposure Based on Street View Images and Machine Learning. Forests 2024, 15, 655. https://doi.org/10.3390/f15040655
Zhang T, Wang L, Hu Y, Zhang W, Liu Y. Measuring Urban Green Space Exposure Based on Street View Images and Machine Learning. Forests. 2024; 15(4):655. https://doi.org/10.3390/f15040655
Chicago/Turabian StyleZhang, Tianlin, Lei Wang, Yike Hu, Wenzheng Zhang, and Yuyang Liu. 2024. "Measuring Urban Green Space Exposure Based on Street View Images and Machine Learning" Forests 15, no. 4: 655. https://doi.org/10.3390/f15040655