Demand planning-oriented research on nighttime urban lighting provides a foundation for formulating strategies to eliminate dark areas and reduce light pollution. In this paper, Binjiang District of Hangzhou was investigated. Four factors, namely land-use type, road grade, parcel volume, and nighttime crowds, were evaluated. Based on the spatiotemporal geographic data and the urban lighting planning of Hangzhou, a calculation method for the supply and demand of urban lighting at night in Hangzhou was constructed. In this process, the current state of lighting brightness in different areas of the district were calculated and compared with the results of the total lighting demand to analyze reasonableness. The research results show that according to the actual lighting demand classification, the first to fifth levels of lighting control zones accounted for 1.84%, 19.69%, 49.61%, 21.74%, and 7.12% of the total statistical land area of the district, respectively. Focus should thus be placed on the second, third, and fourth levels of lighting control zones when covering lighting demand. Importantly, areas with unreasonable supply and demand for lighting construction accounted for 20.8% of the total statistical land area, indicating that the nighttime lighting demand and carbon emissions in the Riverside District should be adjusted and optimized. This paper proposes a research method to compare supply and demand for the planning and construction of nighttime urban lighting, which can improve the science on lighting demand measurement.

Urban disasters pose severe, concentrated risks to dense populations, generating asymmetric and time-critical demands on emergency services and infrastructure. To address these challenges, we develop a behaviorally informed shelter siting framework that integrates a Theory of Planned Behavior (TPB)-based choice model with a Non-dominated Sorting Genetic Algorithm III (NSGA-III) multi-objective spatial optimization to simulate aggregation willingness and determine optimal shelter locations. The model explicitly represents symmetric and asymmetric spatial patterns and jointly optimizes population coverage and travel time under three demand scenarios (general, emergency, vulnerable). Comparative experiments show consistent, measurable gains: coverage increases of 0.19~6.9% and travel-time reductions of 14~18% are obtained, with improvements concentrated in high-need pockets. These results indicate that behaviorally informed, symmetry-aware optimization improves access, equity, and robustness while offering a modular tool for planners.