Helin Liu

Simulating the Dynamics Between the Development of Creative Industries and Urban Spatial Structure: An Agent-Based Model

Creative industries have been widely adopted to promote economy growth, urban regeneration and innovation. It is expected that this strategy can produce a sustainable development model. However, in reality it is not effective enough because the implemented policy based on linear analysis is misleading. This chapter aims to fill this gap by examining the dynamics among creative industries, urban land space and urban government from a complex systems’ view. It presents a general simulation framework and an agent-based model (named “CID-USST”) developed in NetLogo. This is a spatially explicit model where a simplified urban space is used to represent the real urban land space. The agents involved include the creative firms, the creative workers, and the urban government. The resulting urban spatial structure is examined from two aspects: the spatial density distribution and the spatial clustering pattern of both the creative firms and the creative workers.

Incorporating GIS data into an agent-based model to support planning policy making for the development of creative industries

This paper presents an extension to the agent-based model “Creative Industries Development–Urban Spatial Structure Transformation” by incorporating GIS data. Three agent classes, creative firms, creative workers and urban government, are considered in the model, and the spatial environment represents a set of GIS data layers (i.e. road network, key housing areas, land use). With the goal to facilitate urban policy makers to draw up policies locally and optimise the land use assignment in order to support the development of creative industries, the improved model exhibited its capacity to assist the policy makers conducting experiments and simulating different policy scenarios to see the corresponding dynamics of the spatial distributions of creative firms and creative workers across time within a city/district. The spatiotemporal graphs and maps record the simulation results and can be used as a reference by the policy makers to adjust land use plans adaptively at different stages of the creative industries’ development process.

Examining the dynamics of the interaction between the development of creative industries and urban spatial structure by agent-based modelling: A case study of Nanjing, China:

Much of the focus of research on creative industries’ influence upon urban land use has been around the investment in specific regeneration projects or flagship developments rather than addressing the nature and location of the infrastructure, networks and agents engaged. In other words, the complexity of the institutional/temporal and spatial interaction among the involved elements is overlooked or not well understood. This paper presents an agent-based model named CID-USST (Creative Industries Development-Urban Spatial Structure Transformation) that examines the dynamics of the interaction between the development of creative industries and urban spatial structure by outputting a set of adaptive scenarios through time and space. It reveals that the spatial distribution of both the creative firms and the creative workers evolves in a repeating up-and-down pattern even when the exogenous urban economic condition is set to be steady. Moreover, the analysis also points to the policy implication that more open job/rent market information will lead to more rapid geographical clustering of the creative firms and the creative workers, which possibly may reduce the time cost in their spatial evolvement, and perhaps accelerate innovation if we accept that geographical proximity can enhance knowledge and information spill-over.

Conclusions and Further Development

This book aims to explore the dynamics of the interaction between the development of creative industries and urban spatial structure by agent-based modelling. Regarding the complexity of the interaction, it is proposed that this dynamics should be understood as a bottom-up and top-down process. In order to pave the foundation for agent-based modelling, a case study of Nanjing metropolis has been conducted. Through it, the locational behaviours of the firms and the workers, and the respective role of each involved interest group are clarified. By referring to these findings, an agent-based model named CID-USST is then developed and applied to scenario analysis with the aim to generate deeper insight into the dynamics. However, the spatial environment of this model is an abstract single-centred urban space, which limits its applicability to customising geographically coordinated policies at suburban-district level. Thus, the book continues with a further development of the model by incorporating the GIS data of Nanjing in the last chapter. In this concluding chapter, it will highlight the key findings and the possible topics for further development.

