Complexity and emergence in city systems: implications for urban planning

Cities can be regarded as the quintessential example of complexity. Insofar as we can define a hidden hand determining their morphology, this is based on the glue that stitches together the actions of individuals and organizations who build and plan the city from the ground-up, so-to-speak. When general systems theory entered the lexicon of science in the mid-20th century, cities were regarded as being excellent examples of systems with interactions between basic elements that demonstrated the slogan of the field: the ‘whole is greater than the sum of the parts’. Since then, as complexity theory has evolved to embrace systems theory and as temporal dynamics has come onto the agenda, cities once again have been used to illustrate basic themes: global organization from local action, emergent morphology from simple spatial decision, temporal order at global levels from volatile, seemingly random change at the level of individual decision-making, evolution and progress through co-evolution, competition, and endless variety. Here we will sketch these ideas with respect to cities illustrating particularly three key ideas which involve the tension between continuously changing systems, qualitative transformations, and radical change based on emergent properties of the whole. Our analysis has many implications for a new theory of urban planning which is built from the bottom up, rather than from the top down which is the traditional way in which such interventions are currently carried out in the name of making better cities. Contemporary problems such as ethnic segregation, urban sprawl, traffic congestion, urban decline, and regeneration are all informed by the perspective on complexity theory that we bring to bear her

Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin

Network geography: relations, interactions, scaling and spatial processes in GIS

This chapter argues that the representational basis of GIS largely avoidseven the most rudimentary distortions of Euclidean space as reflected, forexample, in the notion of the network. Processes acting on networks whichinvolve both short and longer term dynamics are often absent from GIscience. However a sea change is taking place in the way we view thegeography of natural and man-made systems. This is emphasising theirdynamics and the way they evolve from the bottom up, with networks anessential constituent of this decentralized paradigm. Here we will sketchthese developments, showing how ideas about graphs in terms of the waythey evolve as connected, self-organised structures reflected in theirscaling, are generating new and important views of geographical space.We argue that GI science must respond to such developments and needs tofind new forms of representation which enable both theory andapplications through software to be extended to embrace this new scienceof networks

Big data, smart cities and city planning

I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems

Scaling and universality in the micro-structure of urban space

We present a broad, phenomenological picture of the distribution of the length of open space linear segments, $l$, derived from maps of 36 cities in 14 different countries. By scaling the Zipf plot of $l$, we obtain two master curves for a sample of cities, which are not a function of city size. We show that a third class of cities is not easily classifiable into these two universality classes. The cumulative distribution of $l$ displays power-law tails with two distinct exponents, $\alpha_B=2$ and $\alpha_R=3$. We suggest a link between our data and the possibility of observing and modelling urban geometric structures using Levy processes.Comment: 11 pages, 3 figures; minor change

Complexity in city systems: Understanding, evolution, and design

6.4 Exemplars of complex systems There are many signatures of complexity revealed in the space-time patterning of cities (Batty, 2005) and here we will indicate three rather different but nevertheless linked exemplars. Our first deals with ..

Hierarchies in cities and city systems

Hierarchy is implicit in the very term city. Cities grow from hamlets andvillages into small towns and thence into larger forms such as ?metropolis?,?megalopolis? and world cities which are ?gigalopolis?. In one sense, allurban agglomerations are referred to generically as cities but this sequenceof city size from the smallest identifiable urban units to the largest containsan implicit hierarchy in which there are many more smaller cities thanlarger ones. This organisation approximately scales in a regular but simplemanner, city sizes following a rank-size rule whose explanation is bothmysterious and obvious. In this chapter, we begin with a simple but wellknownmodel of urban growth where growth is randomly proportionate tocity size and where it is increasingly unlikely that a small city becomesvery big. It is easy to show that this process generates a hierarchy which isstatistically self-similar, hence fractal but this does not contain anyeconomic interactions that we know must be present in the way cities growand compete. We thus modify the model adding mild diffusion and thennote how these ideas can be fashioned using network models whichgenerate outcomes consistent with these kinds of order and scaling. Wethen turn this argument on its head and describe how the same sorts ofmorphology can be explained using ideas from central place theory. Thesenotions are intrinsic to the way cities evolve and we conclude by notinghow city design must take account of natural hierarchies which groworganically, rather than being established using top-down, centralizedplanning

A rigorous definition of axial lines: ridges on isovist fields

We suggest that 'axial lines' defined by (Hillier and Hanson, 1984) as lines of uninterrupted movement within urban streetscapes or buildings, appear as ridges in isovist fields (Benedikt, 1979). These are formed from the maximum diametric lengths of the individual isovists, sometimes called viewsheds, that make up these fields (Batty and Rana, 2004). We present an image processing technique for the identification of lines from ridges, discuss current strengths and weaknesses of the method, and show how it can be implemented easily and effectively.Comment: 18 pages, 5 figure