X14. London prototype. Conceptual age simulation model - colouring-cities/manual GitHub Wiki

Author: Polly Hudson, 2019

Introduction

This section uses a conceptual model to explain the potential of Colouring Cities platforms to operate as digital twins providing insights into the relationship between urban resilience, age diversity, lifespans and the speed of change, using spatiotemporal microsimulations employing longitudinal and live dynamics data. It also looks towards addressing Mike Batty and Paul Longley’s call in 1994 for models able to mimic the process of city ageing.

In the conceptual model, churn in the stock is calibrated by a time slider. This enables the impact of accelerating or decelerating demolitions and new build rates in cities to be visualised (in colour). Patterns emerging at city scale will depend on initial stock composition and local rules applied in relation to age, location, land use etc. as well as to rules and hierarchies of change between urban components (as also discussed in Chapter 4). Though the conceptual model is used here to study change to building age the model could also be used to illustrate change in relation to other types of attribute.

The model is set to operate as a constrained system where pressure on land is assumed, meaning that new build will automatically replace demolished buildings. The maximum time period over which the model will run is 500 years, with the maximum frequency of demolition on the same site constrained to five per century. A zoom facility enables the city to be viewed at building, plot, streetblock, neighbourhood, district and city level. Colour-coded age bands for twenty-year increments are added to enable patterns of diversity to become visible in a way not possible using larger temporal intervals. Here, the lightest colour represents the youngest buildings and the darkest, the oldest. When the slider is set to 0, no turnover will take place within the stock. In this scenario, a state of economic inactivity will exist. Absence of such activity can indicate stagnation and failure of stock to adapt sufficiently to accommodate changing social, economic and technological needs. It can also forewarn of potential economic collapse. The city will be deserted, perhaps through natural or manmade disaster; population migration resulting in shifts in technology; depletion of previously abundant resources; or through the failure, as in the case of Roman cities, of an empire or political system. This can be seen to also represent ‘unbuilding communities’ as defined by Chavez et al. and referred in Chapter 2. The colour of the city may be light or dark depending on how recently churn ceased. Jacobs argued that an urban area (regardless of size) will be ‘all old because it is a failure’ rather than it being a failure because it is old (Jacobs, 1961, p.189). This is because new insertions are essential for urban vibrancy and economic success (Jacobs, 1961).

A lack of lighter colours therefore indicates lack of economic vibrancy. As the slider is pushed forward, the stock begins to turn over, though still at a very slow rate. Economic activity can now be detected, but, if the attractiveness of land in the city, or areas in it, is low, little incentive will exist either to develop or demolish. In this scenario, the regeneration of building stocks is difficult, with Thomsen et al. noting that ‘a low and/or declining market demand generally results in a slow speed of transformation that might lead to dereliction’ (Thomsen et al., 2011a, p. 329). It may also indicate population shrinkage and/or economic decline. As time progresses, most of the city’s buildings will become old and the mean age of the stock will increase, owing to sluggish rates of demolition and new construction. If slow activity persists, the ratio of old buildings to new will increase and the model will become increasingly dark in colour.

We now push the slider until the speed of change can be described as incremental or piecemeal. Existing buildings are steadily replaced with new, with adaptation in plots common. As rates of construction and demolition are slow, low volumes of energy and building waste are generated. A mixture of buildings of different ages and types is created as new buildings are not demolished before they have time to age (Jacobs, 1961). Long periods of incremental development in the city, or areas within it, will result in a diversity of age represented by many colours. Such a rate of change is described by M.R.G Conzen as ‘repletion’ (Jones and Larkham, 1991) and is typical of slow-growing traditional economies in Europe. Thomsen et al. note that ‘until a century ago the small-scale “organic” renewal and transformation process was the normal way buildings and towns were redeveloped’ (Thomsen et al., 2011a, p. 352). An option to zoom to plot level and view buildings in 3D is also included, to understand age diversity at sub-building level. Here each new accretion is separately coloured in 3D in the style of Fani Kostourou an array of colours as step-by-step change is made within plots.