The Foundation for Agent-Based Modelling: Empirical Evidence of Creative Industries’ Interactions with Urban Land Use in Nanjing

The underlying idea of agent-based modelling is that many aggregate phenomena emerge from the complex interactions among individuals at a lower level in a system; the rules of these interactions are supposed to be simple which can be easily described and understood by mathematical or computational languages. So, the very foundation for agent-based modelling is to figure out these rules. By mining data collected in Nanjing, this chapter aims to provide empirical evidence for the generalisation of agent-based modelling-oriented interaction rules. First, the development of Nanjing is reviewed in historical perspective followed by an examination of the spatial distribution of the creative firms and the creative workers. Second, the locational factors that shape the firms’ office and the workers’ housing location preference are examined via data from questionnaires and checked against conclusions drawn from GIS analysis. Then, it proceeds to explore the citizens’ attitude/reaction towards creative industries’ booming and the corresponding supportive policies enforced by the government. Finally, by synthesising the conclusions drawn from the above analyses, the interactions among the four interest groups (the creative firms, the creative workers, the individual citizens and the urban government) are generalised into a dynamics framework which is the basis for modelling in the next chapter.

Examining the Dynamics by Incorporating GIS Data with the CID-USST Model

The spatial environment of the CID-USST model is a highly abstract and simplified version of Nanjing. One obvious limitation resulting from this manipulation is that policy implications cannot be easily projected to real urban location. In practice, however, policy makers are keen to know exactly where to invest so as to optimise plan efficiency constrained by limited budget. In addition, it is also crucial to customise policies and adjust land-use plan accordingly locally referring the dynamics of the creative industries in different sites. Regarding this, this chapter illustrates a further development of the agent-based model “CID-USST” by incorporating GIS data. The goal is to facilitate urban policy makers to decide where to develop offices for the creative firms and housing for the creative workers once a comprehensive land-use plan is formally approved and to customise supportive/guiding policies locally.

Simulating the Dynamics of Creative Industries’ Interactions with Urban Land Use by Agent-Based Modelling

The last chapter examined the locational behaviours of the creative firms and the creative workers and their interactions with the urban government and the citizens in Nanjing. This chapter continues with the aim to demonstrate how these empirical observations are parameterised and the dynamics simulated by agent-based modelling. It begins with a brief introduction to the model development platform NetLogo and its capability to simulate the dynamics. Then, it proceeds to model design, the first step of agent-based modelling which explains three issues: (1) How is the theoretical dynamics framework proposed in the last chapter further developed and simplified into an agent-based modelling framework? (2) How is the abstract urban space of Nanjing described in the model? (3) How are the condition-action rules of the three agent classes quantitatively defined? After this clarification, the chapter comes to its final section which focuses on the second step of modelling: model implementation. It details how the model design is translated into the supposed agent-based model in NetLogo.

Model Validation and Scenario Analysis

Now, we have already developed an agent-based model to simulate the dynamics of the interactions between the involved agents (the creative firms, the creative workers and the urban government) and urban land use. It is expected that, through scenario analysis by using this model, further insight into this dynamics can be generated. However, scenario analysis by using an agent-based model without validation of its correctness and reliability can very likely produce misleading conclusions. Regarding this, this chapter first concentrates on model validation. It then proceeds to scenario analysis, in searching for new features of the dynamics and further policy implications.

The Development of Creative Industries and Urban Land Use: Revisit the Interactions from Complexity Perspective

It took more than 50 years for the concept of “creative industries” to evolve from “culture industry”, through “cultural industries” to “creative industries” (O’connor 2007). In contrast to this comparatively long conceptual evolution history, the time for creative industries to gain its global promotion is much shorter, only around 15 years since its coinage in the 1990s. The underlying policy rationale, as Foord (2008) concludes, is urban policy makers’ high expectation of urban growth and innovation. The wide cultivation of creative industries in urban development scheme, inevitably, presents urban government the issue of how to arrange land space to accommodate creative industries in an efficient and adaptive way. This question cannot be easily solved without a comprehensive and insightful understanding of creative industries and the dynamics of their interactions with urban land use. This chapter aims to revisit existent theoretical discussions on this aspect.

Application of Agent-Based Modelling to the Dynamics of Creative Industries’ Interactions with Urban Land Use: An Introduction

As has been proposed, the dynamics of creative industries’ interactions with urban land use is complex and can be examined by the approach of agent-based modelling. In agent-based modelling, one central issue is to clearly define the rules that the agents follow. However, the locational behaviours of the creative firms and the creative workers are not easy to describe as the factors are multidimensional. This chapter focuses on explaining how the concept of locational utility function is introduced to describe the locational behaviours of the firms and the workers and what the requisite data are for this purpose.