Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio
Abstract
1. Introduction
1.1. The Paradox of Security and Pedagogy in Campus Design
- Oversimplified safety zoning: Traditional binary classifications of “safe/unsafe” zones fail to account for the gradient nature of the spatial permeability that is required for adolescent socialization. This rigid framework ignores transitional zones that facilitate both security and social interaction;
- Excessive visibility and its paradoxes: While visibility is a cornerstone of CPTED, overreliance on surveillance technologies (e.g., cameras) and transparent designs can disrupt peer socialization and raise privacy concerns [2,24]. Transparency, intended to enable casual supervision, may inadvertently expose students to unwanted attention or interruptions, undermining their perceived safety [6]. Visual connectivity between spaces can also yield counterproductive effects, such as bystander fixation during conflicts [25];
- Isolated security interventions: Conventional approaches prioritize fragmented measures over holistic spatial performance. These interventions often distort spatial layouts, creating compensatory blind spots and compromising their educational functionality.
1.2. Computational Morphology: Bridging Design and Security
- Space syntax: Introduced by Hillier and Hanson, space syntax is a theoretical and computational approach that applies graph theory to analyze spatial configurations [39,40]. Unlike traditional architectural analyses which are focused on the physical form of buildings, space syntax emphasizes topological relationships—how spaces connect, integrate, and sequence within a system [41]—and builds bridge a between spatial features and behavioral patterns [42]. Key elements in space syntax include axial lines, convex spaces, integration, connectivity, and depth. These metrics reveal how spatial arrangements influence the flow of activities, social behaviours, and interaction patterns [43];
- Parametric modelling: parametric modelling translates spatial relationships into mathematical formulations, shifting design from being qualitatively subjective to being quantitatively rigorous. Parametric design facilitates exploring design alternatives and optimizing spatial configurations based on a set of predefined constraints [44]. It is particularly useful for understanding the relationship between geometry and spatial performance, and includes visibility, circulation, and environmental factors;
- Computational methods and algorithms: These methods involve the use of computer algorithms to solve complex design problems. One popular tool is evolutionary algorithms (EAs), which can automate the exploration of complex design spaces, optimizing solutions for multi-objective problems [35]. Rule-based generative systems encode design logic into computational workflows, enabling data-driven spatial analyses and iterative refinements.
2. Material and Methods
2.1. Natural Visibility Metrics in Campus Safety Evaluation
2.2. Algorithmic Iteration and Spatial Analysis for Campus Layout Optimization
2.2.1. Campus Layout Configuration Generates Simulation
- 1)
- Although the functional configuration may change, the building form generally remains unchanged, and, consequently, the morphology of public spaces typically remains consistent as well;
- 2)
- When changes in the functional configuration lead to alterations in the morphology of public spaces, the building form is often modified concurrently. In such instances, the influence cannot be attributed to a single factor, making it challenging to evaluate;
- 3)
- If vertical changes in the building layout entail modifications to the spatial relationships between different buildings, both the building form and density will undergo corresponding adjustments. In these scenarios, the variables are no longer singular.
2.2.2. Morphological Features Extraction of Public Space
- (1)
- Determine the quantity of edges n, and then generate n degrees ai (i ∈ [1, n]), sorting them in ascending order;
- (2)
- Generate n distances di. The vertices can be uniquely determined in a polar coordinate system by ai and di. These vertices are then connected sequentially to form a polygon;
- (3)
- Scale this polygon until its area equals S.
2.3. Case Selection
3. Results
3.1. Building Layout with Natural Surveillance
3.1.1. General Relationships Between Building Layouts and Rva
3.1.2. The Analysis of High-Occlusion Spaces
- (1)
- The Rva weights all spaces equally, favoring concentrated high-visibility clusters;
- (2)
- The Rvh penalizes low-visibility extremes, requiring homogeneous visibility distribution.
3.2. Public Space Form
3.2.1. The Correlation Between Public Space Density and Natural Visibility
3.2.2. The Correlation Between Public Space Shape and Natural Visibility
3.3. Integrated Impacts of Spatial Configurations on Natural Visibility
4. The Spatial Distribution of Student Activities for the Validation of the Natural Visibility Rate Results
- A1–A4: designated student congregation zones during non-instructional intervals, with specialized programmatic allocations: A3: hardscape recreational facility with basketball courts; A4: synthetic turf playground integrating football field infrastructure;
- R1: primary vehicular thoroughfare governed by temporal access restrictions (vehicle prohibition during education operations: 08:00–18:00);
- L1: ecological buffer zone exhibiting minimal pedestrian interaction frequencies, serving primarily as visual relief space.
