Environmental Design

Security is one of the most critical factors affecting the quality of urban spaces. Nowadays, most of these spaces have become merely pathways with neither social life nor sense of belonging to it. Insufficiency of public surveillance along with weak sense of control and surveillance results in spaces with high crime rate. In the late 60s and early 70s, high crime statistics in open urban spaces around America and Europe, forced many city planners to provide physical and cultural solutions for it. Sensitive environmental design can simultaneously prevent the occurrence of crime and increase the control and surveillance over the public spaces. The main purpose of this paper is to achieve the most critical factors enhancing safe urban spaces. The research method is descriptive analysis and is done by comparative study on three outstanding theorists’ point of view toward the subject. Research findings identify that crime prevention is largely achieved through applying territoriality, surveillance and social interaction factors in environmental design.

The psychological bond between elderly residents and their living environment—termed place attachment—plays a critical role in aging-in-place strategies. This study investigates the impact of environmental design characteristics on place attachment and evaluates the predictive capabilities of machine learning in this context. Methods: A cross-sectional survey was conducted among 490 elderly residents in Tehran using a 38-item Likert-scale questionnaire. The study applied three regression-based algorithms—Linear, Polynomial, and Ridge Regression—to model the relationship between 20 environmental design variables and place attachment scores. Results: "Positive Home Experiences" (r = 0.68), "Freedom from Confinement" (r = 0.64), and "Safety Features" (r = 0.53) emerged as the most influential predictors. Ridge Regression achieved the highest prediction accuracy, with an R² value of 0.6792. Conclusion: The findings demonstrate the potential of machine learning to support human-centered design by enabling the early-stage evaluation of housing for the elderly. The proposed predictive framework can inform architecture curricula, computer-aided design (CAD) tools, and age-friendly housing policies.