NUSHJC Team 1
Pinxi, Sahad, Shashvat
How much of the Data is ACTUALLY usable?
The graph above shows the cumulative frequency for light emittance. From our analysis of the data, 11% of emittance values are below 0, 40% of data is below 10 lux. A range of 0-10 lux would correspond to 'pitch black' in terms of human perception. This is obviously not possible as there is sufficient lighting in the park at all times. Even at night there are lamp posts every 20m.
The recorded date on the sensor data shows obvious errors. Some data sets show the same sensor collecting data on the same day but twice, but it is clear from the data that it was actually done over 2 different days. Another group’s data shows the date to be 2019.
Although the sensor data without the correct date is still usable because we are still able to study changes in trends across the day, it becomes very hard to be sure about the validity of the data if we cannot cross compare the data with other groups based on the date it was collected on. Note that the environmental variables are also going to be dependent on day because on some days, for instance, it may be cloudy causing the temperature to decrease throughout the day but we will not be able to capture such evidence if the date is not accurate.
The graph above shows the frequency of temperature readings recorded with the GeoSense. We found that 17.5% of temperature data was above the highest temperature recorded in Singapore (36°C). It is highly unlikely that these temperatures are achieved as the hottest month in Singapore is May and the data was collected in June.
Apart from this, there was some unusuable data measured by other sensors. 3% of Carbon Monoxide ppm data recorded values less than 0. In addition, 35.5% of noise data was above 130dB, and 1.68% of the data was above 194dB, the theoretical limit for loudness on earth. After counting which GeoSense teams recorded their location, we found that 62.5% of teams did not do so. This would render a lot of the data unusable as the location is extremely crucial in making sense of the data.