## 5 Discussion
### Future Works  
Future research should systematically optimize and expand this methodology across multiple dimensions to enhance its theoretical depth and practical value. In terms of data collection, the results of this study will further address the current research limitations in temporal-spatial dimensions and climatic conditions. By adopting a longitudinal tracking research method, extended observations will be conducted to capture the differential impacts of seasonal variations (spring, summer, autumn, winter), tourism cycles (off-peak/peak seasons), and special time nodes (public holidays, weekends/weekdays) on the usage behavior of elderly-oriented rest facilities. Furthermore, future research could develop a \"time-space-climate\" three-dimensional dynamic analysis model. By quantifying the interaction mechanisms between environmental parameters and elderly outdoor activity behaviors, this model would systematically reveal the spatiotemporal heterogeneity of rest facility utilization rates and their climate response patterns. The establishment of such a model will contribute to the development of a data-driven, aging-adapted outdoor space optimization design method, providing scientific planning and decision-making support to address the outdoor activity needs of the elderly in the context of climate change. In terms of expanding application scenarios, this paper can transcend current limitations by systematically broadening its scope along spatial and facility-type dimensions. Spatially, the model's applicability could be extended to diverse tourist destinations, including mountainous scenic areas, coastal resorts, and historic districts, each presenting unique environmental characteristics. Regarding facility types, the research scope could be broadened to incorporate comprehensive amenities such as information centers, barrier-free pathways, and sanitation facilities, thereby establishing a robust evaluation index system for aging-friendly service adequacy. By developing a multidimensional, hierarchical assessment framework, this approach both facilitates comprehensive evaluations within individual scenic spots and provides differentiated, evidence-based theoretical foundations for aging-friendly renovations across various tourism destinations. At the technical optimization and functional enhancement level, priority should be given to addressing computer vision accuracy challenges in complex environments. Advanced deep learning techniques, such as multi-scale feature fusion, could mitigate interference from object occlusion and extreme lighting conditions (e.g., backlighting, low illumination). Additionally, transfer learning frameworks could be explored to adapt to regional variations in data distribution. The integration of IoT sensor networks and edge computing technologies could facilitate real-time monitoring systems for aging-friendly facilities, enabling the automated collection and analysis of big data on visitor flow and facility utilization frequency. Time-series prediction models (e.g., LSTM, Transformer) combined with spatial statistical analysis could establish dynamic forecasting mechanisms for anticipating facility demand trends. Furthermore, multi-source data fusion methodologies could incorporate GIS, urban transportation data, and community demographic statistics to develop comprehensive evaluation models that account for multiple factors, including local age demographics, public transport accessibility, and pedestrian environment safety. Analytical Hierarchy Process (AHP)- or machine-learning-based feature importance assessment could quantify the relative weights of these influencing factors. For practical applications, an intelligent decision-support system could be developed to generate customized renovation recommendations such as optimized spatial layouts of service facilities, accessible route network planning, and emergency service point allocation.