New Hong Kong

Running simulations for a new city


Context

The following exercise has the scope of utilising the evolutionary multi-objective optimization engine for Grasshopper 3D Wallacei in a specific urban tissue and generating different simulations aiming to find the fittest solution to a problem that is constrained by a set of predefined limitations. The urban tissue to “optimize” in this exercise is the one of the city of Hong Kong, specifically the one of the Kowloon Peninsula.


Challenges

Extended and comprehensive research based on academic papers, news, and official reports has been developed in order to identify what are the problems of Hong Kong. Considering the need of operationalizing them through Grasshopper codes, the final 3 challenges selected are the following:


Fig. 1 - Hong Kong challenges

    Heat Island effect
  • Definition: Metropolitan area which is significantly warmer than its surrounding rural areas
  • Current situation: At the peak of summer in Hong Kong, every degree Celsius rise in temperature results in several more deaths associated with excessive heat exposure. UHI also exacerbates heat waves, increases the risks of heatstroke and exhaustion, especially among those with chronic diseases and working outdoors

    • Light pollution
  • Definition: Brightening of the night sky caused by street lights and other man-made sources, which has a disruptive effect on natural cycles and inhibits the observation of stars and planets
  • Current situation: Hong Kong has been named the world’s worst city for light pollution. Some areas, like the Tsim Sha Tsui are 1,200 brighter than a normal dark sky. Overall brightness is at least 30 times higher than in the countryside

    • Mental health crisis
  • Definition: Residents struggling with stress, depression, anxiety, or mental disabilities
  • Current situation: Official statistics show that 1 in 5 Hong Kong employees are on the job an average of 55 hours per week and 1 in 6 residents had a common mental disorder, such as anxiety, depression, and psychotic disorder.

Preparation of the phenotype

The final design approach to design the phenotype has been based on the characteristics elements of the urban morphology of Hong Kong. In fact, after trying to use an existing grid of Hong Kong and to generate our own grid, the final decision has been to populate a specific area with buildings and generate our own version of Hong Kong by using the main characteristics of its essence:

  • High building density
  • Narrow streets
  • Lack of green spaces
  • High buildings (average over 40 floors)
  • High variability of heights
  • Non-orthogonal grid

Definition of the fitness criteria (objectives)

Once defined the challenges and the phenotype, we proceeded to define the fitness criteria for the development of the project. In total, 5 fitness criteria were selected:

Fig. 2 - Fitness criteria

  • Maximise green façadesin areas where there is a higher solar exposure. This will help to reduce the heat island effect by reducing the number of reflective surfaces and at the same time will have a positive impact on the employees’ mental health, since it will increase the contact with natural elements and sunlight
  • Maximise parasitic structuresaiming to generate new green spaces vertically considering the high building density and the lack of green areas. Similar to the green façades, the impact is linked both to the heat island effect and the mental health crisis
  • Maximise bridge visibility: mainly related to tas a way to generate new green spaces accessible from the office buildings. The logic behind is similar to generating large spaces and with higher visibility. Hong Kong has a tradition to generate skyways, like the Central Elevated Walkway, and our proposal tries to re-interpret such structures
  • Maximise total frontal area: specifically the proportion of areas for each specifunderstood as a measure to increase wind penetration and consequently reduce the heat island effect. The frontal area index considers the projection of building façades on a panel considering the direction of the wind…
  • Minimise light pollution generated by the buildings. At the same time that buildings receive sun exposure, they also generate light pollution at night. A higher building density increases the light pollution generated

Methodology

The setup for the urban fabric started with creating buildings that were scalable in XY directions and rotate around the center and the generation of the heights that represented the characteristics of Hong Kong’s skyline. Next, the connections between all the buildings were created by generating all the potential connections and in order to select the bridges that have the highest visibility in the urban fabric an isovist visibility analysis was done and the 10% with the highest visibility was used to optimise the selection of bridges with the highest visibility. Secondly, a solar analysis was done on the buildings to maximise solar exposure and introduce parasitic structures. These structures were added in the areas where the solar exposure was at its highest level. As a third step, the frontal area index was calculated using occlusion and calculating the inverted area of the projected one on a specific panne. Finally, the light pollution was calculated by taking into consideration the heights of the buildings estimating that the taller the buildings the higher the light pollution and with this, we tried to optimise by minimising the light pollution.


Fig. 3 - Methodology

Results of the evolutionary simulation

The setup for the urban fabric started with creating buildings that were scalable in XY directions and rotate around the center and the generation of the heights that represented the characteristics of Hong Kong’s skyline. Next, the connections between all the buildings were created by generating all the potential connections and in order to select the bridges that have the highest visibility in the urban fabric an isovist visibility analysis was done and the 10% with the highest visibility was used to optimise the selection of bridges with the highest visibility. Secondly, a solar analysis was done on the buildings to maximise solar exposure and introduce parasitic structures. These structures were added in the areas where the solar exposure was at its highest level. As a third step, the frontal area index was calculated using occlusion and calculating the inverted area of the projected one on a specific panne. Finally, the light pollution was calculated by taking into consideration the heights of the buildings estimating that the taller the buildings the higher the light pollution and with this, we tried to optimise by minimising the light pollution.


Analysing the 5 fitness criteria together

The process to generate simulations aimed to understand the existing links between genes and fitness criteria. Therefore, the first simulation considered the 5 fitness criteria (generation size: 20 and generation count: 20) and these are the main elements of the analysis:


Fig. 4 - Fitness criteria analysis

  • No relevance of the wind penetration: the evolution of the Standard deviation curve is becoming flattered and the fitness value is identified in the first iteration. At the same time, the SD value trendline and the mean value trendline grow in an unstable pattern.
  • Light pollution works in an opposite effect: as per the parallel coordinate plot, there is evidence on how the minimization of light pollution works in reverse compared to the rest of the indicators. Considering the way the light pollution is measured, indeed it works in an opposite way: the smaller the building, the smaller the light pollution generated. On the other hand, the rest of the fitness objectives (except the wind penetration) are positively correlated with the size of the building: the higher the building, the higher solar exposure.

Fig. 5 - Exported solutions 3D

Fig. 6 - Exported solutions

Analyzing the selected 3 fitness criteria

The previous analysis of the results showed how 2 of the fitness criteria selected are not supporting in the optimization process:

  • Maximise wind penetration: due to the building density, there is no much variability.
  • Minimise light pollution: the optimisation of these fitness criteria is contradictory in comparison of the other valid fitness criteria.

Wallacei was run a second time excluding the non-valid fitness criteria (generation size: 20 and generation count: 20).


Conclusion

To conclude, the selected 3 fitness criteria are optimised to their individual parameters. Yet, the morphologies produced do not lead directly to results that could be correlated with the urban environment. Even though the final phenotypes present differences, the characteristics that distinguish one from the others are quite eminent. The results are morphologically similar to the urban fabric of the Hong Kong case study, an observation that could lead to the conclusion that given the current structure of the city, the proposed scenarios could be integrated rather easily. Yet, a further investigation of the grid of the city could lead to more accurate conclusions. Hence, the model could have more potential if it was tested with more variations on the given phenotypes.



New Hong Kong is a project of IAAC, Institute for Advanced Architecture of Catalonia developed at Master in City & Technology in 2020/21 by students: Riccardo Palazzolo, Aishath Nadh Ha Naseer and Diana Roussi, and faculty: Milad Showkatbakhsh