We wanted to present environmental and social data in a creative way, so through studies of several articles, we were able to create a platform in near real time, which presents the data in an intuitive and clear way for anyone who uses it.
First, our challenge was to understand how Covid-19 behaves in the environment. Through reading articles, we discovered environmental variables, like temperature, air-quality, humidity, that has a great correlation with the contamination index.
For example, when the average temperature was below 25.8 ° C, each 1 ° C rise was associated with a −4.8951% (t = −2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.
And we collected data on population density, age pyramid. To calculate whether a city was elderly, we use the Aging Index, where we divide the number of people that has age between 0-14)/ people that has age of 65+, and multiplied them with 100
We use the SEDAC platform to collect data about the population and COVID-19 statistics.
Used the POWER plataform, filtering with temperature bellow 2 meters, relative humidity bellow 2m, and wind speed bellow 10 meters. We are using the API to collect data in real time.
And for the air quality, we integrate the API of the global partner meteormatics, collecting data about air pollution
We use Node as our Backend platform, and the No-SQL database MongoDB for saving our data. For the map, we developed using the library GeoJS, that allow us to draw on Map, and manipulate as we want.
To check our prototype, go to this link. (https://team-stell-nasa.herokuapp.com/)
The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil, https://doi.org/10.1016/j.scitotenv.2020.139085.
NASA’s Prediction of Worldwide Energy Resources (POWER) https://power.larc.nasa.gov/data-access-viewer/
Socioeconomic Data and Applications Center (SEDAC), https://sedac.ciesin.columbia.edu/mapping/popest/covid-19/
Association between climate variables and global transmission oF SARS-CoV-2 https://doi.org/10.1016/j.scitotenv.2020.138997
Impact of meteorological factors on COVID-19 transmission: A multi-city study in China
https://doi.org/10.1016/j.scitotenv.2020.138513