Zhicheng Deng

Zhicheng Deng, Zhaoya Gong, Jean-Claude Thill et al.

Current studies on activity space are limited by the conceptualization of absolute physical space that fails to consider the heterogeneity of relational spaces reconstructed from spatial interactions of human movements between locations and falls short in incorporating the inherent hierarchical property of human mobility. Consequently, these approaches cannot faithfully reflect how people interact with urban spaces through travels. From the lens of relational space, this study proposes the new Hierarchical Activity Region Model (HARM) to derive the space and hierarchical properties of activity spaces perceived by various urban groups. We demonstrate the enhanced validity of our model on travel behavior in Manhattan, New York City, before, during, and after Hurricane Sandy on the basis of taxi data. Empirical results show that intra-urban travel retains clear hierarchical organization, even under disruption of a major weather event. Yet, travel undergoes a compression effect in travel hierarchies, characterized by fewer hierarchical levels and enlarged characteristic scales, followed by a rebound. Clustering the derived hierarchies reveals pronounced heterogeneity that stems from differences in population profiles; some groups sustain deeper structures or recover quickly, while others experience a persistent loss of levels. This study provides valuable insights into the functional hierarchies of urban mobility, which could inform more sustainable, resilient and equitable urban planning. The proposed methodological framework is generic for studying human mobility in broader contexts.

Luyao Niu, Zhicheng Deng, Boyang Li et al.

The "15-minute city" envisions neighborhoods where residents can meet daily needs via a short walk or bike ride. Realizing this vision requires not only physical proximity but also efficient and reliable access to information about nearby places, services, and events. Existing location-based systems, however, focus mainly on city-level tasks and neglect the spatial, temporal, and cognitive factors that shape localized decision-making. We conceptualize this gap as the Local Life Information Accessibility (LLIA) problem and introduce AskNearby, an AI-driven community application that unifies retrieval and recommendation within the 15-minute life circle. AskNearby integrates (i) a three-layer Retrieval-Augmented Generation (RAG) pipeline that synergizes graph-based, semantic-vector, and geographic retrieval with (ii) a cognitive-map model that encodes each user's neighborhood familiarity and preferences. Experiments on real-world community datasets demonstrate that AskNearby significantly outperforms LLM-based and map-based baselines in retrieval accuracy and recommendation quality, achieving robust performance in spatiotemporal grounding and cognitive-aware ranking. Real-world deployments further validate its effectiveness. By addressing the LLIA challenge, AskNearby empowers residents to more effectively discover local resources, plan daily activities, and engage in community life.