Understanding urban gentrification through machine learning

Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate ‘uplift’ or ‘decline’ by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification

Promises and Pitfalls of a New Early Warning System for Gentrification in Buffalo, NY

Gentrification and its resultant displacement are one of the many "wicked problems" of social policy. The study of gentrification and displacement spans half a century, concerns a variety of spatial, temporal, and social contexts, and describes socio-political processes of across the globe and throughout history. One current iteration of this field of inquiry are efforts to identify "early indicators" of gentrification and/or displacement, or the creation of "early warning systems" (EWS). The current work adds to scholarship on the utility of developing an EWS by examining the methodological considerations required for such systems to serve a justice-oriented preventative role

Predicting Neighborhood Change in Detroit: A Data and Ethical Analysis of Data-Driven Policymaking

This research develops a technical tool that attempts to predict neighborhood change – as measured by indicators of socioeconomic “wellbeing” – and investigates the ethical challenges inherent in such a process. The technical component utilizes publicly-available data to predict changes in socioeconomic status in Detroit neighborhoods from 2012 to 2017 utilizing machine learning techniques. The research investigates how these data can shed light on Detroit’s socioeconomic changes since its declaration of municipal bankruptcy, if there is any predictive power to this data, and what the ethical ramifications of such quantitative assessments might be. Can data analysis and algorithms predict neighborhood change – gentrification or decline? Should such processes be utilized in the policymaking realm? This paper also presents an argument against the use of such algorithm alone as a decision-making mechanism, especially without first working within the communities that might be most affected by its implementation in policy or investment decision-making.Master of ScienceInformation, School ofUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162559/1/Graff_Alissa_Final_MTOP_Thesis_20200810.pd

Stratifying and predicting patterns of neighbourhood change and gentrification ‐ an urban analytics approach

While recent debates have widely acknowledged gentrification’s varied manifestations, success in enumerating and disentangling the process and its defining features from other forms of neighbourhood change at-scale and across entire cities, has remained largely elusive. This paper addresses this gap and employs a novel, open and reproducible urban analytics approach to systematically examine the past and future trajectories of neighbourhood change using London, England, as a case-study example. Using suites of datasets relating to population, house prices and built environment development, the nature of gentrification’s mutations and its spatial patterns are extracted through a multi-stage data dimensionality reduction and classification methodology. Machine Learning is subsequently adopted to model gentrification’s observed trends and predict its future frontiers with interactive visualisation methods offering new insights into gentrification’s projected dynamics and geographies

Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund

Cities across the United States are undergoing great transformation and urban growth. Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development. One great challenge is to use the tools of data science to promote equity along with growth. The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement. On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values. In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years. To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment. In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments. We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program. These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.Comment: Presented at the Data For Good Exchange 201

High Frequency Gentrification Prediction Using Airbnb Data

We propose a methodology for estimating neighborhood gentrification using high frequency, publicly available Airbnb data. Leveraging 3.8 million text reviews from Jan 2014 to Dec 2019 across 17 US cities, we find guest reviews and rental characteristics to be predictive of gentrification during the same period. Both structured features (e.g. number of listings) and unstructured features (e.g. word frequency in reviews) are found to be important predictors across multiple specifications. Using our trained model, we predict and map current gentrification rates ahead of official statistics. These models are provided freely to enable rapid policy response and further research