Classification and Regression via Integer Optimization for Neighborhood Change

Olson, Alexander W.; Ke-xin, Zhang; Calderon‐Figueroa, Fernando; Yakubov, Ronen; Sanner, Scott; Silver, Daniel; Arribas‐Bel, Daniel

doi:10.1111/gean.12252

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…The use of more traditional government statistics has also been blended with more sophisticated methods such as random forests to better predict gentrification (Reades, De Souza, & Hubbard, 2019). Olson, Zhang, et al (2021) build upon the clustering techniques discussed in the previous section to develop a predictive neighborhood clustering method using classification and regression along with integer optimization in a way that groups neighborhoods according to their predictive characteristics. Arguably, a case could be made that improved prediction via data-driven and machine learning methods does not provide new insights into underlying processes as they simply learn and replicate from the data they are fed.…”

Section: Looking Forward: Under S Tand Ing Chang E S In Ne Ar Re Al T...mentioning

confidence: 99%

GIScience and neighborhood change: Toward an understanding of processes of change

Delmelle

2021

Transactions in GIS

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Processes of neighborhood change are the result of the unfolding of events and decisions by multiple actors operating at varying spatial and temporal scales, enabled and constrained upon an unequal urban landscape. The contributions of GIScience toward understanding these processes have evolved from the simple mapping of static, cross‐sectional maps toward an embrace of novel data and methods that enable longitudinal trajectories to be extracted and neighborhood futures to be predicted. In this article, I review these advancements and chart a course forward that considers a future research agenda that is critically cognizant of the potentials and perils of new data sources and method, is representative of the full spectrum of processes operating both visibly and invisibly that give rise to observed neighborhood outcomes, and considers their varying spatial and temporal scales.

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Section: Looking Forward: Under S Tand Ing Chang E S In Ne Ar Re Al T...mentioning

confidence: 99%

GIScience and neighborhood change: Toward an understanding of processes of change

Delmelle

2021

Transactions in GIS

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“…Current research tends to focus on the nature, patterns, and causes of change itself [ 36 ]. Most use some form of cluster analysis, but there are several ongoing debates [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

A Markov model of urban evolution: Neighbourhood change as a complex process

Silver

Silva

2021

PLoS ONE

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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.

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“…Current research tends to focus on the nature, patterns, and causes of change itself [36]. Most use some form of cluster analysis, but there are several ongoing debates [37].…”

Section: Socio-spatial Neighbourhood Change Researchmentioning

confidence: 99%

A Markov model of urban evolution: Neighbourhood change as a complex process

Silver

Silva

2020

Preprint

Self Cite

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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 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.

show abstract

Classification and Regression via Integer Optimization for Neighborhood Change

Cited by 10 publications

References 29 publications

GIScience and neighborhood change: Toward an understanding of processes of change

GIScience and neighborhood change: Toward an understanding of processes of change

A Markov model of urban evolution: Neighbourhood change as a complex process

A Markov model of urban evolution: Neighbourhood change as a complex process

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