Generative approaches to masterplanning can be extremely valuable in providing insight to a project and can highlight optimum scenarios that may not be obvious from a conventional design approach.
We can work with you to develop a growth or optimization algorithm tailored to your spatial planning requirements, working with our specialist partners where appropriate. Please note the basic CityCAD platform does not include generative layout design, and this is only offered as a consultancy service. While generative approaches can be very powerful, they should be treated with extreme caution in terms of the introduced design criteria governing each planning initiative. Even a small subset of design variables can affect critical masterplanning decisions aimed at creating successful urban places.
Above: In this technology demonstrator of a potential redevelopment of Heathrow Airport, we assumed that houses next to green space would generate higher values than others. This led the optimization algorithm to propose elongated residential areas in order to maximize their land value across the site. The detailed feasibility of these forms can then be explored using CityCAD to estimate the quantity of roads needed to service this layout.
Generative approaches can be used to optimize a masterplan layout. For example, if a site has zoning/planning permission for 3,000 houses, an optimization algorithm can suggest a layout that might provide the highest financial return while still complying with other environmental or liveability criteria.
There are three main things to consider when preparing this type of analysis:
- Target objective functions - these are objective functions to be minimized or maximized. For example, we can aim to maximize the overall land value, or minimize the average distance between houses and public transport nodes. The planner can apply different weightings for each design objective function or let the optimizer provide the best maps on a Pareto basis.
- Constraints - these are defined limiting factors. For example, we could say that no building should be more than five storeys in a particular area, or that industrial land uses cannot be located next to residential land uses.
- Spatial planning parameters to be optimized - in many cases this will simply be the land use type of a specific parcel of land. In more detailed examples, we could vary other parameters such as the capacity of transport infrastructure or the height of buildings.
Other practical applications of spatial optimization technology include:
- Evaluating the performance of existing masterplans and proposals.
- Evaluating new zoning and planning regulations so as to enhance their effectiveness in practice.
- Optimize the location of new land uses on opportunity sites within a city, in order to meet specified employment and housing criteria most efficiently, or additional planning objectives and constraints.
- Optimize the location of renewable energy sources within a region, for example based on recorded wind potential, land use restrictions and other factors.
- Determination of optimal locations for unique land uses such as landfill sites and power plants, and the zones for network development such as roads, gas pipes and other linear infrastructure.
- Optimum spatial allocation of agricultural development within a region based on factors such as availability of water, slope of the ground and soil characteristics.