Urban research has long recognized that neighbourhoods are dynamic and relational. However, lack of data, methodologies, and computer processing power have hampered a formal quantitative examination of neighbourhood relational dynamics. To make progress on this issue, this study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods, their past characteristics, and flows of groups among them, potentially providing greater expressive power in predictive models. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. Substantively, we find that either local area information (e.g. area demographics) or group profiles (tastes of Yelp reviewers) give the best results in predicting local culture, and they are nearly equivalent in all studied cases. Methodologically, exploring group profiles could be a helpful alternative where finding local information for specific areas is challenging, since they can be extracted automatically from many forms of online data. Thus, our approach could empower researchers and policy-makers to use a range of data sources when other local area information is lacking.

An urban planner might design the spatial layout of transportation amenities so as to improve accessibility for underserved communities -- a fairness objective. However, implementing such a design might trigger processes of neighborhood change that change who benefits from these amenities in the long term. If so, has the planner really achieved their fairness objective? Can algorithmic decision-making anticipate second order effects? In this paper, we take a step in this direction by formulating processes of neighborhood change as instances of no-regret dynamics; a collective learning process in which a set of strategic agents rapidly reach a state of approximate equilibrium. We mathematize concepts of neighborhood change to model the incentive structures impacting individual dwelling-site decision-making. Our model accounts for affordability, access to relevant transit amenities, community ties, and site upkeep. We showcase our model with computational experiments that provide semi-quantitative insights on the spatial economics of neighborhood change, particularly on the influence of residential zoning policy and the placement of transit amenities.

Gentrification--the transformation of a low-income urban area caused by the influx of affluent residents--has many revitalizing benefits. However, it also poses extremely concerning challenges to low-income residents. To help policymakers take targeted and early action in protecting low-income residents, researchers have recently proposed several machine learning models to predict gentrification using socioeconomic and image features. Building upon previous studies, we propose a novel graph-based multimodal deep learning framework to predict gentrification based on urban networks of tracts and essential facilities (e.g., schools, hospitals, and subway stations). We train and test the proposed framework using data from Chicago, New York City, and Los Angeles. The model successfully predicts census-tract level gentrification with 0.9 precision on average. Moreover, the framework discovers a previously unexamined strong relationship between schools and gentrification, which provides a basis for further exploration of social factors affecting gentrification.

Critical toponymy examines the dynamics of power, capital, and resistance through place names and the sites to which they refer. Studies here have traditionally focused on the semantic content of toponyms and the top-down institutional processes that produce them. However, they have generally ignored the ways in which toponyms are used by ordinary people in everyday discourse, as well as the other strategies of geospatial description that accompany and contextualize toponymic reference. Here, we develop computational methods to measure how cultural and economic capital shape the ways in which people refer to places, through a novel annotated dataset of 47,440 New York City Airbnb listings from the 2010s. Building on this dataset, we introduce a new named entity recognition (NER) model able to identify important discourse categories integral to the characterization of place. Our findings point toward new directions for critical toponymy and to a range of previously understudied linguistic signals relevant to research on neighborhood status, housing and tourism markets, and gentrification.

Gentrification is a social process where wealthier households move into a neighborhood. It increases the average household income, encourages the emergence of new businesses, and causes demographic displacement. The reasons behind the troublesome gentrification of Chicago go back to the 1920s. At that time, gentrification was partly encouraged by stimulating private investment and paving the way for urban renewal operations. In the 1950s, many white families moved out of the urban neighborhoods while the Latinos, African Americans, and other minorities moved into the city.

Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semi-spatial and non-spatial feature engineering techniques, and we empirically show that spatially-conscious machine learning models outperform non-spatial models when married with advanced prediction techniques such as feed-forward artificial neural networks and gradient boosting machine models.

Concern about gentrification has grown in the past decade as the affluent and educated have surged back into cities. But can the pace and pattern of future gentrification be predicted? New research by a team of data scientists and geographers says so. The research, conducted by Jonathan Reades, Jordan De Souza, and Phil Hubbard of Kings College London and published in the Urban Studies journal, uses an artificial intelligence technique called machine learning that essentially trains computer models to learn from past data to predict future patterns. In this case, the research team used data on past gentrification in London to predict where it will next occur.

In Donut County, you control a giant, mobile hole in the ground that gets bigger the more it swallows. In Donut County, the main character is a hole. That's what you play as. A hole that swallows stuff and gets bigger so it can swallow bigger stuff. Donut County is not about the hole.

From the tall windows of WIRED's offices in San Francisco's South-of-Market neighborhood I've watched almost a decade of radical change made physical in concrete and glass. The city's forest of new skyscrapers is at least in part the legacy of Mayor Ed Lee, who died early Tuesday morning after almost seven years in office. San Francisco is rolling into the second quarter of the 21st century with the purposeful but cautious stutter-step speed of a first-generation self-driving car--the wealthiest, youngest, smartest people on earth live alongside some of the poorest; utopia and dystopia are barely a few blocks apart. That's the city Ed Lee built. It's a cliché to say upon a politician's death that he or she had a complicated legacy, but here we are. Lee was a housing advocate who presided over a city in a deepening housing crisis, facing massive gentrification, displacement, and homelessness.

Recently, the Urban Institute called for the creation of "neighborhood-level early warning and response systems that can help city leaders and community advocates get ahead of (neighborhood) changes." Open data and open-source analytics allows community stakeholders to mine data for actionable intelligence like never before. The objective of this research is to take a first step in exploring the feasibility of forecasting neighborhood change using longitudinal census data in 29 Legacy Cities (Figure 2). The first section provides some motivation for the analysis. Section 3 provides results and the final section concludes with a discussion of community-oriented neighborhood change forecasting systems. Neighborhoods change because people and capital are mobile and when new neighborhood demand emerges, incumbent residents rightfully worry about displacement.