What are the limitations of the proposed spatial combination prediction method for community-embedded retirement buildings as discussed in the conclusions?

The conclusions identify two main limitations: the dataset was selected and produced based on the researcher's experience due to resource and time constraints, introducing bias and contingency, and despite data enhancement its size and diversity are lacking, affecting model generalization and accuracy. In addition, the method is still a pre-design spatial layout approach that has not been developed into an end-user application for designers or community managers, so further optimization and experimentation are needed for practical use.

The study's conclusion section explicitly states two categories of limitations. First, regarding the dataset, resource and time constraints meant that dataset selection and production depended on the researcher's experience, which introduced certain biases and contingencies. Even though data enhancement methods were used, the dataset's number and diversity remained insufficient, and this shortfall negatively affected model generalization and accuracy. Second, regarding visualization, the proposed method is described as a pre-design spatial layout approach, but it had not yet been developed into an end-user application for designers or community managers. Because of energy and time constraints, practical application still requires further optimization and experimentation. The authors suggest that expanding the dataset and designing software visualization are important for future application, and they call for introducing more fine-grained retirement building data and field research to capture spatial layout patterns more precisely.