As the rate of new construction is increased, to a limit of around 1% per annum, the stock’s behaviour and composition begins to mimic that of mature European cities, and what Chavez et al. describe as ‘built’ communities. Compared to rapidly urbanising areas, construction and demolition rates in this scenario are still relatively low. Rather than piecemeal development occurring across the city, chunks of it are now more likely to be redeveloped at one time, though these may sit alongside designated areas, where demolition is slowed, creating patches of dark and light colours. Rapid change to areas of a mature city may also be the result of booms and slumps associated with economic cycles or innovation in construction technology and transport (Whitehand, 1994); slum clearance policy (Jacobs,1961; Power, 2010), redevelopment relating to, a natural disaster or war; or perhaps the consequence of policies of an autocratic regime. This causes bursts of lightness to appear across the model. As turnover rates increase, the mean age of the stock will decrease and, although older buildings may survive through protection, age diversity will be less than the piecemeal growth scenario above. Lighter colours will increase the more older buildings are replaced by new. Diversity of colour now begins to only be seen in specific locations, rather than across the model as a whole.

In our final scenario, the slider is pushed close to its limit. Buildings are now rapidly demolished and replaced. The turnover is so fast that the city’s stock becomes young again, characterised by single-age construction. This type of scenario represents Chavez et al.’s ‘unbuilt’ communities classification. Here the city loses its diversity and is represented only by the lightest colour band i.e. buildings that are less than 20 years old. As lifespans are so short, related volumes and flows of materials and energy are very high. Unless protected through designation or other planning constraints, cities will, in this scenario, lose finite historical and cultural assets within a very short period space of time. In such cases, deep-rooted associations that make up the cultural identity of cities will also be lost (Liu, 2014). The capacity of this type of urban area to attract diverse populations and generate vitality now becomes limited, with affordability decreasing as low-cost, older rental property becomes more scarce (Jacobs, 1961). Attraction and uniqueness also decrease owing, to the lack of diversity of uses, and of age and form. Resilience will also decline owing to the capacity for weaknesses in design, and failure in construction systems and materials to be repeated across homogeneous area. More limited opportunities also exist for knowledge spill-over and innovation (ibid.). Lack of time between redevelopments also means lack of time for buildings to adapt through trial and error and to identify and allow weak typologies, features and technologies to be carefully weeded out.

The term ‘ghost city’ can be used to describe both the first scenario in the conceptual model, that of the deserted city, (Jin et al., 2017) and the last. Predictions made in 2001, that in 2015 over half of China’s building stock would be only 15 years old, have now been proven to be correct (Yang and Kohler, 2008), with entire cities built in under fifteen years (Shepard, 2017). Xiaobin Jin et al. characterise these as having low levels of diversity, low population density, high building vacancy rates, extensive unlit built-up areas, and poor vitality (ibid.). Though some will be built from scratch, others will have resulted in large-scale demolition and redevelopment of older stock generated incrementally over many centuries. Thomsen notes that in this scenario ‘a high and/or rising demand usually fuels development and transformation, which may (in combination with changing conditions) potentially lead to excessive speed and even to system overshoot and market collapse. In both cases, the result is a loss of resources, of value in different forms’ (Thomsen et al., 2011a, p. 329).

The model raises many questions: What, for example, will become of ghost cities in the next twenty years? Based on Jane Jacobs’ observations, further tested in Chapter 7, these single-age cities may be subject to ongoing cycles of demolition in future, where all buildings ‘fail’ simultaneously and are then rebuilt, never allowing time for the process of incremental development to generate diversity of age and form, and thus resilience. Should, as Hassler and Jacobs have argued, existing designation/zoning tools be applied at city scale to effect a more precise calibration of rates of change within planning systems? And could interactive rule-based simulations such as this, allowing sliders to be moved to calibrate change and simulate scenarios, based on local rules of change, using historical and current data, be incorporated into open data platforms? Would this not help policy makers and citizens to test and visualise sustainable and unsustainable trajectories?

Colour, interactive simulations of this kind also have the potential to illustrate the ease and speed with which uniqueness, resilience and vitality can be lost in cities, the difficulty of its reconstruction, and the scale of energy and waste flows (physical, socio-cultural and economic) associated with such loss. Though the development of such simulations is beyond the initial scope of the prototype open data platform set up in this study (for which static colour data visualisations are built), additional types of dynamic data, along with rule-based ‘procedural’ models able to support their construction.