Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines

• URL: http://arxiv.org/abs/2603.21639v1
• Date: Mon, 23 Mar 2026 07:14:34 GMT
• Title: Engineering Distributed Governance for Regional Prosperity: A Socio-Technical Framework for Mitigating Under-Vibrancy via Human Data Engines
• Abstract summary: This paper introduces the Distributed Human Data Engine (DHDE), a socio-technical framework previously validated in biological crisis management.<n>Using high-granularity data from Japan's least-visited prefecture (Fukui), we utilize an AI-driven decision support system (DSS) to analyze two datasets.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Neutralizing Structural Inequality in the Nigerian FinTech Sector [0.0]
  This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector.<n>Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering.
  arXiv  Detail & Related papers  (2026-07-11T13:46:06Z)
• A Framework for Modeling Liquefaction-Induced Road Disruptions After Earthquakes: Implications for Emergency Response and Access in the Cascadia Region of North America [0.0]
  Large earthquakes along the Cascadia Subduction Zone (CSZ) are expected to trigger widespread soil liquefaction.<n>This study introduces a mechanics-informed, data-driven framework for estimating liquefaction-induced road closures and service reductions.<n>Results show that impacts are concentrated in low-lying coastal zones, river valleys, and urban waterfronts, with major disruptions expected along critical routes.
  arXiv  Detail & Related papers  (2026-03-16T15:54:13Z)
• Toward Equitable Recovery: A Fairness-Aware AI Framework for Prioritizing Post-Flood Aid in Bangladesh [0.0]
  Post-disaster aid allocation in developing nations often suffers from systematic biases that disadvantage vulnerable regions.<n>This paper presents a fairness-aware artificial intelligence framework for prioritizing post-flood aid distribution in Bangladesh.
  arXiv  Detail & Related papers  (2025-12-22T03:45:15Z)
• Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence [64.36291202666212]
  Urban General Intelligence (UGI) refers to AI systems that can understand and reason about complex urban environments.<n>Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs.<n>We propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI.
  arXiv  Detail & Related papers  (2025-10-18T15:59:09Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response [50.76124284445902]
  Building damage assessment (BDA) is an essential capability in the aftermath of a disaster to reduce human casualties.<n>Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events.<n>We present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
  arXiv  Detail & Related papers  (2025-01-10T14:57:18Z)
• From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas [22.782323200872366]
  We propose a systematic road extraction framework combining an encoder-decoder architecture and morphological operations on satellite imagery. Experiments of road network extraction on real-world data in impoverished regions achieve a 42.7% enhancement in the F1-score. We also propose a comprehensive road network dataset covering approximately 794,178 km2 area and 17.048 million people in 382 impoverished counties in China.
  arXiv  Detail & Related papers  (2024-06-17T07:40:13Z)
• Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs [46.52719756897067]
  We present GeoHG, the first space-aware socioeconomic indicator inference method that utilizes a heterogeneous graph-based structure to represent geospace for non-continuous inference.
  arXiv  Detail & Related papers  (2024-05-23T03:19:02Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.