5. Discussion
5.1. Building Layout and Campus Safety
5.2. Public Space Morphology and Campus Safety
5.3. Reconciling Natural Visibility and Safety in Campus Design
5.4. Digital Technology for Solving Architectural Problems
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
Appendix C
Appendix D
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LYTa | LYTh | |
---|---|---|
LYTa | 1 | −0.357 * |
LYTh | −0.357 * | 1 |
LYTa | LYTh | |
---|---|---|
LYTa | 1 | −0.717 ** |
LYTh | −0.717 ** | 1 |
PA | OA | PSA | BCR | Rva | Rvh | |
---|---|---|---|---|---|---|
PA | / | 0.86792 *** | 0.9929 *** | −0.21593 | 0.73246 *** | 0.70424 *** |
OA | 0.86792 *** | / | 0.82164 *** | 0.20311 | 0.41268 ** | 0.40082 ** |
PSA | 0.9929 *** | 0.82164 *** | / | −0.2989 * | 0.78374 *** | 0.75835 *** |
BCR | −0.21593 | 0.20311 | −0.2989 * | / | −0.68295 *** | −0.64353 *** |
Rva | 0.73246 *** | 0.41268 ** | 0.78374 *** | −0.68295 | / | 0.96944 *** |
Rvh | 0.70424 *** | 0.40082 ** | 0.75835 *** | −0.64353 *** | 0.96944 *** | / |
Collinearity | Variables | VIF |
---|---|---|
High multicollinearity (VIF > 10) | BCR | 28.530 |
FA | 25.174 | |
Moderate multicollinearity (5 ≤ VIF ≤ 10) | CI | 6.390 |
Low multicollinearity (VIF < 5) | PSA | 4.308 |
LYTa | 1.261 | |
LYTh | 1.160 |
Collinearity | Variables | VIF |
---|---|---|
Moderate multicollinearity (5 ≤ VIF ≤ 10) | BCR | 5.670 |
CI | 5.310 | |
Low multicollinearity (VIF < 5) | PSA | 3.038 |
LYTa | 1.257 | |
LYTh | 1.142 |
Coefficient | Standard Error | t-Value | p-Value | ||
---|---|---|---|---|---|
Rva (adjusted R2 = 0.970) | Intercept | 0.008 | 0.024 | 0.319 | 0.751 |
PSA | 0.165 * | 0.050 | 3.339 | 0.002 | |
BCR | −0.038 | 0.046 | −0.839 | 0.406 | |
FA | −0.848 * | 0.053 | −16.046 | 0.000 | |
CI | 0.070 | 0.045 | 1.551 | 0.128 | |
LYTa | 0.021 | 0.027 | 0.794 | 0.431 | |
LYTh | −0.042 | 0.026 | −1.628 | 0.110 | |
Rvh (adjusted R2 = 0.950) | Intercept | −0.004 | 0.031 | −0.135 | 0.893 |
PSA | 0.178 * | 0.065 | 2.760 | 0.008 | |
BCR | −0.068 | 0.060 | −1.134 | 0.263 | |
FA | −0.746 * | 0.069 | −10.818 | 0.000 | |
CI | 0.090 | 0.059 | 1.527 | 0.134 | |
LYTa | −0.029 | 0.035 | −0.830 | 0.411 | |
LYTh | 0.125 * | 0.034 | 3.699 | 0.001 |
Coefficient | Standard Error | t-Value | p-Value | ||
---|---|---|---|---|---|
Rva (adjusted R2 = 0.799) | Intercept | 0.007 | 0.061 | 0.122 | 0.903 |
PSA | 0.677 | 0.097 | 6.976 | 0.000 | |
BCR | −0.446 * | 0.098 | −4.568 | 0.000 | |
CI | 0.310 * | 0.109 | 2.841 | 0.007 | |
LYTa | 0.024 | 0.069 | 0.353 | 0.725 | |
LYTh | −0.013 | 0.066 | −0.193 | 0.848 | |
Rvh (adjusted R2 = 0.824) | Intercept | −0.004 | 0.058 | −0.074 | 0.941 |
PSA | 0.628 * | 0.093 | 6.781 | 0.000 | |
BCR | −0.426 * | 0.093 | −4.572 | 0.000 | |
CI | 0.300 * | 0.104 | 2.889 | 0.006 | |
LYTa | −0.027 | 0.066 | −0.402 | 0.690 | |
LYTh | 0.150 | 0.063 | 2.385 | 0.021 |
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Liu, F.; Zhou, H.; Xie, J.; Tang, Y.; Liu, S. Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio. Buildings 2025, 15, 1472. https://doi.org/10.3390/buildings15091472
Liu F, Zhou H, Xie J, Tang Y, Liu S. Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio. Buildings. 2025; 15(9):1472. https://doi.org/10.3390/buildings15091472
Chicago/Turabian StyleLiu, Feng, Hao Zhou, Jiangtao Xie, Yue Tang, and Shuyu Liu. 2025. "Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio" Buildings 15, no. 9: 1472. https://doi.org/10.3390/buildings15091472
APA StyleLiu, F., Zhou, H., Xie, J., Tang, Y., & Liu, S. (2025). Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio. Buildings, 15(9), 1472. https://doi.org/10.3390/buildings15091472