Unveiling the hidden vitality of block-structured neighborhoods through a multimodal urban perception and ensemble learning framework

Vibrant communities are essential for sustainable urban development, yet prior research has often overlooked subjective spatial perceptions and lacked spatiotemporal analysis of vitality drivers in non-core areas. To address this, we introduce the concepts of hidden vibrant communities (HVN) and hidden vibrant areas (HVA), expanding a framework for constructing community vitality (NV) by integrating multimodal data. Combining group perceptions with spatiotemporal dynamics, we identify HVNs in non-core areas and classify them into HVAs based on spatial features. We further introduce a stacking ensemble learning framework to improve both NV prediction accuracy and the methodology. We then explore the impact of community environmental features on HVNs and HVAs, identify vitality drivers and thresholds, and reveal differences and commonalities in vitality across regions. The results find that HVN vitality varies significantly over time and space, while core areas remain stable. Point of interest density (POID) is the primary driver and other factors, such as POI mix (ME) and housing price index (HPI), exhibit region-specific effects. Threshold analysis identifies critical points where these factors positively or negatively impact vitality. District-level classification is used to further analyze the commonalities and differences in vitality drivers across HVAs, informing targeted urban planning strategies to enhance livability.