The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

The impact of natural disasters on rural areas has recently increased, the question of how to effectively improve the livelihood resilience of rural residents has therefore become an essential issue. In this context, a livelihood resilience evaluation index system for rural residents was constructed in four dimensions: livelihood quality, livelihood promotion, livelihood provision, and disaster stress. A structural dynamics model was used to analyze the changing characteristics of the livelihood resilience of rural residents in China between the years 1980 and 2020, and a livelihood resilience trend from 2021 to 2030 was predicted based on the ARIMA model. The main factors influencing livelihood resilience were explored using ridge regression analysis. The results show that: (1) livelihood resilience of rural residents in China fluctuated significantly between 1980 and 2020, tending generally to increase; (2) livelihood resilience is positively correlated with livelihood quality, livelihood promotion, and livelihood provision, while it is negatively correlated with disaster stress; (3) livelihood quality, livelihood promotion, and livelihood supply will still increase between the years 2021 and 2030, while the increase of livelihood resilience will tend to slow down; and (4) six variables have a significant positive impact on livelihood resilience, and provide a basis for the subsequent enhancement of livelihood resilience.

The Yangtze River Delta region is the most economically active and open region in China. It is also the region with the most prominent contradictions between urban and rural development. Therefore, it is necessary to strengthen the resilience of the rural areas in this region so that they can develop further with resources and opportunities after sustaining shocks. This study used a weighted TOPSIS method to measure the rural resilience of 153 research units from 2000 to 2019 and then applied the ESDA method to measure the spatial agglomeration or heterogeneity characteristics. The results show that: (1) The rural resilience of this region is higher in the east and lower in the west; (2) rural resilience has obvious spatial agglomeration characteristics, which have undergone a process of shifting from strong to weak before becoming strong again; and (3) the hotspots of rural resilience gradually shifted from Jiangsu to Zhejiang, while the coldspots gradually shifted from Zhejiang to Anhui. Finally, the K-means clustering algorithm was applied to divide all research units into five types: natural capital-dominated areas, productive capital-dominated areas, human capital-dominated areas, social-financial capital-dominated areas and general development areas. Then, the strategies for resilience promotion were proposed accordingly.

Previous studies have identified that environmental awareness correlates with the choice of bicycle travel. However, few studies have considered the relationships with different types of healthy behaviors and environmental behaviors. This study examined the relationships between several healthy and environmental behaviors and the choice of bicycle commute using survey data. A total of 803 residents participated in this questionnaire survey. Using factor analysis, we constructed latent factors of healthy behaviors and environmental behaviors. Using a binary logistic regression model, we examined the relationship between latent factors and cycling usage, controlling for demographic characteristics. Factor analysis revealed three latent factors of healthy behaviors: “healthy diet”, “avoiding tobacco or overdrinking”, and “physical activity”. The latent factors of environmental behaviors were as follows: “household behavior” and “purchasing behavior”. The results showed that “avoiding tobacco or overdrinking”, “physical activity” and “purchasing behavior” correlated positively with bicycle commuting. Differences were also observed in relation to demographic characteristics.

Improving urban walkability is critical to the long-term development of cities. Although previous studies have demonstrated a relationship between the built environment and walking, an approach that can control the exploration of different functional areas has not yet been discussed. In this study, built environment features include density, design, diversity, destination accessibility, and distance to transit. Geodetector and regression methods were used to investigate the impact of the built environmental features on pedestrian volume in different functional areas of Kumamoto City. It was found there were various dominant features for the different functional areas in the city, including the city center (diversity, design, and density), local hubs (destination accessibility, density, and distance to transit), living hubs (density, design, and distance to transit), UPA (diversity, design, and distance to transit), UCA (density, density, and design), and NPA (density). Additionally, population density and land use diversity in the overly dense population area were negatively related to pedestrian volume. This study complements research on pedestrians and the built environment in different functional areas, and provides advice for the urban planners and government of Kumamoto City.

Compact cities are widely used in urban planning in Japan due to the following benefits: efficient land use, reduction in the transport network and reliance on mass transport, low emissions, etc. However, Compactness often means high density. In disaster-resistant Japan, whether the compact city form can effectively respond to disasters is needed further discussion. In the Kumamoto City Master Plan, 15 local hubs have been planned to promote the development of the compact city. In this study, 15 local hubs are selected as the research objects. Moreover, the entropy method was chosen to evaluate the disaster prevention capability. The results show that disaster risk is high and the disaster prevention ability is weak in the central urban area, which is likely to cause greater losses when the disaster occurs. The local hubs that are far away from the city centre also have the weak disaster prevention due to the lack of disaster prevention facilities, while some hub areas are more capable of disaster prevention despite the high risk of disasters. Therefore, in the post-disaster reconstruction plan, it is recommended making a focus on the low-risk and disaster resistant areas. At the same time, the cancellation of hubs with high risk and weak disaster prevention needs to be further discussed.