Thesis subject

MSc thesis topic: Trend analysis explaining healthy green cities

Currently 55% of the world population lives in cities. UN projections estimate this will grow to 68% in 2050. With a total of 9.6 billion, cities need to be adapted to secure a healthy environment to live in.

The 3-30-300 rule [1] is defined as a concept to create healthier and greener cities. The rule is a set of three quantitative measures: 3 visible mature trees from every home, 30 percent tree canopy cover in every neighbourhood and 300 metres from the nearest high-quality public park or other green space. All three measures can be determined for different cities with different sizes.

With all its different functions, cities are clear examples of complex systems. For the explanation of the functioning of complex systems, scale plays an important role. Where population, road density and other factors might grow linear with city size. Other factors show increasing or decreasing trend with city growth. Functioning and comparison of cities can be analysed using aggregated statistics. Bettencourt [2] proved that explaining factors of a city can be captured in linear function. As an example, city size can be linear related to its population. Besides linear scaling, where a doubling of the population is linearly correlated to its size, population growth might also increases faster with size. He determines sub-linear- scaling, and its counter gesture super-linear scaling when population growth increases slower compared to the city size.

Objectives and Research questions

Based on the trend analysis, one can determine cities that do follow determined trends and those that perform different. Especially those that differ, are interesting for further exploration, to explain their differences.

  • Explore if a linear scaling trend applies for the 3-30-300 measures related to factors like population or road density or health or CO2 emission, or urban land use / land cover (fragmentation), or heat stress etc.
  • Determine outliers of the trend analysis and explain their differences.

Requirements

  • Proficiency with GIS
  • Work with large data
  • Python skills

Literature and information

Theme(s): Modelling & visualisation; Human – space interaction