Taiwan is in a hazard-exposed area where people often suffer through typhoons, earthquakes, and landslides, and must face the challenges of environmental and climate changes in ongoing and future developments. Taiwan has implemented an integrated and interdisciplinary project, which is titled Civil IoT Taiwan, for better disaster risk management and risk communication with all stakeholders by cooperating closely with authorities, scientists, and industry. The purposes of this project are to raise public risk awareness to reduce disaster damage and loss and sustainably increase the social, economic, and environmental impacts. For measuring the social impacts of the Civil IoT Taiwan, the social return on investment (SROI) is an evaluation tool to demonstrate the outcomes and impacts of Civil IoT Taiwan to measure its social effects. The SROI ratio of this project is 1.12. Civil IoT Taiwan has just implemented the first development stage in establishing infrastructure for monitoring and sensing; thus, the significant changes and impacts on society, economics, and the environment will be evaluated in the next phase. This ongoing project will also involve more stakeholders for more sustainable and resilient environmental governance in future development.

The accurate and rapid transfer of complex urban spatio-temporal data is crucial for urban computing tasks such as urban planning and public transportation deployment for smart-city applications. Existing works consider auxiliary data or propose end-to-end models to process complex spatio-temporal information into more complex deep features. However, the latter is incapable of decoupling spatio-temporal knowledge, which means these end-to-end models lack modularity and substitutability. A general modular framework that can automatically capture simple representations of complex spatio-temporal information is required. In this paper, we thus propose a universal framework for the transfer of spatio-temporal knowledge based on a radial basis function (RBF). We termed this approach STRBF-TF. The proposed STRBF-TF generates simple RBF representations of spatio-temporal flow distribution with an RBF transfer block and also leverages a channel attention mechanism. Moreover, we propose two RBF kernel initializers suitable for the source and the target domains, respectively. The framework retains important spatio-temporal knowledge in simple representations for the reconfiguration of spatio-temporal feature distribution for fast and accurate transfer. We conducted cross-domain learning experiments on a large real-world telecom dataset. The results demonstrate the efficiency and accuracy of the proposed approach, as well as its suitability for real-world applications.

Since the emergence of big data analytics, forecasting future population distributions in specific regions has become a popular prominent research topic. Accurate predictions offer benefits for urban planners, traffic management authorities, and commercial stakeholders. However, most studies have concentrated on population dynamics within isolated locations, often overlooking broader spatial fluctuations across larger geographic areas. This narrow scope limits the practical utility of such predictions. Therefore, generative adversarial networks (GANs) have been employed to estimate population counts across multiple locations within expansive regions. Despite their potential, many GAN-based models encounter significant challenges when tasked with predicting numerous locations simultaneously, resulting in prolonged training times or failure to achieve convergence. To address these limitations, we developed a novel random number generation method to improve the training efficiency and convergence stability of GANs. We also set a new identification criterion to ensure that the large-scale population distributions generated by GAN closely reflect real-world conditions. The developed model in this study was validated using actual telecommunications-based pedestrian flow data from Taiwan, demonstrating its effectiveness and practical feasibility.

The selection of key grid cells is critical for enhancing the interpretability and reliability of urban spatial-temporal flow prediction. Manual selection by domain experts can be inconsistent and time-consuming, while recent deep learning approaches that rely on feature visualization often produce unstable results due to sensitivity to input changes. In this work, we propose CRBF-Net, a convolutional radial basis function network that decouples feature representations from prediction outputs by introducing spatially localized basis kernels to prioritize selection stability. Our proposed method is evaluated on real-world telecom mobility data from Taipei, Taiwan. Experimental results demonstrate that CRBF-Net ensures high selection consistency and enhances the transferability of spatial insights across various downstream prediction models, including LSTM, ANN, and KNN. MAPE reductions were observed across all locations, with improvements of up to 8.24% compared to prior visualization-based selection strategies. Additionally, stability analysis via the Jaccard Index confirms that CRBF-Net maintains significantly higher spatial consistency across multiple independent trials. These results highlight the robustness and practical utility of CRBF-Net in selecting meaningful spatial regions, providing a stable and generalizable approach to key grid cells selection in urban flow prediction while contributing to the development of trustworthy and interpretable GeoAI systems.