Emily Talen

At a moment when housing shortages dominate policy discussions, missing middle housing has emerged as a central topic in U.S. policy debates. Cities and towns across the United States are trying to...

Historic preservation and equity goals often conflict in urban neighborhoods, creating policy dilemmas where heritage conservation may either support or undermine community stability and affordable housing. This article addresses three questions: How do preservation-equity dynamics vary across different urban contexts? What neighborhood types emerge when areas are clustered by preservation and market conditions? What context-specific strategies might promote both heritage conservation and social equity? Using Chicago as a case study, we apply k-medoids clustering analysis to eight variables measuring potentially significant historic resources, preservation designations, affordability, income, racial composition, demolition rates, and displacement vulnerability across census tracts. The analysis reveals six distinct neighborhood types, each with different preservation challenges and opportunities. Key findings expose systematic inequities in heritage recognition: affluent neighborhoods demonstrate “curated heritage” where preservation operates selectively alongside luxury redevelopment, while neighborhoods with the oldest housing stock—predominantly Hispanic and Black communities—achieve minimal preservation recognition despite rich architectural resources. Preservation rates correlate strongly with income and racial composition rather than building age or architectural significance, indicating that current preservation frameworks systematically devalue certain communities’ cultural heritage. Effective preservation-equity policy requires context-specific strategies rather than universal approaches: recognition and community engagement in under-served neighborhoods with significant historic resources; targeted subsidies and anti-displacement measures in gentrifying areas; and inclusive density policies in already-preserved affluent neighborhoods.

The National Register of Historic Places (NR) is a structured artifact of meaning-making that encodes disciplinary values linking architectural and cultural significance to wealth and stylistic distinction. In doing so, it systematically underrepresents vernacular, working-class, and the built environments of racially and ethnically marginalized communities. This paper uses artificial intelligence (AI) to examine how that meaning is constructed. We analyze the preservation record across three scales: a national dataset of 100,117 NR listings (1966–2025), a state-level profile of Illinois’s 1997 NR listings, and a close analysis of Lake Forest, Illinois, a community whose exceptional concentration of NR-listed estate architecture makes it an ideal site for examining how preservation significance has been defined and what it excludes. Two parallel AI methods are applied to eighteen Lake Forest nomination documents and their associated photographs. Natural Language Processing (NLP) analyzes nomination text to trace how preservation professionals connect buildings to cultural value; blind AI image analysis examines the same properties to assess how a model trained on cultural imagery constructs visual meaning independently. NLP analysis reveals a corpus dominated by architectural description, with social history, landscape, and labor systematically underrepresented. The visual analysis confirms and amplifies the nomination record’s class-based assumptions while reproducing the same omissions regarding labor, diversity, and community context. These findings inform debates about AI’s potential to audit existing listings and support nominations for underrepresented property types, while showing that without deliberate corrective design and policy reform, such tools are as likely to replicate the preservation system’s inequities as to repair them.

Urban transit networks are crucial for sustainable cities, yet their uneven distribution often results in unequal access to urban resources. Prior studies primarily focused on accessibility within transit networks while overlooking its implications for residents’ behaviors and the historical and political roots of inequality. Using door-to-door transit information and over 66 million mobility records, we examine racial disparities in New York City’s transit accessibility and their impact on mobility behaviors. We find that ethnic minorities have 4.9% lower neighborhood accessibility, 15.7% less job accessibility, and 14.9% poorer access to essential facilities compared to the white population. These disparities exacerbate residential segregation by 26.9% and confine minorities to limited mobility, higher unemployment risks, and longer travel to essential services. Importantly, inequalities persist even after accounting for socioeconomic covariates, housing values, and residential sorting, pointing to the enduring influence of wealth, political power, and discriminatory planning legacies. Simulations indicate that enhancing transit infrastructure in minority block groups could reduce these gaps by up to 49.8%. Our findings highlight the critical need to integrate equity, affordability, and inclusiveness into the design of future sustainable transit systems.

This paper describes a straightforward method for calculating an open-source Walkable Accessibility Score (WAS) that measures walkability at the block group scale based on walking distance to business establishments, schools, and parks. Exploratory analysis of the WAS reveals high concentrations of walkable accessibility in the centres of the densest and/or largest cities. Our optimised specification ( decay = 0.008, upper = 800, k = 30) performs very well, achieving a Spearman rank correlation of 0.912 with proprietary Walk Score® values (for 2011). We provided pre-calculated data for each year from 1997 to 2019 and Python code for calculating the WAS at the project’s GitHub repository . The method is particularly useful in that it uses simple Euclidean distance calculations, and thus can be run at scale on a laptop or personal computer.