Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China.

Recently, global climate change has led to a high incidence of extreme weather and natural disasters. How to reduce its impact has become an important topic. However, the studies that both consider the disaster's real-time geographic information and environmental factors in severe rainstorms are still not enough. Volunteered geographic information (VGI) data that was generated during disasters offered possibilities for improving the emergency management abilities of decision-makers and the disaster self-rescue abilities of citizens. Through the case study of the extreme rainstorm disaster in Zhengzhou, China, in July 2021, this paper used machine learning to study VGI issued by residents. The vulnerable people and their demands were identified based on the SOS messages. The importance of various indicators was analyzed by combining open data from socio-economic and built-up environment elements. Potential safe areas with shelter resources in five administrative districts in the disaster-prone central area of Zhengzhou were identified based on these data. This study found that VGI can be a reliable data source for future disaster research. The characteristics of rainstorm hazards were concluded from the perspective of affected people and environmental indicators. The policy recommendations for disaster prevention in the context of public participation were also proposed.

Squatting behavior during the COVID-19 pandemic: The case of the informal settlement "Los Hornos" in Buenos Aires.

The COVID-19 outbreak magnified territorial inequalities and increased vulnerability among low-income groups. Inhabitants in informal settlements are structurally disadvantaged in coping with communicative diseases such as the COVID-19 pandemic. Despite that, the pandemic has been accompanied by the proliferation of informal settlements. This study explores how the pandemic caused the squatting on new land with the case of "Los Hornos" in suburban Buenos Aires. We used a random forest algorithm and Google Earth Engine to estimate the rapid growth of a new informal settlement from a series of satellite images from early 2020. We also conducted semi-structured interviews with inhabitants to investigate the link between squatting and COVID-19. The study revealed that squatting on new land during the pandemic was mainly due to economic difficulties, overcrowding in existing informal settlements in the metropolitan center, and speculation in the informal housing market. This case is an example of how the most vulnerable groups bore the brunt of the pandemic, how the households in the existing informal settlement were behaving similar to those in the formal housing market (i.e., away from the urban centers), and how the outbreak had also been an opportunity for collective action of squatting a new land to materialize.