The chosen challenge (9) consists of recognizing patterns that may assist in predicting the hotspots of the disease. In this way, attempting to solve this challenge, the project acts correlating existing data - like the number of cases, deaths, social determinants of health and urbanistic data. It's important to note that know the curve_ also tries to facilitate expanding the variables, creating various categories of information. This information would be more accessible than the traditional data since they were converted into heatmaps and graphics, showing the places in which the spreading of COVID-19 is getting more significant
The group chose challenge 9 because of the application possibilities that this outcome gives. The approach formulating the project was: specifying the location of the initial plea of data, assigning solid information that had multiple factors involved, and, finally, converting those graphics in highly didactic maps. In relation to NASA plataforma, we used the urbanistic data and selected the area extension (In kilometers) of the region's urban system. Tools, languages of coding, and hardware: to the backend were python, PostgreSQL, and Django; to the frontend JavaScript and React and, in general, we used Digital ocean. Our team had problems related to the term, attempting to find a characteristic that would make the project distinctive and finding public API that did not have throttling, but it's important to notice that those problems were unraveled with adequately delimiting the chores, a better theoretical basis acquired in the webinars and by using other software (????). Thus, it's important to notice that we accomplished data that will encourage a better diffusion of awareness, that is a crucial standard to battle COVID 19
API’s IBGE public; https://sedac.ciesin.columbia.edu/mapping/popest/covid-19/