Spatial Optimization Methodology for Lighting Infrastructure Targeting Criminal Behavior Prevention: A Case Study of Closed Non-Gated Residential Areas

With the acceleration of urbanization, the importance of crime prevention in aging residential areas has become increasingly prominent, as improper spatial distribution of streetlights significantly weakens security effectiveness. This study proposes a crime prevention-oriented spatial optimization method for lighting facilities, focusing on enclosed but non-gated residential areas. By quantitatively modeling the lighting matrix, the study innovatively achieves refined spatial planning. First, a crime prevention demand-oriented lighting requirement matrix is constructed based on a road classification model. Second, a lighting resource allocation matrix is generated through spatial simulation of the light environment. Finally, a multi-objective optimization model is established to minimize illumination differences at the grid level. The genetic algorithm results indicate that, compared to traditional layouts, the optimized scheme effectively enhances the coverage of lighting with crime deterrence effects while balancing cost efficiency and crime risk reduction. This paradigm provides a generalizable methodological framework for urban security governance in lighting planning.