Local Life Information Accessibility

• Local Life Information Accessibility (LLIA) is a framework that evaluates how individuals and intelligent agents access and utilize localized information across physical, digital, and cognitive dimensions.
• It integrates measurable metrics such as travel-time costs, digital recommendation accuracy, and personalized contextual relevance to guide urban planning and policy evaluation.
• LLIA employs advanced multi-hop reasoning, geo-indexed data pipelines, and comprehensive benchmarks to improve local information retrieval and enable intelligent urban services.

Local Life Information Accessibility (LLIA) denotes the degree to which individuals, intelligent agents, or service infrastructures can accurately, timely, and contextually acquire, reason over, and act upon information relevant to their immediate geographic, social, and temporal environment. LLIA transcends traditional notions of physical proximity, capturing not only the ease of access to amenities but also the effectiveness of digital tools and information infrastructures in informing, recommending, and planning local activities. This multifaceted concept encompasses computational accessibility metrics, multi-hop intelligent agents, cognitive- and context-aware retrieval systems, and spatially adaptive models for urban environments.

1. Formal Definitions and Conceptual Domains

LLIA formalizes the ability of agents or users to retrieve and utilize localized information along three principal axes:

• Physical Accessibility: Quantified as the spatial and temporal effort (distance, travel time, barriers) needed to reach essential amenities or services. Formulations include travel-time metrics, isochrone-based coverage, and minimum-time thresholds for origin–destination pairs (Lang et al., 2020).
• Information Accessibility: The capability of digital systems (recommendation engines, agentic search) to provide complete, correct, and faithful answers about locally relevant entities, grounded in real-world constraints (business hours, events, locations) (He et al., 8 Dec 2025).
• Cognitive and Contextual Accessibility: Models that account for user cognitive maps—spatial familiarity, temporal context, and personal preferences—so that recommendations and retrievals reflect not only geographic closeness but also individualized relevance (Niu et al., 2 Dec 2025).

For a user $u = (s_u, t_u, p_u)$ (location, temporal context, cognitive profile) and a spatiotemporal knowledge base $K$, LLIA can be conceptualized via an operator:

$$A(u) = \operatorname{Retrieve}(Q, s_u, K) \cup \operatorname{Recommend}(s_u, t_u, p_u, K),$$

where retrieval addresses explicit queries with localization, and recommendation leverages passive or model-driven profiling (Niu et al., 2 Dec 2025).

2. Mathematical and Algorithmic Frameworks

2.1 Accessibility Metrics and Urban Analytics

Core metrics include:

• Travel-Time Cost: $t_{ij}^m$ for origin $i$, destination $j$, and transport mode $m$.
• Accessibility Scores: $A_{ij}^m = \exp(-\beta t_{ij}^m)$ or $1/(t_{ij}^m + \varepsilon)$.
• Aggregate Indicators:
  • Minimum Travel Time (MTT): $\mathrm{MTT}_i^T(m) = \min_{j \in D^T} \tau_{ij}^m$.
  • Isochrone Coverage: $$C^T_m(\theta) = \frac{1}{|O|} \sum_{i \in O} \mathbf{1}\{\mathrm{MTT}_i^T(m) \leq \theta\}$$ (Lang et al., 2020).
• Statistical and Spatial Models: Negative Binomial Geographically Weighted Regression captures the impact of local amenity distributions on observed flows, with accessibility features (e.g., $A_{i,c}$: number of reachable POIs of category $c$ within 15 minutes’ walk) (Graells-Garrido et al., 2021).

2.2 Agentic Search and Multi-Hop Reasoning

Advanced LLIA requires agents to handle multi-hop reasoning over constrained, heterogeneous local services:

• Benchmarks: LocalSearchBench provides 300 real-world, multi-hop QA tasks over 150,031 merchant entries across three cities, with evaluative focus on correctness, completeness, and faithfulness (He et al., 8 Dec 2025).
• Workflow Orchestration: Agents sequentially invoke location-grounded retrieval (LocalRAG), general web search (for event/weather context), and dynamic toolchains to satisfy multi-constraint queries. Failures typically result from ambiguity, incomplete multi-hop planning, or hallucination from dynamic data sources (He et al., 8 Dec 2025, Lan et al., 3 Jun 2025).
• Evaluation Metrics:
  • $\mathrm{Correctness} = \#$ fully correct answers$/\#$ total queries
  • $\mathrm{Completeness}$: fraction of required information pieces retrieved.
  • $\mathrm{Faithfulness}$: proportion of answer facts consistent with ground truth (He et al., 8 Dec 2025).

