Spatial Analysis: COVID-19 Clusters and Factors In New York City

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Introduction
The threat from the emergence of epidemics, disasters, and public health emergencies poses a global health problem for the human race. When a pandemic spreads across many countries usually affects many people and causes several deaths. The impact of a pandemic varies from one place to another as some regions are likely to experience a more intense effect than others. The variation across different regions and territories is caused by factors such as demographic factors, physical environments, socio-economic factors, cultural factors, and behavioral characteristics of the population. Media coverage of spatial distribution of pandemic cases can be done through interactive maps from the onset of the illness. It's a common practice that even the World Health Organization(WHO) usually communicates the proliferation of pandemics by counting infections via territorial world maps. The pandemic outbreak of the new coronavirus-induced respiratory disease (COVID-19) has been covered since its onset using a published world map that is regularly updated by a team of researchers of the US-based Johns Hopkins University as the diseases continue to spread.
Spatial Analysis Of COVID-19 Clusters and Contextual Factors In New York City
A study was conducted by Cordes & Castro (2020) in New York to analyze COVID-19 clusters and their contextual factors. The first objective was to determine positivity rates, rates of testing, and the percentages of individuals infected for a better understanding of the case burden and access to testing. Another objective was for the evaluation of contextual factors that are related to the clusters across New York City. The two objectives sought to provide advanced knowledge on inequities existing in the city and current exposure of the city to the COVID-19 pandemic. The data would thus, be used during resource allocation to ensure equitable distribution.
There were three outcome variables created; testing rates, positivity rates, and proportion of positive tests. The study adopted the use of cluster analysis and contextual factor analysis. The cluster analysis deployed the use of choropleth maps for the three testing outcomes and covariate created using four quantile categories that enhance an equal number of zip codes in each category. To achieve a manageable size of clusters, the population in the clusters did not exceed 2 percent. The population at each zip code averaged 0.56 percent; therefore, a maximum cluster size created an approximation of one to six zip codes. The relationships between contextual factors and the testing outcomes at zip code levels were analyzed by the use of descriptive statistics and correlations.
The study focused on a place as a container that constitutes various social aspects of life. The description of the place as human territory resulting from economic, political, and social processes is clearly in line with the paper. The covariates that were used in New York included; proportions of whites, blacks, Hispanic, Asians, non-citizens, population using public transport, bachelors or graduates, rental income of above 50 percent, poverty, uninsured individuals, those with medium income, and those who r...