A multi-scaled agent-based model of residential segregation applied to a real metropolitan area

https://doi.org/10.1016/j.compenvurbsys.2017.11.002Get rights and content

Highlights

  • A multi-scaled agent-based model for investigating the dynamics of residential segregation

  • Model simulates the relocation of residents of a representative population of a large urban area in a realistic environment

  • Simulation experiments performed using Auckland metropolitan area which revealed the following:

  • higher population growth (& immigration) does not necessarily exacerbate the intensity of residential segregation

  • segregation is not necessarily greater at the mesoscale than at the macroscale

  • change of housing vacancy rate has significant impact on the dynamic and intensity of segregation

Abstract

Residential segregation influences many aspects of urban life. It affects people's access to centres of education, healthcare, business and determines the composition of our neighbourhoods, thereby impacting our social network and urban structure. In order to understand the potential impact of policies on residential segregation and complex urban system, a dynamic modelling support tool would be essential. This research article presents a multi-scaled agent-based model capable of simulating the relocation of residents of a representative population of a large urban area in a realistic environment for investigating the dynamics of residential segregation. Using an experiment, we show that this data-driven model can replicate plausible residential distribution and segregation patterns observed in the Auckland region (New Zealand's metropolis). Simulation outcomes are promising, demonstrating the potential of the model for investigating practical policy-relevant questions and acquiring valuable insights into the future state of the urban mosaic landscape and causes behind residential segregation dynamics.

Introduction

As segregation has become an important feature of a modern city (Batty, 2010), our understanding of its causes, role and impact on the social and urban fabric of our societies remain relatively limited (Bruch & Mare, 2006). Yet, our ability to accurately model, measure, understand and anticipate segregation would be essential in having a more equitable distribution of public services and better social cohesion in the society.

The pioneering work of Thomas Schelling, 1969, Schelling, 1971 was an important milestone in investigating this multifaceted phenomenon (Clark, 1991). It built the foundation for an individually-based modelling (Crooks & Heppenstall, 2012) investigation focusing on the actions of agents (persons/ household embodiments) who made choices regarding where to relocate and live in the simulated world. Although many of these abstract models help us think about the “real world” (Fossett, 2011), the unrealistic and simplistic nature of artificial worlds in Schelling-style models has prompted “questions about how well they portray the neighbourhood dynamics of real cities” (O'Sullivan, 2009, p. 507). As a result, there are indications in the recent years that residential mobility and segregation modelling development point towards more realistic trends and their applicability to real urban areas.

This more realistic modelling approach is often comprised of four key dimensions: 1) more representative spatial characteristics; 2) use of real/empirical data; 3) more consistent and reliable evaluation (e.g. calibration and validation) against empirical benchmarks; 4) broader explanatory factors (determinants).

A model with more realistic characteristics has several advantages. Since “the outcomes of residential segregation models may strongly depend on the way that neighbourhoods are conceptualized and represented” (O'Sullivan, 2009, p. 508), it would be more consistent to use/ integrate real-world data which correspond to the same administrative spatial boundaries based on which data are collected (Rolfe, 2014).

Subsequently, the combination of “real data along their spatial characteristics is the ultimate form of model validation” (Stanilov, 2012, p. 258), as the evaluation of the model would naturally become more intuitive and reliable. Furthermore, inclusion of more explanatory factors in the model would enhance the overall realistic trait of the model, not the least because of the possibility of comparing the effects of the implemented mechanisms on the empirical and historical benchmarks. Overall, a more realistic model has higher potential to communicate its insights more effectively and engage easier with policy-makers (Stanilov, 2012).

Among agent-based models (ABM) in the residential segregation sphere, the pioneering work of Benenson, Omer, and Hatna (2002) has set a high standard for subsequent work (Bruch, 2014, Crooks, 2010, Feitosa et al., 2011, Yin, 2009).

However, a comprehensive assessment of changing patterns of residential segregation should preferably allow a thorough examination at different inter and intra levels of spatially nested entities (Parisi, Lichter, & Taquino, 2011). The geographical scale (e.g. divisions of a subdivided metropolitan area) can portray distinct dimensions of residential segregation (Reardon et al., 2009). A model with multi-scaled capability (illustrated in Fig. 1) will allow the investigation of segregation patterns on both macro-segregation (e.g. Metropolitan Area) and meso-segregation (e.g. Territorial Authority), based on their encompassed micro-spatial units' subdivisions (e.g. Area Units).