3. System Architectures and Data Pipelines

Structural elements of LLIA systems include:

• Data Integration and Preprocessing: Construction of geo-indexed graphs using open datasets (e.g., OSM, GTFS). Spatial indices (quadtree, PostGIS) accelerate KNN queries and spatial lookups (Lang et al., 2020, Bolten et al., 2016).
• Multi-Attribute Graph Modeling: For routing and accessibility in urban environments, annotated graphs encode lengths, slopes, barriers, and amenity attributes. Edge cost functions parameterize user-specific constraints (e.g., slope, construction, ramp) (Bolten et al., 2016).
• Retrieval-Augmented Generation (RAG) Pipelines:
  • GeoRAG for spatial KNN retrieval.
  • GraphRAG for semantic/hierarchical expansion.
  • VectorRAG for semantic vector similarity (Niu et al., 2 Dec 2025).
• Hierarchical Item Tokenization: LLM-Aligned Geographic Item Tokenization (LGSID) injects spatial distance priors and collaborative signals into embedding and quantization, boosting geographic coverage and semantic relevance for local-life recommendation (Jiang et al., 18 Nov 2025).

4. Empirical Benchmarks and Evaluation

LLIA research incorporates large-scale, multi-modal, and city-scale evaluation environments:

• Urban Case Studies: In Buenos Aires, walk-based median travel times to schools are 9 minutes, with spatial quantiles highlighting equity gaps; similar metrics predict “access deserts” for policy interventions (Lang et al., 2020).
• 15-Minute Cities: In Barcelona, population-normalized POI coverage and entropy metrics directly inform neighborhood-level accessibility, cross-validated with mobile phone OD flows and demographic features (Graells-Garrido et al., 2021).
• Agentic Search Evaluation: Benchmarks such as LocalSearchBench report DeepSeek-V3.1 at 34.34% correctness, 80.00% completeness, and 60.80% faithfulness on complex multi-hop queries, indicating substantial room for advancement (He et al., 8 Dec 2025).
• LLM-Based Recommendation: Fine-tuned compact models (e.g., Qwen2.5-7B) reach parity with 70B-parameter models (≈70% composite accuracy) at an order-of-magnitude lower inference cost (Lan et al., 3 Jun 2025).
• Real-World Deployments: AskNearby achieves 75.6% Precision@4 and NDCG@4=0.96 for geo-personalized retrieval, outperforming leading map-based and LLM baselines, and reaching high spatial-temporal relevance (STR=83.8%) (Niu et al., 2 Dec 2025).

5. Applications, Policy, and Societal Implications

LLIA frameworks and tools are deployed for:

• Urban Policy Evaluation: Quantitative LLIA metrics identify infrastructural gaps (“access deserts”), simulate intervention scenarios (e.g., new transit lines), and support equity analysis through spatial correlation with socioeconomic indices (Lang et al., 2020).
• Barrier-Free Routing: Segmented, attribute-rich sidewalk graphs enable route planning for limited mobility, with direct user control over slope, curb ramp, and construction barriers, closing >88% of routing gaps in urban networks (Bolten et al., 2016).
• Cognitive-Map-Driven Community Apps: Systems integrating cognitive profiling (preference, familiarity) with spatiotemporal retrieval and recommendation supports highly personalized, context-aware local service discovery (Niu et al., 2 Dec 2025).
• Hyperlocal News Personalization: Precision-focused geoparsing pipelines match user and content geohash footprints, yielding sub-10 km median serve distances for local news (Shah et al., 2023).
• Local-Life Recommendation: Geographic and collaborative alignment in LLMs enhances city/town coverage and maintains semantic accuracy in generative recommendation, validated on >50 million-interaction datasets (Jiang et al., 18 Nov 2025).

6. Limitations and Future Challenges

Known constraints include:

• Static Environments: Most LLIA benchmarks and systems rely on offline or snapshot data; dynamic real-time constraints (traffic, events) remain incompletely addressed (He et al., 8 Dec 2025, Niu et al., 2 Dec 2025).
• Multi-Modality and Event Fusion: Current pipelines are text-centric; integration of images, live sensors, and user–agent mixed-initiative workflows is outstanding (He et al., 8 Dec 2025).
• Geographical and Cultural Generalizability: Most empirical evaluations focus on a single country or city; transfer and adaptation to globally diverse environments and languages require further research (Niu et al., 2 Dec 2025).
• Semantic–Spatial Trade-offs: Achieving optimal balance in embedding similarity versus geographic precision remains a challenge, requiring adaptive weighting and domain-specific alignment (Jiang et al., 18 Nov 2025).
• Clarification and Hallucination: Agentic systems face persistent difficulties around query ambiguity and hallucination risk when aggregating multi-source or web data (He et al., 8 Dec 2025).

Opportunities for future development include adaptive cognitive-map learning, real-time multimodal fusion, adversarial evaluation of faithfulness, broader deployment in varied urban/rural contexts, and meta-learning for parameter optimization (Niu et al., 2 Dec 2025, He et al., 8 Dec 2025).

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By integrating spatial analytics, multi-modal retrieval, cognitive modeling, and agentic workflows, LLIA provides both theoretical and practical infrastructure for evaluating and advancing the accessibility of local life information—a prerequisite for equitable, actionable, and context-responsive urban digital services.