Smart Campus project – part 3 (COVID-19) – in progress

I have been playing with the project data to study the impact of COVID-19 social distancing / lockdown to the university, especially the use of campus facilities. Meanwhile there are some time series analysis and behavioural modelling that I’d like to complete sooner than later. Everything has taken me so much longer than what I planned. Here are some previews followed by moaning, i.e., the painful processes to generate these.

Number of connected devices on campus

The above shows some regular patterns of crowd density and how the numbers dropped due to COVID-19 lockdown. Students started to reduce their time on campus in the week prior to the official campus closure.

Autocorrelation

The autocorrelation grape shows a possible daily pattern (data resampled in 5 minute interval so 288 samples is a day, hence the location of the first peak).

Seasonal decomposition

Seasonal decomposition based on the hypothesis of a weekly pattern. There is also a strong hourly pattern, which I’ll explain in the paper (not written yet!).

A comparison of area crowd density dynamics of one floor of an academic building. from left to right: Pre-lockdown baseline, Near-lockdown reduced social contact, and working/studying from home during lockdown).

These ones above show the area crowd density dynamics of one floor of an academic building. The one on the left shows how an academic workspace, a few classrooms and study areas were used during a normal week when few people in the UK felt the COVID-19 is relevant to them. The middle one shows the week when there were increasing reports of COVID-19 cases in the UK and the government was changing its tones and advising social distancing. Staff and students reduced their hours on campus. The one on the right shows a week during university closure (building still accessible for exceptional purposes).


Bullet points of work carried out (moaning):

  • The main tables on the live DB have 500+ million records (which takes about 300 GB space). It took a week to replicate it on a second DB so I can decouple the messy analysis queries from the main.
  • A few python scripts to correlate loose data in the DB which got me a 150+ GB CSV file for time series analysis. From there, the lovely Pandas happily chews the CSV like its bamboo shoots.
  • The crowd density floor map was done for live data (10 minute coverage). To reprogramme it for historical data and generate the animation above, a few things have to be done:
    • A python script ploughed through the main DB table (yes the one with 500 million records) and derive area density in a 10-minute interval. The script also did a few other things at the same time so the whole thing took a few hours.
    • A new PHP page loaded the data in, then some Javascripts looped through the data and display the charts dynamically. It’s mainly setIntervals() to call Highcharts’ setData, removeAnnotation and addAnnotation.
    • To save the results as videos / GIFs, I tested screen capturing, which turned out to be OK but recording the screen just didn’t feel right to me. So I went down the route of image exporting and merging. Highcharts’ offline exporting exportChartLocal() works pretty well within the setIntervals() loop until a memory issue appeared. Throwing in a few more lines of Javascript to stagger the exporting “fixed” the issue. FFMPEG was brought in to covert image sequence to video.
    • Future work will get all these tidied up and automated.

[to be continued]

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