Sociology, University of Toronto, Toronto, Canada.
School of Cities, University of Toronto, Toronto, Canada.
PLoS One. 2021 Jan 15;16(1):e0245357. doi: 10.1371/journal.pone.0245357. eCollection 2021.
This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada. First, we classify Toronto neighbourhoods into distinct groups using established geodemographic segmentation techniques, a relatively novel application in this geographic setting. Extending previous studies, we pursue a hierarchical approach to classifying neighbourhoods that situates many neighbourhood types within the city's broader structure. Our hierarchical approach is able to incorporate a richer set of types than most past research and allows us to study how neighbourhoods' positions within this hierarchy shape their trajectories of change. Second, we use Markov models to identify generative processes that produce patterns of change in the city's distribution of neighbourhood types. Moreover, we add a spatial component to the Markov process to uncover the extent to which change in one type of neighbourhood depends on the character of nearby neighbourhoods. In contrast to the few studies that have explored Markov models in this research tradition, we validate the model's predictive power. Third, we demonstrate how to use such models in theoretical scenarios considering the impact on the city's predicted evolutionary trajectory when existing probabilities of neighbourhood transitions or distributions of neighbourhood types would hypothetically change. Markov models of transition patterns prove to be highly accurate in predicting the final distribution of neighbourhood types. Counterfactual scenarios empirically demonstrate urban complexity: small initial changes reverberate throughout the system, and unfold differently depending on their initial geographic distribution. These scenarios show the value of complexity as a framework for interpreting data and guiding scenario-based planning exercises.
本文旨在通过开发和探索加拿大多伦多的社会空间邻里演化马尔可夫模型，推进邻里变化研究和城市复杂性理论。首先，我们使用既定的地理人口细分技术将多伦多邻里划分为不同的群体，这在这种地理环境下是一种相对新颖的应用。在扩展先前研究的基础上，我们采用分层方法对邻里进行分类，使许多邻里类型处于城市更广泛的结构内。我们的分层方法能够纳入比大多数以往研究更丰富的类型，并使我们能够研究邻里在这种层次结构中的位置如何塑造它们的变化轨迹。其次，我们使用马尔可夫模型来识别产生城市邻里类型分布变化模式的生成过程。此外，我们在马尔可夫过程中添加了空间成分，以揭示一个邻里类型的变化在多大程度上取决于附近邻里的特征。与少数在这一研究传统中探索马尔可夫模型的研究不同，我们验证了该模型的预测能力。第三，我们展示了如何在理论情景中使用这些模型，考虑到当现有邻里转移概率或邻里类型分布假设发生变化时，对城市预测进化轨迹的影响。过渡模式的马尔可夫模型在预测邻里类型的最终分布方面证明是高度准确的。反事实情景经验证明了城市的复杂性：初始的小变化会在整个系统中产生共鸣，并根据其初始地理分布以不同的方式展开。这些情景展示了复杂性作为解释数据和指导基于情景的规划练习的框架的价值。
