Multi-Criteria Spatial Optimisation of Christchurch’s Urban Development

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.

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Background

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.

Planning Objectives

Objective name Description Parameterisation
f_tsu Minimise exposure to inundation from a tsunami Normalised by the maximum inundation depth in any SA
f_cflood Minimise exposure to a 1 in 100-year coastal flooding surge. Includes increased exposure due to sea-level rise in increments of 0.1 meters, up to 3 meters Normalised by the maximum inundation depth in any SA
f_rflood Minimise exposure to future river flooding risk Data did not provide inundation depths, just where it was affected by the flooding. As a result, the objective function returns a binary result
f_liq Minimise exposure to future liquefaction risk Assigned a value in the range 0-1 based on liquefaction susceptibility and judgement
f_dist Minimise the distance of new development to town centres to minimise travel Normalised by the maximum distance for the centroid of any SA from a town centre
f_dev Minimise expansion of urban sprawl in rural zones Objective function scores each SA based on the percentage of the area which is zoned be developed on (residential, mixed)
Data Provider Data Type Data Date Description Source
Environment Canterbury Raster 2019 Ensamble maximum flow depth from a simulated earthquake-induced tsunami, caused by a 1 in 2500 year earthquake in South America Private data sharing agreement with Environment Canterbury, from November 2019 consultancy report by GNS Science titled “Multiple scenario tsunami modelling for Canterbury"
National Institute of Water & Atmospheric Research (NIWA), NZ Polygon 2020 Extreme sea level (ESL) for 1% AEP coastal flooing, given a range from 0m to 3m of sea level rise (SLR) in 10cm increments Private data sharing agreement
Christchurch City Council Polygon 2018 Land which is potentially suscetable to inundation during an extreme hydrological event (1 in 500 years), which may pose a risk to life or property due to water velocity and/or inundation depth experienced Canterbury City Council Geospatial Public Portal. Layer: “FloodHazardHigh” through WFS dataset sharing
Canterbury Maps Polygon 2020 Vunerability to Liquefaction Tonkin + Taylor July 2020 report titled “Christchurch Liquefaction Vulnerability Study”
Canterbury Maps Polygon 2017 Key Activity Centres as shown in the Land Use Recovery Plan 2013. A new polygon features was manually added to represent the new development of the Riverside Market in the recent year Data originated from Enivronment Canterbury
Christchurch City Council Polygon 2018 Land use activity boundaries as identified in the District Plan. The zone is defined by type and code Canterbury City Council Geospatial Public Portal. Layer: “Zone” through WFS dataset sharing

Summary of Results

With the example objectives and using a balanced weighting scheme for a high dwelling projection

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)

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)

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