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Directorate for Social, Behavioral and Economic SciencesNSF · NSFNSF

CAREER: Understanding the Relationship between the Social and Built Environment of Cities

Elizabeth Roberto·William Marsh Rice University, TX·2025–2030·ACTIVE
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INSTITUTION

William Marsh Rice University, TX

PRINCIPAL INVESTIGATOR

Elizabeth Roberto

FUNDING

$500K

YEAR

2025

MOONBASE SCORE

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Abstract

This project examines the complex relationship between the social and built environment in the United States. In particular, it considers how disconnected roads and physical barriers can vary by residential location. The role of the built environment in facilitating social and spatial variation has received little attention. This project provides new insight into how historical and contemporary views of the spatial landscape can become embedded in the urban infrastructure and contribute to variation in how places may differ. In addition to advancing scientific understanding of these processes and developing innovative methods to analyze them, the project also emphasizes education and outreach by mentoring students in computational social science and creating an interactive web tool to share findings with the public, policymakers, and community stakeholders. This research addresses four key questions. First, how is road network connectivity related to spatial variation and proximity to local public services? Second, what types of built environment features facilitate social and spatial variation, and what are their origins and evolution? Third, how have these features influenced home valuation? And fourth, how do valuation practices persist in shaping property appraisals? This project analyzes large-scale datasets, including demographic and geographic data, road networks, historical maps and records, housing market data, and satellite and street-view images. It utilizes a multidisciplinary approach that integrates scholarship from the social and computational sciences. Methods include spatial and network analysis to measure road connectivity, artificial intelligence (AI) tools for image classification, and machine learning to analyze texts. The project will produce novel computational methods and a public-facing interactive web application to engage diverse audiences in the findings and tools developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Directorate for Social, Behavioral and Economic SciencesCAREER-Faculty Erly Career DevSociologythroughtypesunderstandingeducationincludeintelligenceprocessesfacingreceivedworthyreflectsresidentialfourthfacilitatingoutreachscholarshipcomplexquestionsvariationviews

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