Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies

Multiscale Spatial Distribution Pattern and Influencing Factors on Inland Fishing Gardens in China

Recently, a significant number of freshwater fishing gardens have sprouted up across mainland China. These recreational facilities are an important component in promoting the upgrading of the fishing industry and rural revitalization, and they are a key component in the high-quality development of rural tourism. This paper uses fishing gardens points of interest (POI) in China as data sources and employs kernel density estimation and geographical detectors to systematically uncover the multiscale spatial distribution pattern of these gardens, as well as the factors influencing their distribution. The results show that: (1) There are 15,090 fishing gardens in inland China. The spatial distribution of Chinese fishing gardens corresponds well with the “Hu-Line”, with a greater number of gardens clustered in the southeast and few in the northwest. The density distribution exhibits a polarized pattern with multiple high-density centers. (2) The number of fishing gardens varies significantly across regions, with the eastern > central > western > northeastern; Guangdong has the most fishing gardens. The top five provinces have 43.4% of the total number of fishing gardens in the country. Large-scale fishing gardens are common in developed cities such as the Pearl River Delta, Beijing-Tianjin-Hebei, and the Yangtze River economic belt. (3) In natural environmental factors, land altitude and contour are negatively correlated with the distribution of fishing gardens, whereas winter temperature is positively correlated with the distribution. More than 50% of fishing gardens are located within 6 km of urban built-up areas. (4) GDP, population, and tourism revenue are the most important social development factors influencing the distribution of fishing gardens. The moderate factors are per capita income and the rate of urbanization; the weak factors are fishery output value and freshwater products production. In the discussion, suggestions on how to guide the rational layout and healthy development of the fishing garden industry in the region are put forward. We believe that these suggestions could be part of the pursuit to improve the fishing garden industrial policy in China.

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.

Spatial Patterns of the Spread of COVID-19 in Singapore and the Influencing Factors

Exploring the spatial patterns of COVID-19 transmission and its key determinants could provide a deeper understanding of the evolution of the COVID-19 pandemic. The goal of this study is to investigate the spatial patterns of COVID-19 transmission in different periods in Singapore, as well as their relationship with demographic and built-environment factors. Based on reported cases from 23 January to 30 September 2020, we divided the research time into six phases and used spatial autocorrelation analysis, the ordinary least squares (OLS) model, the multiscale geographically weighted regression (MGWR) model, and dominance analysis to explore the spatial patterns and influencing factors in each phase. The results showed that the spatial patterns of COVID-19 cases differed across time, and imported cases presented a random pattern, whereas local cases presented a clustered pattern. Among the selected variables, the supermarket density, elderly population density, hotel density, business land proportion, and park density may be particular fitting indicators explaining the different phases of pandemic development in Singapore. Furthermore, the associations between determinants and COVID-19 transmission changed dynamically over time. This study provides policymakers with valuable information for developing targeted interventions for certain areas and periods.