Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.

Affordable housing is an effective approach for relieving the residential stress of moderate and low-income citizens; it is not only a macro study but also a micro one in the processive phase focusing on site selection and so on. From this view, the site selection of affordable housing in the direct management district under Jiangbei New District in Nanjing is analyzed, including summarizing the status quo and construction models of affordable housing’s site selection; quantifying the influential elements of site selection by the AHP method and GIS spatial analysis. The final result shows that the site of Jiangbei’s new district in Nanjing, with a score ranging from 2.7792 to 3.8572, is recommended for affordable housing. According to this result, the optimization strategy of affordable housing planning and location is put forward to ensure the balance of work and housing and the interests of low-income groups.