The data of COVID-19 and their correlation with wind speed
Cite this dataset
Susanna, Dewi (2022). The data of COVID-19 and their correlation with wind speed [Dataset]. Dryad. https://doi.org/10.5061/dryad.6djh9w14v
In 2020 the world was presently burdened with the COVID-19 pandemic. World Health Organization confirms 34,874,744 cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries. In Indonesia, the number of people who have been infected and the number who have died are approximately 287,008 and 10,740 (CFR 3.7%), respectively, with the most predominant regions being Jakarta (73,700), East Java (43,536) and Central Java (22,440). Many factors can increase the transmission of COVID-19. One of them is wind speed. This data set contains covid-19 data in DKI Jakarta from June 2020 until August 2022 and wind speed in daily power point form. This data can be analyzed to see the correlation between wind speed and the COVID-19 cases.
The records of COVID-19 were obtained from the special website of coronavirus for the Daerah Khusus Ibukota (DKI) Jakarta at the Provincial Health Office (https://corona.jakarta.go.id/en/data-pemantauan). The COVID-19 data (n = 4,740) covered six administrative city areas and 261 sub-districts in DKI Jakarta as research locations, namely Kepulauan Seribu, West Jakarta, Central Jakarta, South Jakarta, East Jakarta, and Nort Jakarta.
The wind speed data was taken from the Meteorology, Climatology and Geophysics Agency's data website. The wind speed data collected for the period June 2020 to August 2022 (n = 790) was obtained from the POWER LaRC Data Access Viewer, Jakarta. The wind speed data in .csv format is downloaded by specifying the type of daily data unit, data period (time extent), and parameter (in this case wind/pressure). The type of data extraction is POWER Single Point, where the location of the centroid or midpoint of DKI Jakarta Province is determined at latitude -6.1805 and longitude 106.8284.
The data of wind speed is in the form of .csv in the form of time series-daily data; it was extracted into a tabular form with two variables, namely wind speed data of 10m and wind speed of 50m (n = 790). The total data (n = 4,740) were grouped into 6 regions with n = 790/region. At the processing steps, the collected data was grouped into variable wind speeds of 10m, wind speeds of 50m, and variables of COVID-19 cases in six areas in DKI Jakarta Province. To find out the distribution of Wind Speed, the daily data before being processed was grouped into per month.
University of Indonesia