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

• URL: http://arxiv.org/abs/2506.00634v1
• Date: Sat, 31 May 2025 16:42:46 GMT
• Title: Social Construction of Urban Space: Understanding Neighborhood Boundaries Using Rental Listings
• Authors: Adam Visokay, Ruth Bagley, Ian Kennedy, Chris Hess, Kyle Crowder, Rob Voigt, Denis Peskoff,
• Abstract summary: We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods.<n>Our findings demonstrate how natural language processing techniques can reveal how definitions of urban spaces are contested in ways that traditional methods overlook.
• Score: 4.708637373177044
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Schelling Games with Continuous Types [3.5232085374661284]
  50 years ago, Schelling proposed a landmark model that explains residential segregation in an elegant agent-based way. We focus on segregation caused by non-categorical attributes, such as household income or position in a political left-right spectrum. We study the existence and computation of equilibria and provide bounds on the Price of Anarchy and Stability.
  arXiv  Detail & Related papers  (2023-05-11T14:13:14Z)
• The Association Between SOC and Land Prices Considering Spatial Heterogeneity Based on Finite Mixture Modeling [1.933681537640272]
  Even within a district, there are multiple sections used for different purposes. Land prices can be managed by adopting the spatial clustering method. Policymakers and managerial administration need to look for information to make policy about land prices.
  arXiv  Detail & Related papers  (2022-11-15T23:18:06Z)
• Latent Topology Induction for Understanding Contextualized Representations [84.7918739062235]
  We study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations.
  arXiv  Detail & Related papers  (2022-06-03T11:22:48Z)
• Measuring and Modeling Neighborhoods [0.0]
  We develop an open-source survey instrument that allows respondents to draw their neighborhoods on a map. We propose a statistical model to analyze how the characteristics of respondents and local areas determine subjective neighborhoods.
  arXiv  Detail & Related papers  (2021-10-26T20:43:41Z)
• XPROAX-Local explanations for text classification with progressive neighborhood approximation [13.312630052709766]
  We propose a progressive approximation of the neighborhood using counterfactual instances as initial landmarks. We then refine counterfactuals and generate factuals in the neighborhood of the input instance to be explained. Our experiments on real-world datasets demonstrate that our method outperforms the competitors in terms of usefulness and stability.
  arXiv  Detail & Related papers  (2021-09-30T11:01:07Z)
• MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal [57.28018917017665]
  We propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. By acquiring and integrating multi-trivial urban data, we first construct a rich feature set to comprehensively profile real estate from multiple perspectives. An evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronouslytemporal dependencies among real estate transactions.
  arXiv  Detail & Related papers  (2021-07-12T03:51:44Z)
• On spatial variation in the detectability and density of social media user protest supporters [52.77024349608834]
  The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users' spatial detectability and density during a protest in Mexico City.
  arXiv  Detail & Related papers  (2021-03-10T14:08:08Z)
• Localization in the Crowd with Topological Constraints [47.51300472171983]
  We introduce a topological constraint that teaches the model to reason about the spatial arrangement of dots. Topological reasoning improves the quality of the localization algorithm especially near cluttered regions.
  arXiv  Detail & Related papers  (2020-12-23T04:33:48Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Disentangled Non-Local Neural Networks [68.92293183542131]
  We study the non-local block in depth, where we find that its attention can be split into two terms. We present the disentangled non-local block, where the two terms are decoupled to facilitate learning for both terms.
  arXiv  Detail & Related papers  (2020-06-11T17:59:22Z)
• Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding [8.396746290518102]
  Urban2Vec is an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest data. We show that Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks.
  arXiv  Detail & Related papers  (2020-01-29T21:30:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.