As one of China’s key poverty-reduction initiatives, poverty alleviation relocation (PAR) unavoidably results in the reshaping of neighboring social networks. This study equally focused on the changes in the scope of social interaction and in the intergroup social support of the two primary stakeholders of PAR in a rural–rural relocation context: the migrant and local groups. In 2019 and 2021, two surveys were conducted in four different types of resettlements: centralized, adjacent, enclave, and infill. To provide decision makers with broad references for sustainable PAR planning, the social changes were compared by groups, types, and years. In general, the migrant group had more significant scope expansion or narrowing in social interaction than the local group, and they were more willing to seek intergroup social support. Specifically, the centralized type was the superior choice since it was well-expanded and group-balanced; the adjacent type was also a good choice in the long term because of its rapid improvement in the later phase; the enclave type should be a last resort because of its persistently negative impact; and the infill type was a good option in the short term, as it rarely improved in the later stage. Furthermore, the personal socioeconomic attributes associated with the above social changes, claims laid to the spaces, and economic benefits and limitations were explored for a more comprehensive understanding.

In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 °C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings.

Considering the inherent laws and logical relationships between disasters and poverty, the combination of poverty alleviation relocation (PAR) and earthquake reconstruction relocation (ERR) is valuable both theoretically and practically. This study compared PAR and ERR projects on a systematic basis in terms of implementation area, mode adoption, construction content, public engagement, and challenges. Moreover, four common types of villages (e.g., enclave, adjacent, centralized, and infill) and the two resident groups of the re-settler and local groups were recognized. Regarding the social restructuring after relocation, the performance of the double-directional degree of social isolation (SO) and spatial isolation (SP) between the two resident groups were evaluated and compared in types, which helps planners predict and clarify the social impact of the chosen type. The underlying social and spatial reasons affecting the social integration were explained to propose the corresponding planning measures for social integration promotion.