Differences in the Disaster-Preparedness Behaviors of the General Public and Professionals: Evidence from Sichuan Province, China

Analysis on the spatial differentiation characteristics of poverty risk caused by disaster under the stress of geological disasters: a case study of Sichuan Province

Research on the poverty risk caused by geological disasters in disaster-prone areas is a useful exploration to coordinate social economic development with disaster prevention and reduction, and is of great significance to the regional sustainable development. Based on statistical data and spatial data, this paper takes Sichuan Province as the typical research area. Remote sensing and geographic information technology are used to study the poverty risk caused by disasters based on the quantitative evaluation of geological disasters risk and regional development level. The spatial differentiation characteristics of poverty risk caused by disasters are explored on the 1 km × 1 km grid scale. The results indicate that (1) the overall risk of geological disasters in Sichuan Province is relatively high, with high and relatively high risk areas accounting for more than 40% and low and relatively low risk areas accounting for less than 30%. The risks in Mountain and Ravine Areas are significantly higher than other areas. (2) The regional development level in Sichuan Province is relatively high, but with significant spatial differences. The development level of high-altitude areas and remote mountainous areas is quite different from that of the Chengdu Plain in the middle Sichuan Province. The uneven development in the east, middle, and west is a prominent problem. (3) The poverty risk caused by disasters is high, and the spatial pattern presents a characteristic of "high in the west and low in the east" with high positive spatial correlation. High-High Cluster Areas are mainly distributed in western and southwestern Sichuan. Low-Low Outlier Areas are mainly distributed in Chengdu Plain and Hilly Areas of Sichuan Basin. High-Low Outlier and Low-High Outlier Areas occupy a relatively small percentage with scattered distribution. This paper provides some theoretical support for policy formulation and management of coordinated development of regional socioeconomic and ecological environment.

Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

Natural disasters can occur anytime and anywhere, especially in areas with high disaster risk. The earthquake that followed the tsunami and liquefaction in Palu, Indonesia, at the end of 2018 had caused tremendous damage. In recent years, rehabilitation and reconstruction projects have been implemented to restore the situation and accelerate economic growth. A study is needed to determine whether the rehabilitation and reconstruction that has been carried out for three years have met community satisfaction. The results of further analysis are expected to predict the level of community satisfaction for the implementation of rehabilitation and other reconstruction. The method used in this paper is predictive modeling using a data mining (DM) approach. Data were collected from all rehabilitation and reconstruction activities in Palu, Sigi, and Donggala with the scope of the earthquake, tsunami, and liquefaction disasters. The analysis results show that the Artificial Neural Network (ANN) and the support vector machine (SVM) with a DM approach can develop a community satisfaction prediction model to implement rehabilitation and reconstruction after the earthquake-tsunami and liquefaction disasters.

Disaster Preparedness Among Populations in Shenzhen, China, With and Without Chronic Disease

OBJECTIVE

This survey examined and compared the disaster perception and preparedness of 2421 residents with and without chronic disease in Shenzhen, China.

METHODS

The participants were recruited and were asked to complete a survey in 2018.

RESULTS

Three types of disasters considered most likely to happen in Shenzhen were: typhoons (73.5% vs 74.9%), major transport accidents (61.5% vs 64.7%), and major fires (60.8% vs 63.0%). Only 5.9% and 5% of them, respectively, considered infectious diseases pandemics to be likely. There were significant differences between those with and without chronic disease in disaster preparedness, only a small percentage could be considered to have prepared for disaster (20.7% vs 14.5%). Logistic regression analyses showed that those aged 65 or older (odds ratio [OR] = 2.76), who had attained a Master's degree or higher (OR = 2.0), and with chronic disease (OR = 1.38) were more prepared for disasters.

CONCLUSIONS

Although participants with chronic disease were better prepared than those without, overall, Shenzhen residents were inadequately prepared for disasters and in need of public education.

A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.