Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street
Abstract
:1. Introduction
2. Literature Review
2.1. Data-Driven Quantitative Spatial Analyses
2.2. Spatial Evaluation Based on Multidimensional Sensory Perception
2.3. Spatial Evaluation Based on Machine Learning Algorithms
2.4. Literature Summary
3. Materials and Methods
3.1. Case Study
3.2. Research Framework
- ①
- Phase I: Quantitative analysis of space and feature clustering. Depthmap X and DBSCAN will quantify and analyse the spatial characteristics, determining the positioning of collection points and related parameters within the research area;
- ②
- Phase II: Selection of Multidimensional Sensory Perception Indicators and Data Collection. The Delphi Method and literature review will be utilised for screening questionnaire indicators and data collection, aiming to obtain multidimensional sensory perception evaluation data;
- ③
- Phase III: Determination of indicator weights and impact relationship analysis. The collected data will be weighted and processed, with model predictions conducted using Random Forest and XGBoost. This will ultimately yield the ranking of the importance of perceptual indicators in assessing the spatial quality of underground pedestrian streets, as shown in Figure 4.
3.3. Research Methodology
3.3.1. Quantitative Analysis of Space and Clustering of Features
- (1)
- Quantitative analysis
- (2)
- Feature clustering
3.3.2. Selection of Multidimensional Sensory Perception Indicators and Data Collection
- (1)
- Selection of indicators
- (2)
- Data acquisition
3.3.3. Indicator Weighting and Impact Relationship Analysis
- (1)
- Indicator weighting
- (2)
- Impact relationship analysis
3.4. Subject Situation
4. Results
4.1. The Result of Quantitative Analysis of Space and Feature Clustering
4.2. Questionnaire Reliability Analysis
4.3. Impact Relationship Analysis
4.3.1. Questionnaire Weighting Results
4.3.2. Correlation Analysis of Multidimensional Sensory Indicators with Spatial Quality Evaluation
4.3.3. Cross-Validation Analysis of Dual Model Regression
5. Discussion
5.1. Research Innovation
5.2. Limitations and Future Directions
6. Conclusions
6.1. Conclusions of the Research
6.2. Suggestion of Design and Application
- ①
- Pilot Testing in Diverse Neighborhoods: Begin by selecting a few representative neighbourhoods throughout the city that encompass various functional areas, including residential, commercial, and mixed-use zones. In these chosen neighbourhoods, conduct a comprehensive assessment of existing conditions, which should involve data collection on ambient noise, air quality, Vision landscapes, and pedestrian experiences. This approach guarantees that the pilot tests encompass a broad spectrum of urban environments, providing a solid foundation for scaling up the project;
- ②
- Multi-dimensional Sensory Evaluation: Implement a multi-dimensional sensory evaluation system in the pilot neighbourhoods, utilising advanced tools and software for comprehensive analysis. For example, employ sound level meters to quantify noise, gas detectors to assess air quality, three-dimensional (3D) scanning technology to capture Vision landscapes, and thermal imaging to evaluate thermal environments. Furthermore, subjective feedback from residents will be collected through meticulously designed questionnaires encompassing all sensory dimensions, including sight, sound, smell, and touch. This thorough data collection facilitates a holistic understanding of the sensory experiences within urban spaces;
- ③
- Virtual Reality and Simulation for Design Optimisation: Employ virtual reality (VR) and computer simulation technologies to enhance and optimise design before implementation. Create a VR model of the pilot neighbourhoods, allowing citizens to experience the proposed changes virtually and offer immediate feedback. Utilise simulation tools and machine learning techniques to forecast the effects of various design options, including noise propagation and air circulation models. This iterative process guarantees that the final design is practical and attuned to public input;
- ④
- City-wide Policy Development and Innovation Integration: Building on the insights and successes garnered from pilot projects, it is essential to develop city-wide policies and standards incorporating a multi-dimensional sensory evaluation system into urban planning practices. By synthesising data from smart city infrastructure and utilising advancements in AI-driven urban analytics, cities can establish dynamic, real-time sensory evaluation platforms that adapt to evolving urban conditions. Additionally, promoting the development of citizen science programs is essential, enabling residents to actively participate in sensory data collection, which fosters a stronger connection between the community and urban planners. These innovative strategies ensure that the sensory evaluation approach is systematically applied across the city while evolving continuously, integrating the latest scientific and technological advancements to enhance urban livability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Index Number | Spatial Cognitive Evaluation Indexes | Description |
---|---|---|
1 | Security | Represents the level of perceived safety and protection within the street environment. |
