Social Construction of Urban Space: Understanding Neighborhood Boundaries Using Rental Listings

2025-05-31

AI Summary

This study identifies a systematic misalignment between institutionally defined neighborhood boundaries in Chicago and residents’ spatial cognition. Using Craigslist rental listings (2018–2024) integrated with geospatial data, the research combines manual annotation, large language model–assisted classification, LDA topic modeling, natural language processing, and GIS-based spatial analysis. It empirically documents, for the first time, a phenomenon termed “reputational whitewashing”—where landlords strategically manipulate neighborhood labels to enhance perceived desirability—and identifies three distinct boundary conflict patterns: contradictory attribution, contiguous co-assignment, and cross-boundary affiliation. Quantitative analysis confirms that neighborhood labeling is highly subjective and strategic; moreover, distance from an officially designated neighborhood center significantly predicts semantic preferences in listing descriptions (e.g., frequency of terms like “safe” or “convenient”). The findings provide computationally grounded linguistic evidence for urban sociology and advance empirical methodologies for studying the dynamic, discursive construction of spatial legitimacy.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Community question answering
• Social Networks and Social Media: Computational social science

Abstract

Rental listings offer a unique window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we classify unstructured listings from Craigslist according to their neighborhood. Geospatial analysis reveals three distinct patterns: properties with conflicting neighborhood designations due to competing spatial definitions, border properties with valid claims to adjacent neighborhoods, and ``reputation laundering"where listings claim association with distant, desirable neighborhoods. Through topic modeling, we identify patterns that correlate with spatial positioning: listings further from neighborhood centers emphasize different amenities than centrally-located units. Our findings demonstrate that natural language processing techniques can reveal how definitions of urban spaces are contested in ways that traditional methods overlook.

Problem

• Analyzing rental ads to reveal mismatches between official and claimed neighborhood boundaries
• Classifying Craigslist listings to uncover competing spatial definitions and reputation laundering
• Using NLP to identify contested urban space definitions overlooked by traditional methods

Innovation

• Analyzing Craigslist ads with NLP
• Geospatial analysis of rental listings
• Topic modeling for urban space patterns