Accurately estimating human mobility in peripheral regional economies presents a fundamental measurement challenge: physical ground-truth sensors are sparse, behavioral intent signals are heterogeneous, and environmental friction introduces systematic bias into demand inference. We introduce the Distributed Human Data Engine (DHDE), a multi-modal sensor fusion architecture that addresses this challenge by integrating physical instrumentation (Edge-AI cameras), digital intent signals (route search impression metrics), behavioral records (90,350 spending records, 97,719 standardized survey responses), and meteorological data across four geographically distributed nodes in Fukui, Japan. The primary measurement-science contribution is the design, deployment, and cross-node validation of the DHDE as a sparse-sensor compensation instrument: a heterogeneous sensor fusion architecture that anchors non-stationary digital intent signals to concurrent physical ground-truth counts, correcting for systematic bias introduced by meteorological planning friction. The instrument is implemented as an ensemble inference pipeline (Random Forest and Ordinary Least Squares with Newey-West robust inference), calibrated across 397 daily observations and validated by chronological holdout replication across four geographically distinct node types. The primary OLS specification achieved an in-sample explanatory power of R2 = 0.810 and a chronological out-of-sample predictive performance of R2 = 0.683. Results identify an Under-Vibrancy Paradox where macro-regional visitor satisfaction correlates positively with crowd density (Spearman rank correlation rs = +0.150, p = 0.002). We estimate an annual proxy gap of 865,917 intent-implied visits, corresponding to JPY 11.96 billion (USD 72.6 million) in foregone revenue.

翻译：精准估算外围区域经济体中的人类移动性面临根本性的测量挑战：物理地面实况传感器稀疏、行为意图信号异质，且环境摩擦因素会向需求推断引入系统性偏差。我们提出分布式人类数据引擎（DHDE）——一种多模态传感器融合架构，通过整合物理仪器（边缘AI摄像头）、数字意图信号（路径搜索曝光指标）、行为记录（90,350条消费记录、97,719份标准化调查问卷）及气象数据，在日本福井县四个地理分布式节点部署以应对此挑战。该设计在测量科学上的核心贡献在于，将DHDE作为稀疏传感器补偿仪器进行设计、部署与跨节点验证：这是一种异构传感器融合架构，通过将非平稳数字意图信号锚定于同期物理地面实况计数，校正由气象规划摩擦引入的系统性偏差。该仪器以集成推断管道（随机森林与带Newey-West稳健推断的普通最小二乘法）实现，基于397天观测数据校准，并通过四个地理异构节点类型的时序留出复制进行验证。主OLS模型在样本内解释力达R²=0.810，时序样本外预测性能达R²=0.683。研究结果揭示了“低活力悖论”——宏观区域游客满意度与人群密度呈正相关（斯皮尔曼秩相关系数rs=+0.150，p=0.002）。我们估算出每年存在865,917次的意图隐含访问缺口，对应约119.6亿日元（7260万美元）的潜在收入损失。