2 | Gorgeous | Refers to the visual appeal and aesthetic richness of the street’s design and features. |
3 | Non-Repression | Measures the sense of openness, avoiding feelings of confinement or claustrophobia in the street space. |
4 | Beauty | Reflects the overall attractiveness and harmonious design of the street. |
5 | Interesting | Captures the engaging and stimulating qualities of the street that hold the attention of pedestrians. |
6 | Open | Describes the spatial experience of openness and freedom, indicating a lack of clutter and barriers. |
7 | Comfortable Lighting | Assesses the adequacy, warmth, and distribution of lighting within the street, ensuring it supports visibility and comfort. |
8 | Quiet | Evaluates the level of noise control and the absence of disruptive sounds in the street space. |
9 | Varied | Describes the presence of a diverse range of sounds, contributing to a dynamic and lively acoustic environment. |
10 | Pleasant | Reflects the overall enjoyment of the soundscape, with pleasing auditory elements that enhance the street experience. |
11 | Good Ventilation | Measures the effectiveness of air movement and freshness in the street, contributing to physical comfort. |
12 | High Wind Speed | Assesses the presence and impact of wind within the street space, considering comfort and usability. |
13 | Warm | Describes the temperature comfort in the street, particularly in relation to warmth and cosiness. |
14 | Non-humid | Indicates the perceived dryness of the air, avoiding excessive humidity that may cause discomfort. |
15 | Flavor | Describes the presence of pleasant and varied scents, such as food aromas or natural fragrances, within the street. |
16 | Non-pungent | Evaluates the absence of harsh or unpleasant odours that could negatively affect the street environment. |
17 | (O)Varied | Indicates the variety of scents present in the street, contributing to a rich olfactory experience. |
Appendix B
Number | Indicators/Machine Learning Algorithms | Equation | Description |
---|---|---|---|
1 | Integration(Axis) | Integration measures the relationship between a specific space (or axis) and all other spaces within a spatial network. A higher integration value indicates that a space is more central or accessible within the network, making it more likely to attract movement or interaction. The formula for integration is based on the average depth value and is normalised to provide a standardised metric that can be compared across different spatial configurations. | |
is the integration value for axis i. m is the total number of axes in the system. represents the mean depth from axis i to all other axes | |||
2 | Connectivity(Axis) | Connectivity is the number of direct connections a space (or axis) has with other spaces within the spatial system. It reflects the immediate visibility or accessibility of a space, indicating how well it is connected to its surroundings. Higher connectivity suggests that the space is easily accessible from many other spaces, which may influence movement patterns and spatial interaction. | |
is Connectivity for axis i. is a binary variable indicating whether axis i is directly connected to axis j. If connected = 1; otherwise, = 0. n is the Total number of axes in the spatial system. | |||
3 | Step Depth(Axis) | Depth represents the distance from a specific point (or axis) to all other points within the spatial system. It is calculated as the sum of the shortest paths between a given point and all others, weighted by the number of nodes at each depth. This value increases as the distance from the point increases, providing insight into how accessible or isolated a space is within the layout. The average depth is commonly used as an indicator of global depth in spatial analysis. | |
: Step depth for the ith axis. d: The distance or ‘step’ between the ith axis and other points (nodes) in the spatial system : The number of nodes that are at distance d from the ith axis. m: The total number of elements (or nodes) in the spatial system. s: The maximum distance (or step) from the ith axis to any other node in the system. | |||
4 | Integration | Integration is a measure that indicates how easily a point can be accessed from all other points in the spatial system. It reflects the global connectivity of a point within the space. Higher integration values suggest that the point is more central and accessible, making it more significant in the overall spatial configuration. | |
: The Integration value for a specific space or axis line 2 : The Mean Depth value of a particular space or line i, which measures how many steps on average are needed to reach other spaces or lines from this specific one. n: The total number of spaces or axial lines within the system. | |||
5 | Connectivity | Connectivity refers to the number of other points or elements directly visible or accessible from a specific point. It is a measure of local visibility and accessibility, indicating how well-connected a particular point is to its immediate surroundings. | |
: Connectivity to the ith node or are in the system. : This term represents the connection between the ith node and the jth node. n: The total number of nodes or axes in the system. | |||