Similarly, the interpretation of shifting patterns of residential segregation and its social implications based on a single measure can be considered incomplete. Since there exist various paradigms and interpretations of segregation (Simpson, 2006), it is desirable to measure several dimensions of segregation (Massey and Denton, 1988, Reardon and O'Sullivan, 2004) in order to acquire more comprehensive portraits of the ethnic mosaic state in the meso and macro geographical entities of the urban area.

This research article presents an agent-based model of residential segregation which contributes to the same realistic modelling direction for analysing the effect of residential location decision of individual residents (agents) on the spatial ethnic mosaic pattern of the central Auckland region (New Zealand metropolis).

The following lists original features of the model. Firstly, the model deals with the entire population sizes based on census values, although only the relocating agents (informed by census mobility values for each ethnic group) are stochastically instantiated and make decisions about their residential location. Secondly, the model dynamic of residential location choice comprise of the main contextual mechanisms, including group and personal preferences (e.g. behaviours conditioned by bounded rationality), empirical vacancy rates (as proxy for combination of real estate market condition and (local) government policies related to new housing development), as well as economic conditions (by empirically informed proxy of residents' economic circumstances to relocate locally or globally). Thirdly, while intra-urban migration (movements by existing population within the boundaries of the urban/ metropolitan area and evidently its smaller spatial nested entities) takes place indigenously, inter-urban migration (movements between population of an external urban area and the simulated metropolitan area) has exogenous effect on the simulation dynamic, exhibiting an open urban system. Lastly, the effects of simulating residential decision-making of four major ethnic groups on various dimensions of segregation are measured and calibrated against their equivalent census-based benchmarks, before the simulations are projected into the future using Statistics New Zealand population growth projection estimates at meso-geographical scale as the base of segregation forecasting scenarios.

Thereby, the model is able to simulate future scenarios depending on changes in overall and ethnic-based population growth conditions and their distributions, including factors which are more susceptible to be influenced by macro (state, institutional) actors (such as control of international immigration, population birth rate, housing development/ vacancy rates), as well as micro (individual) actors (such as changing preferences of relocating residents).

In this article, we focus on presenting the following experiments and results. First, we show that the model is capable of generating patterns that are fairly comparable to the empirical benchmarks built from the application of multiple measures of residential segregation on several quinquennial periods of census data, notwithstanding detailed mechanisms regarding residential decision making are not fully present (implemented). Then, we use various experiments with the model to show that 1) higher population growth (and immigration) does not necessarily (automatically) exacerbate the intensity of residential segregation 2) segregation is not necessarily greater at the mesoscale than at the macroscale, 3) change of housing vacancy rate (in this case, tightening) in one of the meso-geographical units has an impact on the level of segregation in other meso-geographical units, as well as on the at the macro spatial unit (in this case, increase of segregation measured by the entropy-based information theroy index).

Section snippets

Study area and its characteristics

The Auckland region is the largest and most populous urban area in New Zealand and is located in the North Island (see Fig. 2). However, this study focuses on five central territorial authorities (TA) of Auckland City (AKL), Manukau (MKU), North Shore (NSH), Waitakere (WTK) and Papakura (PAK). In this article, we identify this macro geographical entity as a Metropolitan Area (MA), which encompasses a total of 316 selected area units (AU). The census data has been adjusted for multiple-ethnicity

Model implementation

Fig. 4 summarises the operation of the model along with scheduled processes. The following sections outline the details of how these are implemented and operationalized. Most of the scheduled processes are applied at micro-geographical level (often with consideration of empirical values available at mesoscale). However, the relocations are individually based and the moving agents will finally resettle in nearby or more distant micro-geographical locations with consideration of their contextual

Model calibration

The parameters involved in the calibration of a given model are normally those for which the exact empirical values are not available/ known or that (empirically informed) presupposed values are not considered fixed in specific scenarios and therefore open to further tuning (adjustment).

However, for the scenarios presented in this article, we (empirically or hypothetically) assigned specific values to the majority of the model parameters with the exception of tolerance (Θ) and ppersist

Model experimentation

Considering the lack of detailed information (data) on the process of residential location decision making and the subsequent limited realistic explanatory factors implemented in the model and the lack of feasibility in assigning empirical values with certainty to few parameters, in this article, we concentrate on presenting the outcomes of selected experimentations which are not vulnerable to potential uncertainties in the model parameters.

Moreover, like many social phenomena, the residential

Conclusion

This research article introduces a new multi-scaled spatiotemporal agent-based model of residential segregation. The new methodological contribution of the model can be described as a combined set of characteristics which are put into operation to investigate residential segregation dynamics with specific reference to empirical (census) data.

