How would you imagine a beautiful city for the next generations? Currently, local authorities are unequipped to quantitatively assess areas for future development in a manner that can consider multiple planning objectives. So let’s change that.
By Sam Archie & Jamie Fleming, with supervision by Tom Logan (2020). This report was first published in the November 2020 University of Canterbury Civil and Natural Resources Engineering Research Conference.
The sustainable development of cities has recently been identified as an important way for us to adapt to and help solve climate change. In response, the National Policy Statement on Urban Development requires urban-planners to design future urban areas in New Zealand strategically for the next generations, primarily through intensification of existing residential areas. This paper continues the development of a multi-criteria spatial optimisation framework that uses a genetic algorithm. The framework is applied to the case study of Ōtautahi Christchurch to identify areas of priority for urban intensification, to aid decision-makers where to guide future growth whilst taking into account multiple hazard adaption and sustainability objectives.
This framework aimed to find an optimal, or a series of optimal, scenarios that are better for a range of attributes (known as objective functions).
Although the algorithm is currently programmed for Ōtautahi Christchurch, it is possible for this code to be adapted for other cities in New Zealand. Moreover, this case-study presented uses sample weightings and objective functions throughout the analysis to showcase the effectiveness of the framework. However, with consultation with major stakeholders, the analysis can be fine-tuned to better represent and locate development sites that satisfy their needs.
Data Sources of Objective Functions
Data Sources of Constraints
Data Sources of Dwelling Counts
Summary of Results
With the following example objectives, using a balanced weighting scheme for a high dwelling projection
The height and colour of the extruded statistical areas indicate the relative urban densities; not the height of structures
Figures and Illustrations
Note: Clicking on any image will enlarge it.
Figure 1. Proportions of existing urban densities of Christchurch in 2018, by statistical area, indicating where different transport methods can be supported as outlined by Chakrabarti (2013). Note: A 3D interactive spatial plot of existing densities can be found here
Figure 2. Computational flowchart of the genetic algorithm used to implement the multi-objectional spatial optimisation framework. (Modified from Caparros-Midwood et al., 2016).
Figure 3. Demonstration of the Pareto front for two objectives. (Reproduced from Wang et al., 2015).
Figure 4. Parametrized spatial dataset for each objective function of the Ōtautahi Christchurch case study. A darker shade of red indicates that the statistical area has a high objective function score.
Figure 5. Performance of Pareto-optimal spatial plans that dominate in one objective across all objectives. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Figure 6. Ranked Pareto-optimal development sites. Darker blue signifies where statistical areas appeared more often in the MOPO sets. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Figure 7. Spatial variability of envisioned urban densities of Ōtautahi Christchurch, by statistical area, where the height and colour of the extruded statistical areas indicate relative urban density. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection). Note: A 3D interactive spatial plot of envisioned densities can be found here
Figure A1. Scatter plot of every spatial development plan’s fitness in two competing objective functions analysed in the entirety of the genetic algorithm for the Ōtautahi Christchurch case study. Highlighted is the Pareto-optimal plans along the Pareto-front curve. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Note: Clicking on any image will enlarge it.
Figure S1. Combined map of overall objective scores of each statistical area in Ōtautahi Christchurch, using a balanced weighting scheme between objective functions. A darker shade of red indicates a higher total objective function score.
Figure S2. Pareto fronts. Each set of axis compares one of the objective functions to the other five. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Figure S3. Spatial plots of development plans in the MOPO set for the Ōtautahi Christchurch case study. Each plot represents the development plan that achieved the lowest score in the respective objective. (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Figure S4. All development sites for parent sets at selected generations for the Ōtautahi Christchurch case study (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)
Figure S5. The statistical areas that most commonly appeared in the top 1% (of overall combined objective score) of spatial development plans for the Ōtautahi Christchurch case study (Parents = 1000, Generations = 200, Balanced weightings, High dwelling projection)