Most research in urban informatics and tourism focuses on mitigating overtourism in dense global cities. However, for regions experiencing demographic decline and structural stagnation, the primary risk is "under-vibrancy", a condition where low visitor density suppresses economic activity and diminishes satisfaction. This paper introduces the Distributed Human Data Engine (DHDE), a socio-technical framework previously validated in biological crisis management, and adapts it for regional economic flow optimization. Using high-granularity data from Japan's least-visited prefecture (Fukui), we utilize an AI-driven decision support system (DSS) to analyze two datasets: a raw Fukui spending database (90,350 records) and a regional standardized sentiment database (97,719 responses). The system achieves in-sample explanatory power of 81% (R^2 = 0.810) and out-of-sample predictive performance of 68% (R^2 = 0.683). We quantify an annual opportunity gap of 865,917 unrealized visits, equivalent to approximately 11.96 billion yen (USD 76.2 million) in lost revenue. We propose a dual-nudge governance architecture leveraging the DHDE to redistribute cross-prefectural flows and reduce economic leakage.

翻译：城市信息学与旅游领域的研究大多聚焦于缓解密集全球城市的过度旅游问题。然而，对于面临人口下降和结构性停滞的区域而言，主要风险是"低活力"——一种因访客密度低而抑制经济活动并降低满意度的状况。本文引入此前在生物危机管理中得到验证的社会技术框架——分布式人类数据引擎（DHDE），并将其适配于区域经济流优化。利用日本最偏远县份（福井县）的高粒度数据，我们通过人工智能驱动的决策支持系统（DSS）分析两个数据集：原始福井消费数据库（90,350条记录）和区域标准化情感数据库（97,719份反馈）。该系统实现样本内解释力81%（R²=0.810）及样本外预测性能68%（R²=0.683）。我们量化出每年865,917次未实现访问的机会缺口，相当于约119.6亿日元（7620万美元）的潜在收入损失。我们提出一种双助推治理架构，利用DHDE重新分配跨县流量并减少经济外溢。