6 | Step Depth | Mean Depth (MD) represents the average shortest path distance from one point to all other points in the spatial system. It indicates the accessibility of a point within the space. A lower mean depth value suggests that the point is more accessible from other points, meaning it is closer to the centre of the spatial network. | |
is the step depth for space i. is the distance between space i and space j. n is the total number of spaces in the system. | |||
7 | Entropy Weighting | Calculates the information entropy, which is used to determine the distribution of weights among various criteria based on their information content. | |
: Entropy value for the jth indicator. : Probability distribution of the ith state for the jth indicator m: Total number of states | |||
8 | CRITIC Weighting | Objective weighting method that considers both the standard deviation and the correlation between criteria, giving more importance to more contrasting and uncorrelated indicators. | |
: Weight of the jth indicator. : Standard deviation of the jth indicator. : Correlation between the ith and jth indicators. m: Total number of indicators. | |||
9 | Random Forest Regression | Random Forest Regression combines the predictions of multiple decision trees to produce a more accurate and generalised model output. By averaging the predictions, it reduces the risk of overfitting and improves the model’s robustness, especially when dealing with complex and non-linear relationships in the data. | |
: Final prediction of the Random Forest model. M: Total number of decision trees in the forest. : Prediction from the kth decision tree for the input feature vector X. | |||
10 | XGBoost Regression | XGBoost (Extreme Gradient Boosting) enhances traditional boosting by incorporating regularisation, handling missing data, and using parallel computation, which leads to more accurate and efficient predictions. It sequentially builds trees where each new tree corrects errors made by the previous ones, with the goal of minimising a regularised loss function. | |
: Predicted value for the ith instance. K: Total number of trees in the model. : Prediction from the kth tree for the input |
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Spatial Quality Sensory Perception Evaluation Indicators | ||||||
---|---|---|---|---|---|---|
Vision perception evaluation | Strongly Disagree | Disagree | Neutral | Agree | Strongly agree | |
1. | Security | □1 | □2 | □3 | □4 | □5 |
2. | Gorgeous | □1 | □2 | □3 | □4 | □5 |
3. | Non-Repression | □1 | □2 | □3 | □4 | □5 |
4. | Beauty | □1 | □2 | □3 | □4 | □5 |
5. | Interesting | □1 | □2 | □3 | □4 | □5 |
6. | Open | □1 | □2 | □3 | □4 | □5 |
7. | Comfortable Lighting | □1 | □2 | □3 | □4 | □5 |
8. | Vision perception Satisfaction | □1 | □2 | □3 | □4 | □5 |
Auditory perception evaluation | Strongly Disagree | Disagree | Neutral | Agree | Strongly agree | |
9. | Quiet | □1 | □2 | □3 | □4 | □5 |
10. | Varied | □1 | □2 | □3 | □4 | □5 |
11. | Pleasant | □1 | □2 | □3 | □4 | □5 |
12. | Auditory perception Satisfaction | □1 | □2 | □3 | □4 | □5 |
Somatosensory perception evaluation | Strongly Disagree | Disagree | Neutral | Agree | Strongly agree | |
13. | Good Ventilation | □1 | □2 | □3 | □4 | □5 |
14. | High Wind Speed | □1 | □2 | □3 | □4 | □5 |
15. | Warm | □1 | □2 | □3 | □4 | □5 |
16. | non-humid | □1 | □2 | □3 | □4 | □5 |
17. | Somatosensory perception Satisfaction | □1 | □2 | □3 | □4 | □5 |
Olfactory perception evaluation | Strongly Disagree | Disagree | Neutral | Agree | Strongly agree | |
18. | Flavor | □1 | □2 | □3 | □4 | □5 |
19. | Non-pungent | □1 | □2 | □3 | □4 | □5 |
20. | (O)Varied | □1 | □2 | □3 | □4 | □5 |
21. | Olfactory perception Satisfaction | □1 | □2 | □3 | □4 | □5 |
Overall perception evaluation | Strongly Disagree | Disagree | Neutral | Agree | Strongly agree | |
22. | Overall Perception Satisfaction | □1 | □2 | □3 | □4 | □5 |
Indicator | Corrected Item-Total Correlation (CITC) | The Alpha Coefficient for the Item Deleted Cronbach | Alpha Coefficient |
---|---|---|---|
Good Ventilation | 0.606 | 0.572 | 0.697 |
High Wind Speed | 0.597 | 0.576 | |
Warm | 0.416 | 0.675 | |
Dry | 0.191 | 0.737 | |
Dissatisfied | 0.506 | 0.623 | |
Standardised Cronbach’s Alpha Coefficient: 0.691 |
Critic Weighting Method | Entropy Weighting Method | Integrated Weighting Method | ||||||
---|---|---|---|---|---|---|---|---|
Vision dimension | 1.081 | 1.043 | 1.127 | 22.977 | 0.977 | 0.023 | 25.822 | 24.399 |
Auditory dimension | 1.094 | 1.160 | 1.269 | 25.858 | 0.974 | 0.026 | 30.006 | 27.932 |
Somatosensory dimension | 1.049 | 1.178 | 1.236 | 25.180 | 0.980 | 0.020 | 23.317 | 24.249 |
Olfactory dimension | 1.021 | 1.249 | 1.275 | 25.985 | 0.982 | 0.018 | 20.856 | 23.421 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Yao, T.; Xu, Y.; Sun, L.; Liao, P.; Wang, J. Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street. Land 2024, 13, 1354. https://doi.org/10.3390/land13091354
Yao T, Xu Y, Sun L, Liao P, Wang J. Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street. Land. 2024; 13(9):1354. https://doi.org/10.3390/land13091354
Chicago/Turabian StyleYao, Tianning, Yao Xu, Liang Sun, Pan Liao, and Jin Wang. 2024. "Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street" Land 13, no. 9: 1354. https://doi.org/10.3390/land13091354