LIGHT-DATA

To better understand the relationship between a country’s standard of living and the amount of light it is producing, I had to collect and cross reference 2 sets of data;

Firstly, Gross Domestic Product (GDP) data was collected over a period of 10 years from the top earning countries, together with the lowest earning countries. This data is then compiled into a bar graph for a clear visualisation for the comparison of high and low GDP earnings.
GDP data is then cross referenced with the same few countries light lux levels. This is then used to compare with the international average of 1 to determine which category the country falls under; Optimum brightness, below average or above average.

Link to data sets

Data Visualisation

I had to explore various different graph types to test out which would best fit the set of data I have collected. From donut to line to scatterplots, I felt that the bar graph was the best fit as with its varying heights of the bars, I could show the vast difference between the extreme high and low values most efficiently.
I saw an opportunity to create many interesting outcomes with the different bar heights generated, which helped me to further interpret the data visualization generated and conceptualize ideas for my artefact.

Findings

Countries with higher GDP rates were megacities with higher standards of living. They were also among the few countries that shone the brightest - with their average lux level ratio per country rising above the international average (1.74 (USA)/ 1.5 (China)/ 1.3 (Japan) to 1). In contrast, those that fall in the list of countries with the lowest GDP rates in 2020, were the same countries that happened to be the darkest - their average lux level ratio per country falling below the international average (0.8 (South Africa)/ 0.7 (Yemen)/ 0.5 (Afghanistan) to 1).

INSIGHTS AND CONCLUSION

The megacities with higher standards of living were of course the countries that were more urbanized, with increased technological advancements resulting in higher accessibility to electricity. These urbanized megacities are electrically charged and ready to work beyond the limitations of daylight hours, thus these are the same cities that shine the brightest at night.
On the other hand, countries that are less developed would not have the same level of development, let alone urbanization. Thus their accessibility to electricity as compared to the former example would definitely be drastically different. Without the electricity required to power light, it is inevitable that these countries would be engulfed in darkness when night falls.

However both scenarios each have their own advantages and disadvantages, with the megacities shining ever so brightly, the people and animals living there would feel the impact of the city’s light pollution. Biological clocks of both humans and animals alike would be affected, our body’s physiological processes are disorientated, resulting in a number of health issues like insomnia, fatigue, and increased stress, to name a few.

References

  • "Earth At Night". Earthobservatory.Nasa.Gov, 2016, https://earthobservatory.nasa.gov/features/NightLights/page3.php.
  • "The Age Of Megacities". Arcgis Storymaps, 2021, https://storymaps.arcgis.com/stories/a900831b442e43c79cf9eeb399d5440f.
  • Buchholz, Katharina. "Infographic: The Biggest Economies In The World". Statista Infographics, 2020, https://www.statista.com/chart/19489/biggest-economies-in-the-world/.
  • Cheng, Gwyneth. "A Rising Star". Kontinentalist, 2021, https://kontinentalist.com/stories/light-pollution-effects-put-singapore-and-hong-kong-on-the-map.
  • Evans, Cindy, and Will Stefanov. "Cities At Night: The View From Space". Earthobservatory.Nasa.Gov, 2008, https://earthobservatory.nasa.gov/features/CitiesAtNight.
  • Kadaba, Dipika. CARTO, 2018, https://dipika.carto.com/tables/urbanlightpollution_world/public.
  • Kadaba, Dipika. "Big Cities, Bright Lights: Ranking The Worst Light Pollution On Earth". The Revelator, 2018, https://therevelator.org/cities-ranked-light-pollution/.
  • O'Neill, Aaron. "Afghanistan - Gross Domestic Product (GDP) From 2006 To 2026". Statista, 2021, https://www.statista.com/statistics/262048/gross-domestic-product-gdp-in-afghanistan/.
  • O'Neill, Aaron. "South Africa - Gross Domestic Product (GDP) 2026". Statista, 2021, https://www.statista.com/statistics/370513/gross-domestic-product-gdp-in-south-africa/.
  • O'Neill, Aaron. "The 20 Countries With The Largest Gross Domestic Product (GDP) In 2020". Statista, 2021, https://www.statista.com/statistics/268173/countries-with-the-largest-gross-domestic-product-gdp/.
  • O'Neill, Aaron. "The 20 Countries With The Lowest Gross Domestic Product (GDP) In 2020". Statista, 2021, https://www.statista.com/statistics/256547/the-20-countries-with-the-lowest-gdp-per-capita/.
  • O'Neill, Aaron. "Yemen - Gross Domestic Product (GDP) 1996-2026". Statista, 2021, https://www.statista.com/statistics/524134/gross-domestic-product-gdp-in-yemen/.