The effectiveness of machine learning (ML) models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity. Current methods for evaluating architectural floor plan datasets typically depend on explicit semantic annotations, which limit their effectiveness and scalability when annotations are unavailable or inconsistent. To address this limitation, this research develops an isovist-based latent representation approach to quantitatively measure typicality and diversity within architectural datasets without relying on semantic labels. We introduce Isovist Latent Norm Typicality, a metric that leverages the statistical structure of latent representations derived from isovist morphological features using a variational autoencoder (VAE). This metric quantifies typicality by analyzing distributional shifts in latent representations between individual floor plans and a reference dataset using a modified Wasserstein distance. Experimental results demonstrate the approach's ability to distinguish typical from atypical floor plan configurations, capturing the morphological features that complement conventional metrics. Comparative analysis indicates that our method provides insights into spatial organization, correlating with conventional properties such as programmatic diversity and spatial openness. By quantifying typicality through purely morphological features, the proposed methodology facilitates dataset curation prior to costly semantic annotation, enhancing dataset quality and enabling scalability to more extensive and diverse architectural datasets.