The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.

No abstract

There is a growing need to introduce warning dissemination systems in disaster-prone regions to improve the coverage of information distribution. In this study, a warning dissemination system was designed in which disaster information transmitted by a global navigation satellite system (GNSS) is received by terrestrial infrastructure, such as sirens and public transportation, converted into audio messages, and delivered automatically. The originality of the designed system lies in its appropriate integration of existing satellite systems and terrestrial infrastructure, making the system potentially applicable in many regions. First, we evaluated the effectiveness of the designed system in distributing audio messages using public buses in Brisbane, Australia, where large floods occur frequently. Real-time location information for public buses was acquired in the format of General Transit Feed Specification (GTFS), which is currently used in many countries. Time-series changes in the coverage rate relative to both the flood inundation zone and population were calculated using a geographic information system (GIS). The simulation results showed that the system could reach 60% of the flood inundation zone and 70% of the population on a holiday, indicating that the designed system could be effectively adapted to the target area. The coverage rate was found to peak during 15:00–16:00, with minimum rates observed late at night and early in the morning. These results will allow the development of an effective disaster management plan. In the future, this system will be evaluated in other regions using the same calculation process.

The purpose of this research is to design the architecture of an early warning system with Global Navigation Satellite System (GNSS) and terrestrial infrastructure for improving coverage of disaster information dissemination. In the proposed architecture, necessary segments and information flow are identified to introduce an early warning system to target areas which lack public alert distribution. It can be adapted worldwide by combining GNSS satellite and terrestrial infrastructure. At the beginning of a disaster, information will be sent from the agency via GNSS to widely used terrestrial infrastructure, such as sirens and public vehicles, thus allowing users to receive disaster information even when the ground network has been damaged. The effectiveness of the proposed architecture is examined in terms of redundancy, interoperability, and multi-hazard response by Geographic Information System (GIS) simulation using an open-source format data of public bus in a coastal area of Japan. Results show that the coverage of information dissemination is improved. Thus, the proposed architecture can be adapted to target areas as an early warning system.

Abstract. High-velocity compact clouds (HVCCs) are a population of molecular clouds which have compact appearance (d < 10 pc) and large velocity width (ΔV > 50 km s −1 ), and are found in the central molecular zone of our Galaxy. We performed a 3 mm band line survey toward CO−0.40−0.22, a spatially unresolved HVCC with an extremely large velocity width (ΔV 90 km s −1 ), using the Mopra 22 m telescope. We surveyed the frequency range between 76 GHz and 116 GHz with a 0.27 MHz frequency resolution. We detect at least 54 lines from 32 molecules. Many line profiles show a prominent peak at vLSR ∼ 70 km s −1 with very large velocity width, indicating they are emitted by the HVCC. Detections of largish molecules are indicative of non-equilibrium chemistry. We extracted some prominent lines based on velocity structure, intensity ratios, and PCA analyses. Shock diagnostic lines (SiO, SO, CH 3 OH, HNCO) and dense gas probes (HCN, HCO + ) appear to be prominent. Excitation analysis of CH3 OH lines show an enhancement in Trot in the negative high-velocity end of the profile. These results suggest that CO−0.40−0.22 has experienced a shock, acceleration, compression, and heating in the recent past.