The model can deal with entire population sizes corresponding to census values. Similar to microsimulation models, the probability based mobility turnover

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that greatly contributed to improving this article. We would like to extend our gratitude to the Associate Editor for generous comments and support during this process. We would also like to express our appreciation to Statistics New Zealand for their helpful responses to our general and specific inquiries. Funding from the Royal Society of New Zealand's Marsden Fund (UOA0416) is gratefully

References (45)

  • I. Benenson et al.

    The third state of the Schelling model of residential dynamics

    ArXiv

    (2009)
  • I. Benenson et al.

    Entity-based modeling of urban residential dynamics: The case of Yaffo, Tel Aviv

    Environment and Planning. B, Planning & Design

    (2002)
  • M. Birkin

    Hybrid geographical models of urban spatial structure and behaviour

  • D.G. Brown et al.

    Path dependence and the validation of agent-based spatial models of land use

    International Journal of Geographical Information Science

    (2005)
  • E.E. Bruch

    How population structure shapes neighborhood segregation

    American Journal of Sociology

    (2014)
  • E.E. Bruch et al.

    Neighborhood choice and neighborhood change

    American Journal of Sociology

    (2006)
  • W.A.V. Clark

    Residential preferences and neighborhood racial segregation: A test of the Schelling segregation model

    Demography

    (1991)
  • A. Crooks et al.

    Introduction to agent-based modelling

  • A.T. Crooks

    Constructing and implementing an agent-based model of residential segregation through vector GIS

    International Journal of Geographical Information Science

    (2010)
  • M. Fossett

    Ethnic preference, social distance dynamics, and residential segregation: Theoretical explorations using simulation analysis

    Journal of Mathematical Sociology

    (2006)
  • M. Fossett

    Generative models of segregation: Investigating model-generated patterns of residential segregation by ethnicity and socioeconomic status

    The Journal of Mathematical Sociology

    (2011)
  • M. Fossett et al.

    Overlooked implications of ethnic preferences for residential segregation in agent-based models

    Urban Studies

    (2005)
  • Cited by (67)

    • Ex ante inequality of opportunity in health, decomposition and distributional analysis of biomarkers

      2020, Journal of Health Economics
      Citation Excerpt :

      The intuition behind the Pigou-Dalton transfer principle is not based on income as such but, more broadly, on individuals’ and society’s welfare, which is determined (at least partially) by income. In this utilitarian framework, it has been assumed that utility is an increasing and concave function of income, with diminishing marginal utility of income (Atkinson and Brandolini, 2015; Schwartz and Winship, 1980). For example, taking a dollar away from a richer person and giving it to a poorer person, we decrease the first person's welfare by less than we increase the poorer person's welfare and, thus, we achieve an increase in the total welfare (as the Pigou-Dalton principle assumes).

    • Economic growth, inequality, and well-being

      2016, Ecological Economics
      Citation Excerpt :

      On the other hand, Neumayer (1999, 2000); Dietz and Neumayer (2006), and Jackson and Stymne (2000) see numerous problems with the standard ISEW/GPI method of accounting for inequality. First, Jackson and Stymne (2000) criticize the Gini adjustment because it does not satisfy the principle of diminishing transfers, which requires that the effect of a transfer lessens as the absolute level of income grows (Schwartz and Winship, 1980). Furthermore, Jackson and Stymne (2000) argue that the Gini coefficient is premised on hidden value judgments — implicitly valuing distributions closer to the center.

    • Measuring segregation in social networks

      2014, Social Networks
      Citation Excerpt :

      The axiomatic method has been fruitfully applied in the social sciences. Examples include such diverse domains as utility measurement (Suppes and Winet, 1955), measurement of inequality (Schwartz and Winship, 1980; Cowell and Kuga, 1981; Chakravarty, 1999), income mobility (Cowell, 1985), numerous problems in social choice theory such as the axiomatization of the simple majority rule (May, 1952) or various implications of the assumptions about measurability and comparability of individual utility functions (for example, d’Aspremont and Gevers, 1977, 1985). Regarding segregation, much of the progress in the social stratification research on segregation has been made through the employment of an axiomatic approach (or its associated elements) in the work of James and Tauber (1985), in the later work by Reardon and Firebaugh (2002a) and others (e.g., Egan et al., 1998; Massey and Denton, 1998; Grannis, 2002; Reardon and Firebaugh, 2002b), and recently in work by Alonso-Villar and del Río (2010).

    • AN EMPIRICAL AGENT-BASED MODEL FOR RESIDENTIAL SEGREGATION, CASE STUDY: TEHRAN

      2023, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
    View all citing articles on Scopus
    View full text