Analyzing the Relationship Between Built-Environment Factors and Safety Threat Reports in Cracow, Poland Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.3390/su17209300
· OA: W4415351063
With the acceleration of urbanization, the coupling relationship between the built environment and urban safety hazards has become increasingly prominent. Irrational spatial structures and resource allocations may aggravate safety hazards and negatively affect residents’ quality of life, thus requiring urgent scientific evaluation and optimization. However, existing studies mostly focus on linear correlation analysis, which makes it difficult to reveal the complex nonlinear mechanisms among multidimensional environmental factors. Taking Cracow (Kraków), Poland as the study area, this research utilizes multi-source spatial data to quantify environmental features such as transportation, socioeconomic conditions, visual landscapes, and public services, in order to uncover their role in the formation of safety hazards. An XGBoost-based safety hazard prediction model is constructed, and SHAP interpretability analysis, together with two-dimensional partial dependence plots (2D PDPs), are introduced to systematically explore the synergistic gains, marginal effects, and resource allocation thresholds of key variables. The results indicate that variables such as average housing price, distance to the nearest police station, and average population density contribute significantly to hazard prediction, and that certain combinations of variables exhibit strong synergistic effects in reducing hazards within medium-range intervals. The study concludes that integrating machine learning with interpretability analysis can not only effectively identify the spatial features associated with high levels of safety hazards, but also provide quantifiable and actionable optimization pathways for urban planning and safety hazard governance. This research further underscores the role of managing urban safety hazards as a key pillar in the sustainable development of cities by linking safety hazard modeling with spatial governance strategies that promote inclusive, resilient, and livable urban environments.
