Selected article for: "data visualization and time series"
Author: Rahutomo, Reza Bens Pardamean
Title: Data Engineering Pipeline to Analyse Jakarta’s Air Quality during COVID-19-Caused Lockdown Periods
  • Cord-id: ll76h63n
  • Document date: 2021_1_1
    • ID: ll76h63n
    Snippet: Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta’s air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from openaq.com, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped b
    Document: Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta’s air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from openaq.com, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

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