1. 地理空间大数据和GeoAI技术在环境暴露评估中的整合；
2. 用于疾病绘图和风险评估的 GeoAI 算法的开发和应用；
3. 使用遥感数据和机器学习来分析影响健康结果的环境因素；
4. 在城市健康研究中使用地理空间大数据的时空建模和预测分析；
5. 地理空间大数据和GeoAI在环境健康差异识别和管理中的应用；
6. 基于城市环境健康多源数据分析的地理空间数据融合和集成；
7. 用于探索城市环境健康风险时空模式的大数据分析和可视化技术；
8. 利用地理空间大数据和 GeoAI 促进城市环境健康的伦理问题和挑战；
The following special issue in Urban Informatics is open for submissions. The submission deadline is June 30, 2024.
Theme: Geospatial big data and GeoAI in urban environmental health
Guest editors:
Dr. Yimeng Song, Yale University, USA; Email: yimeng.song@yale.edu
Dr. Li Yi, Harvard University, USA; Email: li_yi@hsph.harvard.edu
Dr. Chen Chen, University of California San Diego, USA; Email: chc048@ucsd.edu
Aim & Scope:
The recent emerging technology, such as geospatial artificial intelligence or GeoAI, the advances in computing technologies, as well as the proliferation of multi-source geospatial big data (e.g., remotely sensed imagery, social media data, cellular, and IoT data), have created tremendous opportunities for researchers to tackle various types of spatial optimization problems, taking into account new data sources and novel technologies. This has significantly impacted the field of urban environmental health, enabling a deeper understanding of the complex relationships between the urban environment and human health. However, methodological innovation and technology fusion pose challenges. These include data integration, quality, algorithm development, ethics, and validation frameworks. This special session seeks cutting-edge research and innovative solutions in geospatial big data and GeoAI in urban environmental health. We invite contributions from diverse disciplines to advance our understanding of applications, methodologies, and challenges. By exploring the synergies between geospatial big data, AI, and remote sensing, we can foster interdisciplinary collaborations and evidence-based decision-making in urban and environmental health management.
Possible topics:
1. Integration of geospatial big data and AI techniques in environmental exposure assessment.
2. Development and application of GeoAI algorithms for disease mapping and risk assessment.
3. Use of remote sensing data and machine learning for analyzing environmental factors influencing health outcomes.
4. Spatial-temporal modeling and predictive analytics using geospatial big data in urban health studies.
5. Applications of geospatial big data and GeoAI in the identification and management of environmental health disparities.
6. Geospatial data fusion and integration for multi-source data analysis in urban environmental health.
7. Big data analytics and visualization techniques for exploring spatio-temporal patterns of urban environmental health risks.
8. Ethical considerations and challenges in utilizing geospatial big data and GeoAI for urban environmental health.
